SLOPE FAILURE ASSESSMENT in PENANG ISLAND USING GEOELECTRICAL METHODS MUHAMAD IQBAL MUBARAK FAHARUL AZMAN April 2018

SLOPE FAILURE ASSESSMENT IN PENANG ISLAND USING GEOELECTRICAL METHODS

MUHAMAD IQBAL MUBARAK FAHARUL AZMAN

Thesis submitted in fulfilment of the requirements for the degree of Master in Science

April 2018 ACKNOWLEDGEMENT

I thank Allah S.W.T for His mercy for giving me this opportunity to further study in Master’s degree and His guidance for me to face challenges in order to complete this research.

I would like to express my sincere gratitude to my main supervisor, Dr Nur

Azwin Ismail for her continuous support, guidance and encouragement that leads me to completing my research. A special thanks to my co – supervisor, Dr Nordiana Mohd

Muztaza for her invaluable advices and suggestions. Many thanks also to all

Geophysics laboratory assistants for their time and effort in assisting me to conduct my research.

My appreciation also to all my very helpful and supportive postgraduate students for their non – stop encouragement and knowledge sharing that allow me to move forward.

My sincere appreciation toward my beloved parents Mr. Faharul Azman

Ahmad Sabki and Mrs. Faridah Md Saad and also my siblings for their prayers and encouragements. Last but not least, Fellowship Scheme of Universiti Sains Malaysia for sponsoring my tuition fees and allowances during my study period.

TABLE OF CONTENTS

Acknowledgement ii

Table of Contents iii

List of Tables vii

List of Figures x

List of Abbreviations xiv

List of Symbols xvi

Abstrak xviii

Abstract xx

CHAPTER 1: INTRODUCTION

1.0 Preface 1

1.1 Problem statements 3

1.2 Research objectives 5

1.3 Scope of research 5

1.4 Significant of study 6

1.5 Thesis outlines 7

CHAPTER 2: LITERATURE REVIEW

2.0 Introduction 8

2.1 Previous studies 8

2.2 Basic theory of 2D resistivity method 25

2.3 Basic theory of induced polarization 28

2.4 Depth of penetration 30

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2.5 Porosity 30

2.6 Moisture content 31

2.7 Bulk density 31

2.8 Summary 32

CHAPTER 3: MATERIALS AND METHODS

3.0 Introduction 33

3.1 Geology and geomorphology of Penang Island 34

3.2 Study area 37

3.2.1 Sungai Batu 42

3.2.2 Bukit Relau 43

3.2.3 Air Hitam 44

3.3 Laboratory test 45

3.3.1 Moisture content measurement 46

3.3.2 Porosity calculation 46

3.3.3 Particle size distribution (PSD) analysis 47

3.3.3(a) Experiment procedure 48

3.3.3(b) Coefficient of gradation, Cc and coefficient 48 of uniformity, Cu

3.3.3(c) Particle size statistics of mean and sorting 50

3.4 Geophysical methods 51

3.4.1 2D resistivity method 52

3.4.2 Induced polarization (IP) method 54

3.4.3 Electrical properties of materials 54

3.4.4 Data acquisition 55

3.4.5 Data processing 57

3.5 Monitoring the study area 58

3.6 Rainfall distribution 59

3.7 Regression analysis 60

3.8 Summary 61

CHAPTER 4: RESULTS AND DISCUSSION

4.0 Introduction 63

4.1 Results 63

4.1.1 Sungai Batu 64

4.1.1(a) Laboratory test 64

4.1.1(b) Particle size distribution (PSD) analysis 65

4.1.1(c) Rainfall distribution 67

4.1.1(d) Geophysical methods 69

4.1.1(e) Slope monitoring 71

4.1.1(f) Changes within Sungai Batu study area 75 subsurface

4.1.2 Bukit Relau 81

4.1.2(a) Laboratory test 81

4.1.2(b) Particle size distribution (PSD) analysis 82

4.1.2(c) Rainfall distribution 84

4.1.2(d) Geophysical methods 86

4.1.2(e) Slope monitoring 88

4.1.2(f) Changes within Bukit Relau study area 91 subsurface 4.1.3 Air Hitam 93

4.1.3(a) Laboratory test 93

4.1.3(b) Geophysical methods 94

4.2 Empirical correlation between soil moisture content and porosity 96

4.3 Empirical correlation between geophysical data and laboratory 97 tests

4.4 Empirical correlation between geophysical data and rainfall 99 distribution

4.5 Summary 101

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS

5.0 Conclusion 105

5.1 Recommendations for future work 109

REFERENCES 110

APPENDICES

LIST OF TABLES

Page Table 1.1 Series of major slope failure occurrences in Malaysia and 1 consequences in terms of deaths from 2002 – 2017.

Table 1.2 Summary of slope failures in Penang Island. 4

Table 2.1 Summary of resistivity value with interpretation at the study 17 areas in Selangor (Muztaza et al., 2017).

Table 2.2 Resistivity range of weathered granite and fresh granite in 18 Malaysia (Yaccup and Tawnie, 2015).

Table 2.3 Saturated peak shear box and particle size distribution tests 24 (Hashim et al., 2015).

Table 2.4 Result of soil classification and particle size distribution test 24 (Hashim et al., 2015).

Table 2.5 Porosity of soil according to the percentage of porosity (Faur 31 and Szabo, 2011).

Table 2.6 Relationship of soil bulk density for root growth based on 31 soil texture (Arshad et al., 1996).

Table 3.1 Description of weathering grade (Attewell, 1993). 41

Table 3.2 Name of equipment as labelled in Figure 3.11. 45

Table 3.3 Name of equipment as labelled in Figure 3.12. 48

Table 3.4 Summary of soil classification (Santamarina et al., 2001). 49

Table 3.5 Conversion from millimetre to phi unit (Folk and Ward, 50 1957).

Table 3.6 Values of sorting and the interpretation (Folk and Ward, 51 1957).

Table 3.7 Resistivity of various rock and sediment (Telford, 1990). 55

Table 3.8 Chargeability of various material (Telford, 1990). 52

Table 3.9 Name of equipment as labelled in Figure 3.16. 57

Table 3.10 Categories of precipitation and the description (Department 59 of Meteorological Malaysia, 2018).

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Table 3.11 Strength of correlation (Evan, 1996). 60

Table 4.1 Laboratory test for Sungai Batu study area. 64

Table 4.2 Particle size distribution analysis result at Sungai Batu study 65 area.

Table 4.3 Mean and standard deviation of Sungai Batu soil samples. 66

Table 4.4 Summary of resistivity value with interpretation and feature 69 found at Sungai Batu study area.

Table 4.5 Summary of chargeability value with interpretation at Sungai 70 Batu study area.

Table 4.6 Laboratory test for Bukit Relau study area. 82

Table 4.7 Particle size distribution analysis result at Bukit Relau study 83 area.

Table 4.8 Mean and standard deviation of Bukit Relau soil samples. 83

Table 4.9 Summary of resistivity value with interpretation and features 86 found at Bukit Relau study area.

Table 4.10 Summary of chargeability value with interpretation and 87 features found at Bukit Relau study area.

Table 4.11 Laboratory test for Air Hitam study area. 94

Table 4.12 Summary of resistivity value with interpretation and features 94 found at Air Hitam study area.

Table 4.13 Summary of chargeability value with interpretation and 95 features found at Air Hitam study area.

Table 4.14 Categories of resistivity value obtained in this research. 101

Table 4.15 Categories of chargeability value obtained in this research. 101

Table 4.16 Summary of result obtained from Sungai Batu study area. 102

Table 4.17 Summary of result obtained from Bukit Relau study area. 103

Table 4.18 Summary of result obtained from Air Hitam study area. 103

Table 4.19 Summary of empirical correlation between geophysical data, 104 laboratory tests and rainfall distribution.

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Table 5.1 Summary of slope failure potentials and the factor for failure 108 at the study areas.

LIST OF FIGURES

Figure 2.2 Resistivity profiles for ERT1 – ERT3 (Giocoli et al., 2015). 13

Figure 2.3 3D fence diagram for resistivity section in Aydin (Drahor et 14 al., 2006).

Figure 2.4 Resistivity profiles according to the location (a) top of 18 landslide (b) toe of landslide (Hazreek et al., 2017).

Figure 2.5 a) Average rainfall at Cameron Highland from March 2008 20 to Dec 2008 b) Observed total displacement at Prisms I-VI (Khan et al., 2010).

Figure 2.6 Safety factor caused by combination of rainfall and the 22 water level fluctuation (Liu and Li, 2015).

Figure 2.7 The flow of current from a point current source (current 28 electrode) and the resulting potential distribution within the subsurface (Loke, 2001).

Figure 2.8 Equilibrium ion distribution (left). Polarization in the 29 electric field direction (right) (Slater and Lesmes, 2002).

Figure 2.9 Principle of time domain IP signal (a) current on – off (b) 30 the measured voltage decay (modified from Slater and Lesmes, 2002).

Figure 3.1 Location of study areas (Google Earth, 2018). 35

Figure 3.2 General geology of Penang Island (Ong, 1993). 36

Figure 3.3 Lineament map of Penang Island (Ong, 1993). 37

Figure 3.4 Location of study areas on topography map. 38

Figure 3.5 Slope in degree for cell 10 units of Penang Island with 39 study area locations (Azmi, 2014).

Figure 3.6 Rainfall distribution from 2014 to 2016 (Department of 40 Meteorological Malaysia, 2018).

Figure 3.7 Number of days with daily amount of rainfall more or equal 41 to 20 mm/day in 2014 – 2016.

Figure 3.8 Aerial view of Sungai Batu survey line (Google Earth, 43 2018).

Figure 3.9 Aerial view of Bukit Relau survey line (Google Earth, 44 2018).

Figure 3.10 Aerial view of Air Hitam survey line (Google Earth, 2018). 44

Figure 3.11 Soil laboratory test equipment. 45

Figure 3.12 PSD analysis equipment. 47

Figure 3.13 The position of current electrodes (C1 and C2) and 52 potential electrodes (P1 and P2) (Loke, 2001).

Figure 3.14 Electrode configurations of 2D resistivity method (Loke, 53 2001).

Figure 3.15 Wenner-Schlumberger electrode arrangement. The ‘n’ 53 factor is the dipole separation factor (Loke, 2001).

Figure 3.16 2D resistivity and IP methods equipment. 56

Figure 3.17 2D resistivity method setup on field. 57

Figure 4.1 Rainfall distribution at Sungai Batu. (a) Seven days 68 cumulative rainfall (b) Rainfall distribution on 6th day and 7th day.

Figure 4.2 Inversion model of (a) 2D resistivity and (b) induced 71 polarization method at Sungai Batu study area.

Figure 4.3 2D resistivity inversion model for August 2016 at Sungai 72 Batu.

Figure 4.4 2D resistivity inversion model for September 2016 at 72 Sungai Batu.

Figure 4.5 2D resistivity inversion model for November 2016 at 73 Sungai Batu.

Figure 4.6 2D resistivity inversion model for December 2016 at 74 Sungai Batu.

Figure 4.7 2D resistivity inversion model for January 2017 at Sungai 74 Batu.

Figure 4.8 Sungai Batu weathered granite zone changes throughout the 76 monitoring period.

Figure 4.9 Monitoring resistivity values at distance 30 – 80 m at 77 Sungai Batu study area.

Figure 4.10 Sungai Batu weak zone changes during the monitoring 78 period.

Figure 4.11 Monitoring resistivity values at distance 90 – 120 m at 79 Sungai Batu study area.

Figure 4.12 Sungai Batu granitic bedrock changes throughout the 80 monitoring period.

Figure 4.13 Monitoring resistivity values at distance 110 – 150 m at 81 Sungai Batu study area.

Figure 4.14 Rainfall distribution at Bukit Relau. (a) Seven days 85 cumulative rainfall (b) Rainfall distribution on 6th day and 7th day.

Figure 4.15 Inversion model of (a) 2D resistivity and (b) induced 88 polarization method at Bukit Relau study area.

Figure 4.16 2D resistivity inversion model for September 2016 at Bukit 89 Relau.

Figure 4.17 2D resistivity inversion model for November 2016 at Bukit 89 Relau.

Figure 4.18 2D resistivity inversion model for December 2016 at Bukit 90 Relau.

Figure 4.19 2D resistivity inversion model for January 2017 at Bukit 91 Relau.

Figure 4.20 Bukit Relau fracture changes throughout the monitoring 92 period.

Figure 4.21 Monitoring resistivity values of distance 50 – 80 m at Bukit 93 Relau study area.

Figure 4.22 Inversion model of (a) 2D resistivity and (b) induced 96 polarization method at Air Hitam study area.

Figure 4.23 Empirical correlation between porosity and moisture 97 content at Sg Batu and Bukit Relau study area.

Figure 4.24 Empirical correlation between resistivity and soil 98 laboratory tests.

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Figure 4.25 Empirical correlation between chargeability and soil 99 laboratory tests.

Figure 4.26 Empirical correlation of resistivity and cumulative rainfall 100 of seven days including the survey day.

Figure 4.27 Empirical correlation of chargeability and cumulative 100 rainfall of seven days including the survey day.

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LIST OF ABBREVIATIONS

2D Two dimension

3D Three dimension

4D Four dimension

ALERT Automated time – lapse electrical resistivity

tomography

ASTM American Society for Testing and Materials

BS British Standard

C1/C2 Current electrode

ERT Electrical resistivity tomography

FOS Factor of safety

GIS Geographic information system

IP Induced polarization

NPP North Penang Pluton

NW Northwest

P – wave Primary wave

P1/P2 Potential electrode

PSD Particle size distribution

RES2DINV Resistivity two-dimension inversion

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RMS Root mean square

S – wave Secondary wave

SE Southeast

SK Sekolah Kebangsaan

SMK Sekolah Menengah Kebangsaan

SP Self – potential

SPP South Penang Pluton

SW Southwest

TLERT Time lapse electrical resistivity tomography

USCS Unified Soil Classification System

LIST OF SYMBOLS

Vm measured voltage

Vr residual voltage

ρa apparent resistivity

∆t length of the time window of integration.

∇ gradient operator

△V Potential difference

Cc Coefficient of curvature

Cu Coefficient of uniformity d diameter

D Effective size

E electric field intensity e void ratio

I current k geometric factor

M chargeability

ƞ Porosity r distance of point in the medium

R Coefficient of correlation

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R resistance

R2 Coefficient of determination

V electric potential w Moisture content

ρ resistivity

ρbulk Bulk density

σ conductivity

ϕ phi unit

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PENILAIAN KEGAGALAN CERUN DI PULAU PINANG MENGGUNAKAN

KAEDAH GEOELEKTRIK

ABSTRAK

Kegagalan cerun di kawasan berbukit dan kawasan cerun tidak berkurang setiap tahun. Hal ini boleh membahayakan penduduk setempat. Walau bagaimanapun, cerun tidak hanya terhad di kawasan penempatan tetapi juga boleh dilihat di sepanjang lebuhraya dan jalan persekutuan. Kajian ini bertujuan untuk menyiasat cerun di kawasan berisiko tinggi di Pulau Pinang (Sungai Batu, Bukit Relau dan Air Hitam) menggunakan kaedah keberintangan 2D dan polarisasi teraruh serta menganalisa dan mengaitkan keputusan dari keadah keberintangan 2D, kaedah polarisasi teraruh, ujian makmal dan taburan hujan untuk potensi kegagalan cerun. Kawasan – kawasan kajian dipilih berdasarkan kepada kajian terdahulu dan kes – kes kegagalan cerun yang dilaporkan berlaku di Pulau Pinang. Berdasarkan kepada keputusan, kebarangkalian untuk kegagalan cerun di Sungai Batu adalah disebabkan oleh kewujudan zon lemah dengan nilai keberintangan rendah 0 – 400 Ωm and nilai kebolehcasan rendah < 3 ms manakala Bukit Relau pula dijangka mengalami kegagalan cerun disebabkan oleh zon luluhawa dengan nilai keberintangan pertengahan 750 – 3000 Ωm dan nilai kebolehcasan pertengahan 3 – 10 ms. Kewujudan rekahan juga menyumbang kepada kegagalan cerun terjadi. Sungai Batu dan Bukit Relau terletak berhampiran dengan sesar, sekaligus meningkatkan potensi kegagalan cerun disebabkan oleh ciri – ciri batuan yang kekar dan ricih. Air Hitam dikenalpasti terdedah kepada kegagalan cerun disebabkan oleh kewujudan zon lempung tepu berdasarkan nilai keberintangan yang rendah (< 400 Ωm) tetapi nilai kebolehcasan yang tinggi (> 25 ms). Ujian makmal tanah juga dilakukan untuk mengenalpasti kandungan air dan keliangan. Pengamatan

xviii kolerasi antara nilai keberintangan dan nilai kebolehcasan diperoleh dari kaedah geofizik dengan kandungan air dan keliangan dari ujian makmal tanah adalah berkadar songsang antara satu sama lain dengan kekuatan kolerasi kuat sehingga sangat kuat.

Analisis taburan saiz zarah berjaya mengenalpasti tanah di Sungai Batu dan Bukit

Relau adalah pasir berkelikir. Cerun – cerun dipantau selama beberapa bulan untuk memerhati sebarang perubahan sub permukaan. Berdasarkan keputusan di Sungai

Batu, zon lemah dengan nilai keberintangan < 400 Ωm ditemui di tengah garis tinjauan manakala zon luluhawa dengan nilai keberintangan 500 – 3000 Ωm dikenalpasti sepanjang garis tinjauan. Kaedah keberintangan 2D mendapati nilai yang menurun, ini menunjukkan zon – zon yang semakin longgar. Rekahan juga dikesan terbentuk semasa tempoh pantauan di jarak 130 – 140 m. Di Bukit Relau, fenomena yang sama dapat diperhati dimana nilai keberintangan menurun dengan masa. Rekahan yang dikesan di jarak 60 m bertambah lebar berbanding di awal tempoh pantauan dan sebuah lagi rekahan dikesan terbentuk di garis tinjauan. Pemantauan cerun membuktikan sub permukaan mengalami perubahan dan ia dipengaruhi oleh pelbagai faktor seperti hujan di sepanjang bulan. Kajian ini berjaya mengenalpasti potensi kegagalan cerun di kawasan – kawasan kajian iaitu zon lemah dan kehadiran bahan lempung di Sungai Batu, zon luluhawa granit di Bukit Relau dan zon lempung tepu di

Air Hitam. Faktor yang membawa kepada kebarangkalian kegagalan cerun di kawasan

– kawasan kajian ialah keadaan tanah yang telap, taburan hujan yang tinggi dan juga kesan luluhawa.

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SLOPE FAILURE ASSESSMENT IN PENANG ISLAND USING

GEOELECTRICAL METHODS

ABSTRACT

Slope failures never cease to end in hilly and slope area every year. This is very dangerous especially for those who lives within the proximity. However, slope is not only found in residential area but also alongside highways and federal roads. This research aims to investigate slope at the selected highly suspected area in Penang

Island (Sungai Batu, Bukit Relau and Air Hitam) using 2D resistivity and induced polarization methods and to analyse and correlate the results from 2D resistivity method, induced polarization method, laboratory tests and rainfall distribution for slope failure potentials. The study areas were selected based on the previous studies and cases of slope failure reported in Penang Island. Based on the results, Sungai Batu has probability for slope failure due to weak zone with low resistivity value of 0 – 400

Ωm and low chargeability of < 3 ms while Bukit Relau was expected to have slope failure due to the weathering zone with intermediate resistivity 750 – 3000 Ωm and intermediate chargeability 3 – 10 ms. The existence of fractures also contributed to trigger the slope failures. Sungai Batu and Bukit Relau are located near faults and thus, intensified the slope failure potential due to highly jointed and sheared rocks characteristic. Air Hitam was determined to be vulnerable to slope failure due to the presence of clay saturated zone indicated by low resistivity value (< 400 Ωm) but high chargeability value (>25 ms). Soil laboratory tests were also conducted to determine the moisture content and porosity. The empirical correlation of resistivity and chargeability values obtained from geophysical methods with moisture content and porosity from soil laboratory tests indicated that they are inversely proportional to each

xx other with strength of correlation range from strong to very strong. PSD analysis successfully determined that the soil at Sungai Batu and Bukit Relau are gravelly sand.

The slopes were also monitored for several months to observe any changes within the subsurface. Based on the results at Sungai Batu, weak zone with resistivity of < 400

Ωm was found at the centre of survey line while weathered zone with resistivity 500 –

3000 Ωm was identified along the survey line. 2D resistivity method recorded decreases in value therefore indicated that the zones were becoming loose. A fracture was also detected formed during the monitoring period at distance 130 – 140 m. As for Bukit Relau, the same phenomenon was observed where the resistivity value decreased with time. Fracture found at distance 60 m is wider since the beginning of monitoring period and another fracture was observed to develop at distance 140 m along the survey line. Monitoring the slope proved that the subsurface experience changes and can be affected by various factors such as rainfall over the months. This research has successfully determined slope failure potentials in the study areas which are the weak zone and presence of clayey material at Sungai Batu, weathered granite zone at Bukit Relau and clay saturated zone at Air Hitam. Factors that leads to slope failures possibilities in the study areas are the permeable soil condition, high rainfall distribution and also weathering effect.

xxi

CHAPTER 1

INTRODUCTION

1.0 Preface

Reported from cases of landslides, rockfalls and other types of slope failures

never cease to end every year. In the worst case, some of the slope failure repair

methods such as retaining walls, rock bolts and geo grid had collapsed together with

the slope failures. This perpetual issue will require an amount of money to be taken

care of.

Slope is not an uncommon structure in Malaysia. It can be seen alongside the

highways, federal roads and also residential areas. About 5000 km of the trunk roads

in Malaysia involved many cut slopes and traversed on hilly and mountainous area.

Approximately about 75% of the roads are underlain by granitic formation while the

other 25% of the roads are underlain by meta – sediments formation such as the

mudstone, sandstone and siltstone. Numbers of slope failure occurrences happen on

the mountainous roads especially during the rainy season which leads to traffic

disruption, injuries and also loss of lives (Singh et al., 2008). Although it happens

frequently, an absolute prevention method has yet to be found. Table 1.1 shows the

major slope failure occurrences in Malaysia in from 2002 – 2017.

Table 1.1: Series of major slope failure occurrences in Malaysia and consequences in terms of deaths from 2002 – 2017.

Coordinate Date Location Deaths Sources Longitude Latitude 20 November Taman Hillview, Hulu Klang 101.761500 3.175442 8 2002

Continuation of Table 1.1

29 November KM 59, Kuala Lipis – Merapoh 101.961883 4.487183 4 2004 2 December Taman Bercham Utama, Ipoh 101.135061 4.645389 2

2004 31 May 2006 Kg Pasir, Hulu Klang, Selangor 101.764778 3.206783 4 KM 8.5, Pelabuhan Sepanggar, 26 June 2006 116.674689 6.095556 1 Kota Kinabalu, Sabah 30 November Ulu Yam Perdana, Kuala 101.674689 3.389919 2 2008 Selangor 6 December Taman Bukit Mewah, Bukit 101.769722 3.186111 5 2008 Antarabangsa, Hulu Klang 16 January Bukit Kanada, Miri, Sarawak 113.996494 4.393056 2 2009 29 January Sandakan, Sabah 118.127078 5.847014 2 2011 Rumah Anak Yatim At – 21 May 2011 101.814444 3.138611 16

Taqwa, Hulu Langat Department of Public Works Malaysia Works ofPublic Department Perkampungan Orang Asli Sg 7 August 2011 101.370464 4.487361 7 Ruil p18 February Kg Terusan, Lahad Datu, Sabah 118.359314 5.048847 2 2012 Kazmi 3 July 2013 Ukay Perdana, Selangor - - 3 et al. (2016) 16 July 2013 Kampung Mesilau, Kundasang - - 1 Utusan 30 December KM Jalan Brinchang – Tringkap - - 2 2014 Astro 31 December Kampung Raja, Cameroon Awani - - 1 2014 Highland 22 October Tanjung Bungah, Penang - - 14 Reuters 2017

Shallow slide is the most common type of slope failure in Malaysia where the

slide surface is usually less than 4 m depth and occurs during or immediately after

intense rainfall (Jawaid, 2000). Other types of slope failure found are deep-seated

slide, debris flow and geologically controlled failures such as wedge failures and rock

falls.

By definition from Frasheri (2012) slope is a dynamic system of geo-

environment phenomena that are related to the movement of soil and rock masses.

Some examples of slope failure including avalanche of the rocks, debris and soil flow,

collapse of the blocks and landslides. Slope failure phenomena can be defined as an

engineering and environmental issue. In order for a slope to fail, there is a process related to deformation of the rock mass, accumulation of stress and mineralogical changes involved. The body movement only occurs as the last stage and associated directly with mechanical and geological condition of the rock mass. The slope become unstable as the rock mass loss mechanical stability due to full compliance of the unstable condition. The rock mass sensitivity to the external force also increased as it become more unstable.

There are many factors that affected slope stability therefore causes slope failure to occur. One of the factor is due to gravitational force which pulls everything to the direction of Earth’s centre. Unstable rock or structure will allow the gravitational force to act and end up as landslide or rockfall. Another major factor that influences slope stability is water. Additional of water on the subsurface will results as the slope to increase in weight therefore becomes unstable. Oversaturated soil with water will cause the grain to lose contact to one another and can also change the angle of repose.

The nature of Earth material such as clayey type of soil and the presence of structure such as fracture and weathered zone in the subsurface can also cause slope failure to occur. On top of all, the most critical factor for slope failure to occur is triggering events such as heavy rainfall and earthquake (Nelson, 2013).

1.1 Problem statements

Aside from the slope at the roadsides, residential areas and agricultures are developed on the slope areas too. The limited flat land in Penang Island has urged developers to develop buildings on the steep slopes (Teh, 2000). This irreversible action has taken tolls on the hills ecosystems. As the developed slope areas spreads, the effects could be more severe and worse. On top of that, illegal forest clearing and

illegal farming on slopes without any consideration of the subsurface condition could accelerate soil erosion and slope failures. Table 1.2 shows the summary of slope failures in Penang Island in 2008 to 2017. This indicates that slope failure in Penang

Island is a crucial matter.

Table 1.2: Summary of slope failures in Penang Island.

Date Location Remarks Collapsed retaining wall 4 October 2008 Persiaran Kelicap, Sungai Ara caused flooding and mudslide to resident houses. Landslide caused road 7 October 2008 Jalan Teluk Kumbar – Balik Pulau closure to traffic Pangsapuri Farlim Tower, Bandar Landslide, damage on 6 April 2009 Baru Farlim, Air Hitam vehicles 10 June 2010 Pangsapuri Bandar Baru Air Hitam Landslide Mudflow and mudslide from 24 June 2010 Batu Feringghi nearby hillslope development project Landslide, caused collapsed 10 March 2011 Taman Terubong Jaya of retaining wall and buried two cars Landslide at KM 2.1, KM September 2013 Penang Hills 2.5 and KM 4.0 Landslide near Kek Lok Si 29 October 2016 Air Hitam Temple heading to Air Hitam Dam Landslide forced road 7 November 2016 Jalan Ujung Baru, Teluk Bahang closure to traffic Three newly built luxury 5 November 2017 Tanjung Bungah houses destroyed.

In accordance to slope failure occurrence, Department of Public Work

Malaysia will act according to Malaysia Standard Code Practice MS 2038: 2006: Site

Investigation – Code of Practice which included the application of boring log, ground water observation and Mackintosh probe test and Malaysia Standard Code Practice

MS 1056: 2005: Soil for Civil Engineering Purpose – Test Method which included laboratory test of disturbed and undisturbed soil samples. Slope failure is a perpetual issue in Malaysia, any method that can be in assistance to solve such issue should come to the front. In this research, the ability of geophysical methods in dealing with engineering and environmental issue are tested to determine the slope failure potential.

The common geophysical methods apply for slope failure studies are 2D resistivity method and seismic refraction method (Popescu et al., 2014; Giocoli et al.,

2015; Muztaza et al., 2017; Hazreek et al., 2017). In this research, the application of

2D resistivity method is paired with induced polarization method as this combination in slope failure studies is yet to be well-known (Marescot et al., 2008).

1.2 Research objectives

The objectives of conducting this research is listed as follows:

i. To investigate slope at the selected highly suspected area in Penang

Island (Sungai Batu, Bukit Relau and Air Hitam) using 2D resistivity

and induced polarization methods.

ii. To analyse and correlate the results from 2D resistivity method,

induced polarization method, laboratory tests and rainfall distribution

for slope failure potentials.

1.3 Scope of research

This research was conducted at Penang Island which is generally made up of granitic rock. The three study areas were chosen based on the landslide distribution in

Penang Island obtained from previous studies and also cases of landslide reported.

The geophysical methods used in this study were 2D resistivity method and induced polarization (IP) method by using ABEM SAS4000 Terrameter and ABEM

ES 10-64C electrode selector. The resistivity inversion models were produced by using

RES2DINV software by Geotomo. Each model inversion was studied and observed so that any slope failure possibilities can be detected.

Aside from geophysical methods, the soil laboratory tests were also conducted in order to measure the soil porosity, moisture content and particle size distribution.

Rainfall distribution were also factored, in order to identify the potential of the slope failures.

1.4 Significance of study

People tend to use the ground methods, geotechnical methods and remote sensing over the geophysical methods (Talib, 2003; Ahmad et al., 2006; Khan et al.,

2010; Lateh et al., 2011; Department of Public Works Malaysia, 2018). There are many reasons that geophysical method should be chosen when dealing with engineering and environmental issues. The geophysical methods are non – destructive, low cost consumption and have wider area coverage. Geophysical methods also have the potential in dealing with engineering and environmental problem such as slope failures.

This research aims to apply geophysical methods in dealing with engineering and environmental problems Thus, this research is carried out in order to convince the potential lies within geophysical methods in dealing with engineering and environmental problems.

By applying geophysical methods in slope study, the expenditure can be reduced and remedial works will be done in more efficient ways and not blindfolded.

The significant of this study is to understand the mechanism of slope failures at highly potential areas in Penang Island as most of the study conducted before were site specific. Another significant of this study is the application of induced polarization method which rarely used for slope study. This aims to detect saturated zone and high moisture area in the subsurface and its origin.

1.5 Thesis outlines

This thesis contained five chapters as follows:

Chapter 1 is the introduction of the research where the preface of study, problem statements, research objectives, scope of study, significance of study, and thesis outline are explained.

Chapter 2 is the literature review. The background of the slope is elaborated, theory of 2D resistivity and IP method is explained and the previous study that related to this research are discussed.

Chapter 3 is the materials and methods chapter. The details about the study area including the geological of the area and the survey lines orientation are shown.

This chapter also explained the equipment used and the procedures during data acquisition. Aside from that, procedure that related to soil laboratory tests are explained as it is part of the research element.

The results are discussed in Chapter 4. This includes the soil laboratory tests, particle size distribution results and geophysical results. The results are correlated to the rainfall distribution. Empirical correlation between laboratory test and geophysical results are also shown in this chapter.

Chapter 5 is the conclusion and recommendations. The research findings are concluded and discussed. Some recommendations for future works are also suggested in this chapter.

CHAPTER 2

LITERATURE REVIEW

2.0 Introduction

For engineering and environmental purposes, people tend to choose the airborne and satellite methods such as remote sensing and Geographic Information

System (GIS) or the direct ground-based techniques such as piezometer and laboratory tests over geophysical method which are also capable in solving the engineering and environmental problems (Talib, 2003; Ahmad et al., 2006; Khan et al, 2010; Lateh et. al, 2011). Fortunately, people had grown interest in geophysical method in order to investigate engineering and environmental problems such as slope stability in the last several decades (Jongmans and Garambois, 2007; Chambers et al., 2011). Each geophysical method has their own specialities on specific targets and environments which will produce good results besides time and cost effective. Several studies on slope failures and the application of geophysical methods on slope stability are included in this chapter.

2.1 Previous studies

A landslide on argillaceous material occurred in Western Cameroon around

Kekem area. Geophysical and geotechnical surveys were carried out by Epada et al.

(2012) with purpose to understand the triggering processes and mechanism of the landslide and to assess the slope stability. Resistivity survey had been conducted by using electrode configuration of Schlumberger array in order to monitor the behaviour of the landslide in term of resistivity. A zone of low resistivity value was identified as clayey sand-filled aquifer in the subsurface of the landslide. This aquifer was suspected

8 to be the triggering factor of the landslide. Based on the geotechnical sounding result, the aquifer had a thickness of 7.0 m whereas the depth of the landslide crest level to the failure surface reached 3.0 m and 20.6 m. Next, laboratory tests were carried out to evaluate the cohesion of the soil and the angle of internal friction. The tests result shows that the soil has low consistency which is almost doughy. As for stability analysis, the mean value of the factor of safety (FOS) is 1.4 which was lower that the slope stability coefficient which is 1.5. Therefore, the slope was unstable and likely to reactivate at any moment.

A landslide was studied at Buzad village, Timis County, Romania by using electrical resistivity tomography (ERT). The landslide occurred in 2006 was reactivation of an old landslide. Popescu et al. (2014) conducted three resistivity survey over the main body of the landslide by using electrode configuration of Wenner array in 2007, 2012 and 2014 (Figure 2.1). The length of the profile was 90 m in 2014 but 100 m in 2007 and 2012 and achieved depth of about 15.0 m along all longitudinal profiles. The results showed that a section with resistivity value of 0-35 Ωm at the upper half while the middle part ranged from 55 – 100 Ωm. The bottom part recorded as shallow high resistivity zone. This suggest that high water content from the middle part of the profile may be the triggering factor of the reactivation. The hypothesis was confirmed from an in-situ observation of the ground water table appeared to the surface. Whereas the high resistivity value of the bottom part is compact material

(sands) embedded in clayey matrix of the old landslide body. Resistivity method allows one to identify high water content area that cause the reactivation and thus reconstruction of the landslide body including the body materials in movement and its volume. Aspect noted in all resistivity profiles, the new and old landslide were located in a zone of obviously changing resistivity values.

(a)

(b)

(c)

Glacial erosion had produced ‘crag and tail’ structure at the Edinburgh Castle which is one of the most important heritage site in Scotland. The crag consists of columnar jointed basal that formed a gentle slope protecting the tail of sediment from glacial erosion. An apparent instability was observed in the southern side of the ‘tail’ between the Edinburgh Castle Esplanade and Johnston Terrace in 1997 which later initiated the geotechnical and geological investigation on the particular slope including

10 electrical resistivity survey. In order to determine the subsurface structures and variations in pore fluid distribution within the slope, Donnelly et al. (2005) conducted six resistivity survey lines with length of 24 m with electrode configuration of Wenner array. Line 1 to Line 5 were positioned down the slope perpendicular with the southern boundary wall of Esplanade whereas Line 6 was positioned across the slope parallel to the Esplanade wall. A shallow slope failure was confirmed based on the result however the initial date of failure is not known but likely since at least 1950’s. The resistivity profiles indicated that the instability was due to zones of low resistivity and high saturation. The backscarp also seems to be associated with relatively thin clay layer which apparently not a slip plane but however may cause the local groundwater to flow in as a highly permeable fill material. Similar condition was found to the east and west of the uppermost featured of the landslide lateral continuation.

Giocoli et al. (2015) performed a joint analysis of geophysical surveys, aerial photos interpretation, morphotectonic investigation, geological field survey and borehole data to investigate an area in the Montemurro territory in southeastern sector of High Agri Valley (Basilicata Region, southern Italy). Generally, the area consists of steep slopes, encased streams within narrow and deep land incision and abrupt acclivity change due to tectonic structures or lithological variation. This area has been affected by the hydrogeological instability which included active and inactive landslides. Surficial investigation identified the existing of northwest (NW) – southeast (SE) trending and southwest (SW) facing scraps between villages of Pergola to the north and Voggioni to the south. Geophysical surveys were conducted to unravel the surface expression of the strand. Three resistivity survey lines were carried out across the NW-SE scarp by using Wenner-Schlumberger electrode configuration with

20 m spacing. Survey line ERT1 and ERT3 achieved depth of penetration of 150 m

11 with spread of 940 m length whereas survey line ERT2 achieved the same depth with spread of 1100 m length (Figure 2.2). In the upper of line ERT2, a body associated with the Verdesca landslide was identified between 430 m and 1100 m based on the resistivity values (25 – 70 Ωm). The estimated thickness was derived from borehole data GB1 – GB3 and resistivity contrast from ERT2, varies from less than 10 m and up to 30 m. Another geophysical method done was a high-resolution seismometer with

24-bit dynamic was aimed at the very low amplitude range. Thirty-five ambient noise measurements with each duration of 12-15 minutes with additional measurements overlap with all resistivity profile lines were taken. The results successfully showed the fundamental frequency was related to the depth variation of the seismic impedance differ between the low resistive Quartenary continental deposits (QD) and Albidona

Formation (AF) and high resistive Gorgoglione Flysch (GF). These joint analyses succeed in achieving the objectives of the study which is to image the shallow geological and structural setting as well as to verify the nature of the NW-SE scarps and thus interpreting it as surface expression of the Montemurro Fault.

Figure 2.2: Resistivity profiles for ERT1 – ERT3 (Giocoli et al., 2015).

A study of landslide was conducted by Drahor et al. (2006) at district of Aydin,

Turkey in 2003 on a slope next to a newly built school building. The slope was unstable due to an excavation work and heavy rainfall. Three resistivity survey lines were conducted over the landslide using Wenner electrode array resulting on the geometry and characteristic of the landslide. Eight boreholes were also carried out on the landslide. Both methods showed a fault presence at the area with addition of a surface rupture indicated by the resistivity profiles (Figure 2.3).

Figure 2.3: 3D fence diagram for resistivity section in Aydin (Drahor et al., 2006).

Resistivity and self-potential methods were applied to study the hydraulics of landslide process as these methods are capable to provide spatial and volumetric information as well as sensitive toward hydraulic changes in the subsurface. A system called Automated Time-lapse Electrical Resistivity Tomography (ALERT) was applied on an active landslide near Malton, North Yorkshire, United Kingdom in order to develop a 4D landslide monitoring system that capable to characterise the subsurface structure of landslide and detecting the hydraulic precursor to movement.

According to Chambers et al. (2009), the time-lapse resistivity imaging was able to show the changes related to the seasonal temperature variation, moisture content and ground movement within the landslide. The 4D resistivity imaging was an effective method to investigate the hydraulic of a landslide whilst taking the influence of temperature and electrode displacement into consideration.

One of study conducted in Malaysia is to investigate the influence of natural slope geomorphology on active cut slope failure near Gunung Pass, Simpang Pulai-

Lojing Highway by Shuib et al. (2012). Previous studies have found out that there was close correlation between the relict landslide scars on the natural slope and the

14 presence of cut slope instabilities. Hence resistivity and seismic refraction surveys were conducted across the relict landslide scars. Based on the results, the steep relict scarps were connected at the subsurface by a circular slip surfaces which is represent by steep weak zones. The head scarp zone was represented by the topmost slip surfaces with tension cracks suggestive of extensional strains while the toe region was represented by the reversed slip surface suggestive of compressional strains. The failure was due to the head and the upper main body zone sliding roughly orthogonal to the foliation while the middle and toe zone is sliding down and out of the foliation.

From the surface and subsurface observation, the natural slope was inferred to be creeping which later induces shallow planar and circular failures at the cut slope.

Based on a study conducted by Chigira et al. (2011), landslides in weathered granitic rocks are greatly influence by the type of weathering and it is site specific. The mechanism of failure in deep weathering profiles in Malaysia are highly controlled by the nature of the weathered material and its mass structure. In Japan, the landslides occurred due to moderately weathered decomposed granite. The rock loosened rapidly as it was exposed to the ground surface and formed loosened layers to slide. The fact of weathered granite is prone to slide was known for many years. However, the landslide mechanism was not yet fully understood due to the weathering profile was site specific and differ among climates. More than 80 landslides occurred along the mountainous main highway to Genting Sempah, Pahang. Most of the landslides occurred in a few hours in June 1995 as there was a continuous rain for more than 72 hours. Majority of the landslide can be categorized as small to medium size with sliding materials less than 500 m3. Types of slope failure included shallow and deep sliding, rock block sliding and debris flow. This is a region that formed by granite batholiths and the granite has undergone intensive tropical weathering. This resulting

15 in forming weathered profiles with various characteristic and thickness. Most of the landslides are related to the weathered materials. The intense and heavy rain saturate the residual soil thus, triggering two major landslides upstream of a tributary of Sungai

Gombak.

A study conducted by Komoo (1997) at Bukit Antarabangsa concluded that the weathered granitic material may contributed to landslide as it was porous, easily crumble and inherited the plane of weakness from the parent rock. Bukit Antarabangsa has marked in the history of Malaysia landslide disasters as there were six major landslides occurred since 1993. The hill was underlain by granite and extensive weathering has transformed the granite into residual soil (grade VI) and completely weathered material (grade V). The weathered material was sandy with average thickness profile of 30 m. The subsurface loose its consistency with increasing amount of water. This condition triggered a collapsed of twelve storey Highland Tower condominium in 1993.

Another study conducted by Komoo and Lim (2003) at the same Bukit

Antarabangsa with 100 m distance south from the previous landslide. In 2002, a bungalow was destroyed due to landslide killing eight people. The landslide mechanism was rather complex but it is due to continuous heavy rain that triggered the sliding. That was not all, other factors may also contribute the triggering of the landslide. Factors such as weathered granitic materials that prone to failure, geological lineament that facilitating the sliding. An old landslide landform found nearby also aided the accumulation of groundwater that resulting in the landslide in 2002.

Muztaza et al. (2017) has conducted a slope failure evaluation and landslide investigation by using 2D resistivity method in Selangor. Nine survey lines were

16 carried out at Site A and another six survey lines at Site B with minimum electrode spacing of 5 m were performed using Pole – Dipole electrode array. Alluvium or highly weathered zone with resistivity value of 100 – 1000 Ωm found at depth > 30 m, saturated area with resistivity value of 1 – 100 Ωm and boulders with resistivity value of 1200 – 7000 Ωm. The granitic bedrock was identified with resistivity value of >

7000 Ωm (Table 2.1). From this study, the triggering factors for slope failure was due to the saturated zones, highly weathered zone, highly contain of sand, and boulders.

Table 2.1: Summary of resistivity value with interpretation at the study areas in Selangor (Muztaza et al., 2017).

Resistivity value (Ωm) Interpretation 1 – 100 Saturated zone 100 – 1000 Alluvium or highly weathered zone 1200 – 7000 Boulder > 7000 Granitic bedrock

A slope failure in Kenyir Lake was evaluated by Hazreek et al. (2017) using resistivity method with a reference to a borehole data. Two resistivity survey line was carried out using electrode array of Schlumberger array. The main factor that triggered the slope failure is the combination of heavy rainfall and the presence weakness zone.

The results also successfully detected fault and rock discontinuities which associated by low resistivity value (Figure 2.4). By applying resistivity method, the shape and depth of subsurface landslide that caused ground damage can be determined easier as the resistivity method mapped the subsurface profile based on the resistivity values.

(a)

(b)

Figure 2.4: Resistivity profiles according to the location (a) top of landslide (b) toe of landslide (Hazreek et al., 2017).

Yaccup and Tawnie (2015) had conducted a comparison between resistivity

imaging technique and borehole data on determining depth of granite body as the

active quarry site in order to define the accuracy of the resistivity data with borehole

information. Eight resistivity profiles were built near to several boreholes with

maximum distances of less than 50 m. The result shows that in the tropical region such

as Malaysia (Table 2.2), the resistivity value of low weathered granite to fresh granite

is more than 5000 Ωm, medium weathered between 1000 – 5000 Ωm and highly

weathered granite is lower than 1000 Ωm. Resistivity and borehole recorded accuracy

more than 80% with another 20% error due to changing of resistivity due to existence

of water, fractured zone and the distance between the survey line and borehole.

Table 2.2: Resistivity range of weathered granite and fresh granite in Malaysia (Yaccup and Tawnie, 2015).

Resistivity value (Ωm) Interpretation < 1000 Highly weathered granite 1000 – 5000 Medium weathered granite > 5000 Low weathered – fresh granite

Jinmin et al, (2013) applied 2D resistivity method for subsurface study at

Bukit Bunuh. The aim was to identify the bedrock structure, fracture of shallow subsurface and identify the alluvium pattern. Due to large study area, the borehole data is not adequate to measure the subsurface condition. Therefore, the borehole data were used as preliminary information to refine 2D resistivity results. A survey line of 2.065 km was done by applying Pole – Dipole array with 5 m minimum electrode spacing.

The results show two major zones in the study area; resistivity value of 10 – 800 Ωm interpreted as alluvium and resistivity value of more than 3000 Ωm as boulder and granitic bedrock.

A study on hill-slope movement was conducted by Khan et al. (2010) to monitor a land movement during rainy seasons in 2008. Two landslides occurred between 23 and 26 km stretched through Gunung Pass, Cameron Highland in April and December 2007 respectively. Based on deduction, the hilly terrain was triggered by the rainfall events. Moreover, the slope at Gunung Pass may experience some movement within the subsurface from the past rainfall events before the failure occurred in 2007. Therefore, a theodolite study was conducted on the slow hill-slope movement to monitor the land movement during rainy seasons of 2008. The results successfully identified a close correlation between the hill-slope displacements and the rainfall amount during the study period. The slow hill-slope movement occurred after a heavy rainfall in 2008. Result from Prism III theodolite showed that the down slope movement was more than 4 m, southward movement of 1.94 m and westward

19 movement of more than 5 m (Figure 2.5). As conclusion, the area was unstable and prone to slide.

(a)

(b)

Figure 2.5: a) Average rainfall at Cameron Highland from March 2008 to Dec 2008 b) Observed total displacement at Prisms I-VI (Khan et al., 2010).

Xu et al. (2016) investigate a landslide in southwestern China by using time – lapse electrical resistivity tomography (TLERT) in November 2013 and August 2014.

The study aims to investigate the subsurface hydrogeological environmental and evolution of landslides based on the electrical structure and geometry of sliding surface. The landslide mechanism is inferred based on the spatiotemporal characteristic of the surface water infiltration and groundwater flow within the

20 landslide body. The resistivity inversions accurately defined the interface between the

Quarternary sediments and bedrock when combined with borehole data. The results show that the surface water penetrates via the fracture zone and fissures into the slipping body and drained as fissure water in the fractured bedrock. This eventually caused the weathered layer to soften and erode. This finding indicated that the TLERT monitoring able to provide preliminary information on critical sliding and can be used for landslide stability analysis and prediction.

A study conducted by Rahardjo et al. (2001) in Nanyang Technology

University Campus successfully indicated that the landslides were initiated by the rainfall infiltration. The daily rainfall and the cumulative rainfall both act as the triggering factor for slope failure. A five days cumulative rainfall with more than 60 mm combined with daily rainfall exceeded than 90 mm was sufficed to initiated a landslide.

A water – seepage coupling model and stability analysis was developed to study the effect water on the soil – slope stability by Liu and Li (2015). In order to analyse the effect of water seepage which came from rainwater and water level fluctuation, safety factor of slope by rainfall and water level fluctuation was simulated.

The simulated result indication that both rainfall and water level fluctuation decreased slope stability according to the safety factor of the slope (Figure 2.6). The reason for this phenomenon is increased in total soil weight and slight improvement of pore pressure in slip surface. Therefore, slope stability is highly affected by rainfall and water level fluctuation.

Factor of safety of Factor

Figure 2.6: Safety factor caused by reservoir water, rainfall and combination of rainfall and reservoir water fluctuation (Liu and Li, 2015).

Lateh et al. (2011) monitored the slope movement before the slope failure occurs by using inclinometer, piezometer and rain gauge at 3.9 km Jalan Tun Sardon,

Penang. The soil was detected move on 30th July 2010 and 4th November 2010 due to the intense rainfall before the occurrence. Therefore, the soil movement and rainfall intensity can be related in affecting slope failure occurrence.

A slope investigation was carried out at Fraser’s Hill due to a debris flow was found at 9 km of Fraser Hill road by Ghazali et al. (2013). Using resistivity method, three survey lines parallel to the slope and two survey lines perpendicular to the slope were applied at the reported area. Results showed existence of granite body at the central part of the east line and nearest to the ground surface. The presence of resistivity less than 600 Ωm within the granite body is interpreted as highly fractured and water conducting zones. The same zone was detected at east – west and north south lines. Therefore, the fracture granite is virtually floating above the water saturated zone thus, considered as unstable.

Ng et al. (2015) has utilized resistivity method to investigate a slope failure at

Precinct 9, Putrajaya with aided by borehole sampling data. Results showed two zones were encountered, high water content zone with resistivity value of 10 – 300 Ωm and dry zone with resistivity value of 300 – 100 kΩm. Borehole sampling recorded subsurface condition was range from fresh to highly weathered granite. Rock quality designation (RQD) indicated value of 0 – 100 % which show the presence of rock fracture that cause seepage flow within the subsurface. Therefore, low resistivity zone face risk for slope failure which demand remedial measures.

Ramadhan et al. (2015) has conducted resistivity method by using electrode configuration of Wenner – Schlumberger at Bendanduwur Gajahmungkur Semarang to identify landslide slip surface. The results of resistivity method reached to depth of

14 m and it shows that the lithology of the location consist of soil, clay, sandstone and breccia. The contact between clay and sandstone found at depth 1.25 to 6.76 m found in the survey lines is interpreted as the slip surface of landslide due to the contact indicated disconnection between the layers also known as weak zone. Landslide can be expected in this location due to the presence of clay, which is impermeable for water to pass through.

A study by Ahmad et al. (2006) at Paya Terubong on a landslide that occurred in 1998. The average thickness of highly to completely weathered granitic soil is found to be approximately 30 m. The sampled residual soil consists of gravel (14%), sand

(55%), silt (18%) and clay (13%) which can also represent as course-grained residual soil which is also known to be susceptible to landslide.

Hashim et al. (2015) used shear box test in order to identify slope failure in

Penang Island. This study aims to determine the soil shear strength under saturated

23 shear box test for samples taken from slope failure locations. The locations were selected from slope failure tragedy sites along Teluk Bahang – Balik Pulau road. The summary of shear box test and particle size distribution test (BS 5930: 1990) is as in

Table 2.3 and Table 2.4 respectively. The results indicated that the slope failures were found mostly in gravelly silt soil and the range of cohesion recorded was largest compare to others soil.

Table 2.3: Saturated peak shear box and particle size distribution tests, BS 5930: 1990 (Hashim et al., 2015).

Soil types Range of cohesion Range of angle of shearing resistance Silt 0.1 – 33.4 22.9 – 47.1 Very silty sand 0.6 – 8.3 30.7 – 62.4 Sandy silt 0.0 – 27 18.6 – 52.9 Very silty gravel 0..8 – 37.5 24.5 – 57.2 Gravelly silt 0.0 – 40.8 18.1 – 65.8

Table 2.4: Result of soil classification and particle size distribution test, BS 5930: 1990 (Hashim et al., 2015).

Soil Percentage (%) Slope condition Soil classification sample Gravel Sand Silt Clay A Unfailed 64.88 18.08 17.04 0.00 Very silty gravel B Failure mass 37.92 20.67 41.25 0.16 Gravelly silt C Failure scar 36.26 30.16 33.53 0.05 Gravelly silt D Failure mass 36.88 24.21 38.67 0.24 Gravelly silt E Failure mass 26.19 36.88 36.85 0.08 Gravelly silt F unfailed 76.93 7.45 15.50 0.12 Very silty gravel

A study conducted by Bery (2016) to identify the empirical correlation of soil properties of shear strength, moisture content, void ratio, porosity, saturation degree and Atterberg’s limit with electrical resistivity values from resistivity tomography models. Eleven undisturbed clayey sand soil samples were collected at different distances, depths and both infield and laboratory condition. Based on the results, the electrical resistivity values were highly influence by the soil properties. A good estimation of soil mechanics properties can be obtained from the results of electrical resistivity tomography model.

A study using Geographic Information System (GIS) by Talib (2003) in order to generate a map for Penang Island to evaluate the landslide hazard. The locations of landslides were identified via imagery and aerial photography interpretation and followed by field surveys. Factors that influenced landslide occurrences were topographic slope, topographic aspect, topographic curvature and distance from drainage from topographic database, geology and distance from lineament from geologic database, land use from TM satellite images and vegetation index value from

SPOT satellite data. Using the probability of likelihood ratio method, the hazard areas were analyses and mapped using landslide occurrence factors. As a result, the generated hazard map and the existing data of landslide areas showed satisfactory agreement.

Chau et al. (2004) also conducted a landslide hazard analysis by using landslide inventory and GIS at Hong Kong based on landslide data from 1984 to 1998.

The result show strong correlation between rainfall distribution and landslide consequences with average annual fatality and injury rate caused by landslide are

11.35 and 11.63 respectively. A landslide hazard zonation was established from the historical data.

2.2 Basic theory of 2D resistivity method

Electrical surveys are capable in determining the subsurface resistivity as well as chargeability distribution based on the ground surface measurement. These ground measurements are related to various parameters such as fluid content, porosity and degree of water saturation in the rock. For many decades, electrical surveys have been used for many type of investigations such as mining, hydrogeology, geotechnical and environmental surveys (Loke, 2001).

The fundamental of 2D resistivity method is the Ohm’s Law. The equation for

Ohm’s Law in vector form for current flow in a continuous medium is as stated in

Equation 2.1.

J = σE (2.1) where σ is the conductivity of the medium, J is the current density and E is the electric field intensity. Parameter measured in 2D resistivity method is the electric field potential. However, in geophysical surveys, the term resistivity, ρ is commonly used.

Hence, resistivity is reciprocal to conductivity, σ as in Equation 2.2.

1 ρ = σ (2.2)

The relationship between electric potential, V and electric field intensity, E is as shown in Equation 2.3.

E = −∇V (2.3)

Where 훻 is the gradient operator therefore, 훻V is the gradient of electric potential.

By combining both Equation 2.1 and 2.3 will produce Equation 2.4.

J = - σ∇V (2.4)

In almost all survey, the current sources are in the form of point sources. Taking the simplest case of homogeneous subsurface and a single point current source on the ground surface for easier understanding, the current flows radially away from the source and the potential varies inversely with distance from the current source. The equipotential surface has a hemisphere shape and the current flow is perpendicular to the equipotential surface (Figure 2.7). This given potential as stated in Equation 2.5.

ρI V = (2.5) 2πr

26 where r is the distance of a point in the medium (including the ground surface) from the electrode and I is the value of current injected through the medium. In practice, a typical arrangement will be with four electrodes (P1 and P2 for potential point sources and C1 and C2 for current point sources). The potential difference, △V is given as in

Equation 2.6.

ρI 1 1 1 1 △V = ( - - + ) (2.6) 2π rC1P1 rC2P1 rC1P2 rC2P2

The equation above gives the potential that would be measured over a homogeneous half space with four electrodes array. However, the actual surveys are inhomogeneous medium with the subsurface resistivity has a 3D distribution. From current, I and potential difference, △V, the apparent resistivity, ρa is calculated (Equation 2.7),

△ V ρ = k (2.7) a I where k is a geometric factor that depends on the arrangement of the electrodes define in Equation 2.8.

2π k = (2.8) 1 1 1 1 ( - - + ) rC1P1 rC2P1 rC1P2 rC2P2

Normally to find resistance, 푅 = V/I, hence for apparent resistivity, ρa from Equation

2.7 can be reduce to Equation 2.9.

(2.9) ρa= kR

The calculated resistivity is not the true resistivity of the subsurface and the relationship between the apparent resistivity and true resistivity is complex. In order to determine the true resistivity of the subsurface from the apparent resistivity, ρa, an inversion is needed (Loke, 2001).

Figure 2.7: The flow of current from a point current source (current electrode) and the resulting potential distribution within the subsurface (Loke, 2001). 2.3 Basic theory of induced polarization

Initially developed for mineral exploration, induced polarization (IP) method measure the ability of the subsurface to store the electrical charge. Nowadays, the IP has been extent toward studies in environmental and engineering problems (Ward et al., 1995). The IP instruments measure in either frequency domain or time domain technique. This method is known to be sensitive to the subsurface metal such as mineral ores and also been used to estimate the hydraulic properties of rocks and soils as it sensitive to the clay content and pore fluid composition as well as used to map the subsurface contamination.

Polarization occurs due to the redistribution of ions along the interfaces following the application of electric current (Figure 2.8). The ions relax to the equilibrium state upon the termination of current. This diffusion-controlled relaxation is the source of the IP subsurface response as it is equivalent to a residual current flow of a discharging capacitor. The IP method measures the magnitude of this polarization

28 as chargeability in term of time domain or in short, this method measures how well a ground to retain electrical charge (Slater and Lesmes, 2002; Alabi et al., 2010).

Figure 2.8: Equilibrium ion distribution (left). Polarization in the electric field direction (right) (Slater and Lesmes, 2002).

In the time domain, the measurement of chargeability, M is as follows (Equation 2.10)

t f V dt ∫t r 1 M= s (2.10) Vm ∆t

Where

ts and tf is the initial time and final time for applied current termination

respectively.

Vr is the residual voltage integrated over a time window defined between time

ts and tf after termination of an applied current.

Vm is the measured voltage at certain amount of time during the current applied.

∆t is the length of the time window of integration.

The common unit for chargeability is millivolts per volt (mV/V).

Figure 2.9 shows the time domain IP waveform recorded and measured properties used in calculation of M.

Figure 2.9: Principle of time domain IP signal (a) current on – off (b) the measured voltage decay (Slater and Lesmes, 2002).

2.4 Depth of penetration

Depth of investigation depends on the distance between the current and potential dipoles. As the current and potential dipoles gets further from each other, resistivity of greater depth can be detected. The same concept applied to induced polarization method (Loke, 2001).

2.5 Porosity

One of the important parameter in geology is porosity. It controls fluid storage in aquifer, oil and gas as well as geothermal system. Moreover, the extension and connection between the pore structure controls the fluid flow and transport through the geological formation. By definition, porosity is the fraction of void volume over total volume which is not that easy to quantify as the term “void” space expressed in earth materials can span over eight orders of magnitude in length scale. In hydrogeology field, there are two types of porosity which are the effective or open porosity and ineffective or closed porosity. The effective porosity is the type that allows flow of fluid or volatiles while ineffective is the vice versa of it (Anovitz and Cole, 2015).

Faur and Szabo (2011) stated that the soil porosity varies depending on the type of soil which can be differentiate through particle size distribution method (Table 2.5).

Table 2.5: Porosity of soil according to the percentage of porosity (Faur and Szabo, 2011).

Type of soil Porosity, ƞ (%) Loose 50 Compact 30 Cohesive 50 - 70 Organic 80 - 90

2.6 Moisture content

Another important parameter in this research is the moisture content. By definition, moisture content is the mass ration of the pore water to solids which usually represent in term of percentage. The pore water consists capillary and hygroscopic water retained in the voids between the solid particles. Moisture content determination is very important as it affect the mechanical behaviour of soil such as shear strength, and stiffness (O’Kelly, 2004).

2.7 Bulk density

The bulk density indicates the soil compaction and reflects the ability for water movement. Arshad et al. (1996) listed the bulk densities above threshold values of soil texture according to USDA soil taxonomy classification (Table 2.6). This is important as the high bulk density reflected soil compaction which results in restriction of root growth and also poor movement of air and water through the soil which later lead to increased water runoff and erosion from slope.

Table 2.6: Relationship of soil bulk density for root growth based on soil texture (Arshad et al., 1996).

Ideal bulk densities for Bulk densities restricted Soil texture plant growth (g/cm3) root growth (g/cm3) Sandy < 1.60 > 1.80 Silty < 1.40 > 1.65 Clayey < 1.10 > 1.47

2.8 Summary

This chapter has reviewed the previous studies related to engineering and environmental problems mostly landslides and their findings including the triggering factors for slope failures. Geophysical methods are capable to solve engineering and environmental problems. The basic theories of 2D resistivity and induced polarization methods are also explained in this chapter.

CHAPTER 3

MATERIALS AND METHODS

3.0 Introduction

Geophysical methods are techniques that capable in providing useful information about the subsurface properties. However, each method has their strength and disadvantages. Therefore, in order to study about properties of subsurface or certain material parameters, one need to fully understand the suitable technique to be use. This chapter discusses about the geophysical methods that were applied for monitoring purpose as well as to study the landslide movements. Methods applied were 2D resistivity and induced polarization (IP) methods.

The 2D resistivity method is an electrical imaging method that can be used to study the characteristics of the Earth subsurface by determining the subsurface resistivity distribution through measurement on the ground surface (Bery et al., 2012).

Induced polarization method is another electrical imaging method that uses the same principle as 2D resistivity method of measuring the subsurface condition through measurement on the ground surface. However, the IP method differ from the 2D resistivity method as it measures in term of chargeability instead of resistivity.

In order to find the relationship between the geophysical and soil parameter, the moisture content and porosity of the soil were measured. The relationship is obliged to recognize the condition of the subsurface either it is favour for slope stability problems such as landslide to occur.

This chapter explained about the steps taken in order to achieve the desired results. Starting from the geology of the Penang Island to understand more about the area followed by the soil laboratory tests then geophysical data acquisition techniques and end with the geophysical data processing steps.

3.1 Geology and geomorphology of Penang Island

Study areas are located at Penang Island (Figure 3.1). Geographically, Penang consists of two entities which are an island with 293 km² known as Penang Island and a part from the mainland with 738 km². The climate is tropical with average mean daily temperature of 27°C. Based on Department of Meteorological Malaysia observation from 1951 to 1980, the wettest period is during months of August to November with mean monthly rainfall variation recorded 237 mm in August to 384 mm in October. In

Malaysia, there are two rainy seasons which are the southwest monsoons from March to September and northeast monsoons from October to February. The highest temperature is between April to June whereas the relative humidity is lowest in June,

July and September (Ong, 1993).

Figure 3.1: Location of study areas (Google Earth, 2018).

As the study area located on the island, this part will focus on the geology of the Penang Island. The terrain here consists of coastal plains, hills and mountains with more concentrated population on the eastern part of the island. Generally, Penang

Island is underlain by igneous rocks with mainly granites. Generally, the study areas are located in South Penang Pluton (SPP) as they are approximately south latitude of

5° 23’. This pluton is formed mainly by the microcline granite (Ong, 1993).

The most significant fault in the Penang Island is Sungai Air Putih – Sungai

Dondang fault where it extends for more than 18 km from Tanjung Bungah to Bayan

Lepas. Rocks that located close to this fault zone are well-jointed and sheared (Ong,

1993). Based on the location (Figure 3.2), the study areas were within 2 km in proximity with the nearest faults. Even though they were not Sungai Air Putih – Sungai

Dondang fault, but hypothetically should have the same features of well-jointed and

35 sheared. Thus, this boost the probability of the occurrence of slope failure. According to Brideau et al. (2009), rock slope failures as in the study areas are frequent control by a complex combination of discontinuities such as folds, faults, shear zone, and/or related tectonic damage that leads to kinematic release.

Figure 3.2: General geology of Penang Island (Ong, 1993).

Aside from faults, the existence of lineaments can be related to the slope failures such as rock fall and landslide. Savigny and Clague (1992) mentioned that the lineaments were assumed to be the surface expression of faults and slope failures were frequently found adjacent to lineaments. Thus, this strengthen the previous assumption that location near faults can have the same features of well-jointed and sheared but with lower intensity as they are only expression of the faults. Based on lineament map of Penang Island (Figure 3.3), the study areas are surrounded by a lot of lineaments.

Therefore, the existence of lineaments is another element that can affect the slope failure probability at the study areas.

Figure 3.3: Lineament map of Penang Island (Ong, 1993).

3.2 Study areas

The study areas were selected based from previous studies and landslide reported. According to Lee and Pradhan (2006), Penang Island is 60% is covered with slopes and hillsides. The slopes angle is range from 25° - 87° with highest point from sea level is about 840 m. The higher elevation areas mainly concentrated at the centre of the island (Figure 3.4).

Figure 3.4: Location of study areas on topography map

Azmi (2014) mentioned that the slope angle is one of several factors that may contributed to landslide. Overlay with the results with cell size 10 from GIS mapping

(Figure 3.5), the study areas were located on slope angle between 13° - 31° (Figure

3.6). The Ministry of Science, Technology and Environment Malaysia proposed three categories for hill land which are gentle slope (below 20°), dangerous slope (20° - 30°) and critical slope (above 30°). Therefore, the study areas were located in gentle to critical slope and thus, increases the probability of the slope failure. Teh (2000) explained the frequent occurrence of slope failures in Penang Island as the developers often neglected the Land Conservation Act of 1960 which aims to conserve hill lands and to protect soil erosion and silting. This Act also disallows development on areas gazetted as “hill land” which define by Penang Structure Plan as land above 60 m.

Figure 3.5: Slope in degree for cell 10 units of Penang Island with study area locations (Azmi, 2014).

The research is conducted in month of August 2016 – January 2017 in accordance to observation conducted by Department of Meteorological Malaysia in

1951 to 1980 which shows the wettest period in Penang Island in during months of

August to November. One of the factor for slope failure occurrence is due to heavy rainfall (Komoo and Lim, 2003; Dhahor et al., 2006; Chigira et al., 2011). Three years of rainfall distribution at Bayan Lepas rainfall station were taken in order to observe the rainfall distribution trend (Figure 3.6). Based on the distribution, mean annual rainfall for 2014, 2015 and 2016 recorded 156.41 mm, 255.63 mm and 181 mm respectively. The highest rainfall distribution in 2014 was September, in 2015 was

November and in 2016 was October. This verify with observation by Department of

Meteorological Malaysia which September to November is the wettest period in

Penang Island.

600.0

500.0

400.0

300.0

200.0 Rainfall Rainfall Distribution (mm)

100.0

0.0 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Month

2014 2015 2016

Figure 3.6: Rainfall distribution from 2014 to 2016 (Department of Meteorological Malaysia, 2018).

Rainfall can also cause erosion which leads to landslide. Erosivity can be defined as the potential for rain to cause erosion and induced landslide. The characteristics of rainfall which include rainfall amount, duration, intensity and seasonal distribution are the factor that influence the soil erosion. Generally, for all climatic regions, rainstorm with intensity of more than 34 mm/day can be responsible to create erosion. In Malaysia, the amount of rainfall that can be liable to cause erosion is 20 mm/day (Roslan and Tew, 1997).

Figure 3.7 shows numbers of day with daily amount of rainfall more or equal to 20 mm/day recorded at Bayan Lepas rainfall station from 2014 to 2016. Based on the number of days, month of September recorded highest number of days that liable for erosion and followed by October. This indicated that erosion due to rainfall can occur during this period.

10 9 8 7 6 5 4

Number Number days of 3 2 1 0

Month

2014 2015 2016 Figure 3.7: Number of days with daily amount of rainfall more or equal to 20 mm/day in 2014 – 2016.

Granite is one of the rock that can be weathered due to chemical decomposition or by physical disintegration. Physical weathering is an essential part in chemical weathering. The rock breaks down by forming joints and new discontinuities and couple with fracturing. Increasing in rainfall distribution will leads to greater weathering effect as water is an intrinsic element in most chemical weathering and the rates generally increases with temperature. In short, weathering effect is greater in hot, wet climates. Table 3.1 represent the weathering grade of rock and the description.

Table 3.1: Description of weathering grade (Attewell, 1993). Degree of Descriptive terms Material description and likely engineering Weathering characteristics

Completely degraded to a soil; original rock fabric is completely absent; exhibit large volume change; the soil has not been significantly VI Residual soil transported. Stability on slopes relies upon vegetation rooting and substantial erosion & local failures if preventive measures are not taken.

Continuation Table 3.1

Rock is substantially discoloured and has broken down to a soil but with original fabric (mineral arrangement & relict joints) still intact; the soil Completely weathered properties depend on the composition of the V parent rock. Can be excavated by hand or ripped relatively easily. Not suitable as foundation for large structures. May be unstable in steep cuttings and exposes surfaces will require erosion protection.

Rock is substantially discoloured and more than 50% of the material is in degraded soil condition; the original fabric near to the discontinuity surfaces have been altered to a greater depth; a Highly weathered deeply weathered, originally strong rock, may IV show evidence of fresh rock as a discontinuous framework or as corestone; an originally weak rock will have been substantially altered, with perhaps small relict blocks but little evidence of the original structure.

Rock is significantly discoloured; discontinuities Moderately weathered will tend to be opened by weathering process and III discoloration have penetrated inwards from the discontinuity surfaces.

Some discoloration on and adjacent to discontinuity surfaces; discoloured rock is not significantly weaker than undisclosed fresh rock; Slightly weathered weak (soft) parent rock may show penetration of II discoloration. Normally requires blasting or cutting for excavation; suitable as a foundation rock but with open jointing will tend to be very permeable.

No visible sign of rock material weathering; no internal discoloration or disintegration. Normally I Fresh requires blasting or cutting for excavation; may require minimal reinforcement in cut slope unless rock mass is closely jointed.

3.2.1 Sungai Batu

The area is located behind Sekolah Kebangsaan (SK) Sungai Batu, Teluk

Kumbar (Figure 3.8) with latest landslide occurrence in April, 2016. Hence, this area was expected to bring significant results to the study. A 200 m resistivity survey line

42 was laid perpendicular to the slope and crossed the landslide location with centre of survey line at 5.290°N, 100.242°E. Grade III – IV weathered rocks can be seen on the surface that further convince about the unstable slope condition.

Figure 3.8: Aerial view of Sungai Batu survey line (Google Earth, 2018).

3.2.2 Bukit Relau

The site is located near Hutan Simpanan along Bukit Relau heading to Balik

Pulau (Figure 3.9). This area was known to be vulnerable to landslide and rockfall especially during wet season. During wet season, landslide or rockfall can be seen along the road although maintenances and prevention works already been applied. In this study area, 200 m survey lines was conducted perpendicular to the slope with center at 5.344°N, 100.250°E. Based on observation on surface, the exposed weathered boulders can be classified as grade III – IV.

Figure 3.9: Aerial view of Bukit Relau survey line (Google Earth, 2018).

3.2.3 Air Hitam

Air Hitam is one of the frequent area for landslide occurrence according to the location of previous landslides. A 100 m survey line was conducted along the road toward Air Hitam Dam (Figure 3.10) with centre of survey line at 5.397°N, 100.270°E.

From observation along the road, large boulders and rocks can be seen on the surface aside from minor landslide and rockfall during wet season. Weathered rock found on the surface can be classified as weathering grade III.

Figure 3.10: Aerial view of Air Hitam survey line (Google Earth, 2018).

3.3 Laboratory test

In order to find the relationship between the geophysical results and soil parameters as well as to determine the soil condition at the study area, soil samples are collected from the study areas and analysed. Figure 3.11 shows the equipment used for soil analysis and label as in Table 3.2. Soil samples were taken along the survey line.

Total of six soil samples taken at Sungai Batu, four soil samples taken at Bukit Relau and one soil sample taken at Air Hitam. The samples were taken at different distances in order to observe the soil condition at different geomorphology and elevation along the survey lines. Test for moisture content were according to BS 1377 (1990) while the porosity was determined from relationship between unit weight and volume. The particle size distribution was performed according to ASTM 422 standard procedure.

Figure 3.11: Soil laboratory test equipment.

Table 3.2: Name of equipment as labelled in Figure 3.11. No Equipment No Equipment 1 Memmert Oven 4 1 liter cylinder flask 2 Tray 5 Electronic weights 3 Glove 6 Soil sample

3.3.1 Moisture content measurement

Soil samples taken were placed into a tray according to the study areas and foreign materials such as grass or tree roots were removed. The sample was weighted using the electronic weights. This was labelled as wet sample. Next, the sample was put into the Memmert oven at 105°C for 24 hours to remove the moistures. Then, the sample was weight once again using the electronic weights and labelled as dry sample.

By using the Equation 3.1, the percentage of moisture was calculated.

wet sample (g)-dry sample (g) Moisture content, w = x 100 (3.1) dry sample (g)

3.3.2 Porosity calculation

The soil porosity is calculated from relationship between unit weight and volume (Cheney and Chassie, 1993). The unit weight or also known as bulk density,

ρb, is defined as (Equation 3.2).

Weight of sample (g) Bulk density, ρ = b Volume of sample (cm3) (3.2) Porosity, ƞ is defined as in Equation 3.3 follows

e Porosity, ƞ = 1 + e (3.3)

where:

e = void ratio of a soil

Void ratio, e of a soil is determined by using Equation 3.4

3 Volume of void (cm ) (3.4) Void ratio, e = Volume of solid (cm3)

3.3.3 Particle size distribution (PSD) analysis

Particle size distribution (PSD) analysis is an index test for soil as it represents the relative proportion of different sizes of particles. By conducting this analysis, the soil particle distribution can be obtained either it contained predominantly gravel, sand, or silt and clay. This is important as the particle sizes is likely to influence the engineering properties.

. PSD analysis were done by using the same soil samples used in the previous laboratory tests. From the PSD analysis, uniformity coefficient, Cu and coefficient of curvature, Cc were calculated (Appendix A). For further study the soil sample, the mean and sorting of the soil were also calculated by using formula provided by Folk and Ward (1957).

In order to conduct this analysis, equipment used as in Figure 3.11 and the labelled in Table 3.3.

Figure 3.12: PSD analysis equipment.

Table 3.3: Name of equipment as labelled in Figure 3.12. No Equipment 1 Endecotts Sieve Shaker EFL2000 Endecotts Laboratory Test Sieves 1. No 4 (4.75mm) 2. No 10 (2 mm) 3. No 20 (0.85 mm) 2 4. No 40 (0.425 mm) 5. No 60 (0.25 mm) 6. No 140 (0.106 mm) 7. No 200 (0.075 mm) 8. Pan 3 Electronic weight

3.3.3(a)Experiment procedure

To conduct PSD analysis, the soil need to be oven-dry beforehand and any lumps were break into small particles. The sieves were stacked according to their sizes in decreasing manner and end with the pan. The procedure was conducted according to ASTM 422. Soil was poured into the top sieve and then covered tightly to ensure no spilling during the shaking process. The stacked sieves were put at the centre of the sieve shaker and then, the sieve shaker run for 10 minutes. After 10 minutes, the sieves were taken out and the retained soil in each sieve was weighted.

3.3.3(b) Coefficient of gradation, Cc and coefficient of uniformity, Cu

Interpretation of PSD analysis in form of grading curve is useful for soil description. There are two types of coefficient related to PSD which are uniformity coefficient, Cu and coefficient of curvature, Cc. Uniformity coefficient explained the ratio of two characteristic size, Cu = 1 indicates all particles are same size whereas larger Cu indicates that the size distribution is wider and vice versa. Coefficient of curvature indicates that soil is in well-graded formation if the Cc is between 1 and 3 with Cu more than or equal to 4 for gravel and Cu more than or equal to 6 for sand.

From the result, the particles size was plotted into a graph of percentage finer versus particle size. Next, Cc and Cu were calculated by using Equations 3.5 and 3.6 for gradation purpose:

(D30)2 C = (3.5) c (D60 x D10)

D60 C = (3.6) u D10

where:

D10 = diameter corresponding to 10% finer

D30 = diameter corresponding to 30% finer

D60 = diameter corresponding to 60% finer

The size of particle may vary over a wide range regardless being in the same area. However, it is still helpful to gain some glimpses on the soil condition in the area as it may influence the soil properties and thus may affects the slope. Table 3.4 is the summary of soil classification for PSD method by Santamarina et al. (2001) according to ASTM standard procedure.

Table 3.4: Summary of soil classification (Santamarina et al., 2001).

Gravel: Less than 5% Cu > 4, 1 ≤ Cc ≤ 3 GW More than 50% fines Not satisfying GW GP coarse fraction Below ‘A’ line GM Coarse: retained on More than 12% More than sieve #4 fines Above ‘A’ line GC 50% retained Sand: Less than 5% Cu > 6, 1 ≤ Cc ≤ 3 SW sieve #200 Less than 50% fines Not satisfying SW SP course fraction retained on More than 12% Below ‘A’ line SM sieve #4 fines Above ‘A’ line SC

Continuation of Table 3.4

ML LL < 50 CL Fine: OL Less than MH 50% retained sieve #200 LL > 50 CH

Highly Pt organic soils G = Gravel S = Sand C = Clay M = Silt O = Organic Pt = Peat W = well graded P = poorly graded H = high plasticity L = low plasticity

3.3.3(c) Particle size statistics of mean and sorting

Particle size statistics were represented in phi units, ϕ which is the logarithmic transformation of millimetre into whole integers according to Equation 3.7. The conversion is as per Table 3.5.

ϕ = -log2 d (3.7) where d = particle diameter in millimetre

Table 3.5: Conversion from millimetre to phi unit (Folk and Ward, 1957). Sieve No Opening diameter (mm) Phi units, ϕ Classification 4 4.75 -2 Gravel 10 2 -1 20 0.85 0.25 40 0.425 1.25 Sand 60 0.25 2.0 100 0.18 2.75 200 0.075 3.75 pan 0 > 3.75 Silt/clay

In this research, mean and sorting of the particle were calculated for analysis.

By definition, mean is the average particle size whereas sorting is method to measure the particle size distribution from a particle size cumulative graph. Equation 3.8 and

3.9 was introduced by Folk and Ward (1957) were most used in calculating mean and sorting.

ϕ16+ ϕ50+ ϕ84 mean = (3.8) 3

ϕ84-ϕ16 ϕ95-ϕ5 sorting = + (3.9) 4 6.6

where:

ϕ5 = phi values at 5 %

ϕ16 = phi values at 16 %

ϕ50 = phi values at 50 %

ϕ84 = phi values at 84 %

ϕ95 = phi values at 95 %

The purpose of calculating the sorting is to determine the type of sorting hold by the sample from the study areas and to understand the soil condition and per described in

Table 3.6.

Table 3.6: Values of sorting and the interpretation (Folk and Ward, 1957). Values in phi unit (ϕ) Interpretation 0.00 – 0.35 Very well sorted 0.35 – 0.50 Well sorted 0.50 – 0.71 Moderately well sorted 0.71 – 1.00 Moderately sorted 1.00 – 2.00 Poorly sorted 2.00 – 4.00 Very poorly sorted > 4.00 Extremely poorly sorted

3.4 Geophysical methods

Two geophysical methods were applied in this research which were 2D resistivity and induced polarization methods. Geophysical methods cannot stand alone thus the correlation from both methods were essential to achieve more accurate results.

3.4.1 2D resistivity method

One of the most convenient geophysical method used for shallow subsurface is the electrical resistivity method (Telford et al., 1990). Basically, this method measures the electrical potentials between an electrode pair called the potential electrodes (P1 and P2) while transmitting a direct current between another pair of electrodes called the current electrodes (C1 and C2) as shown in Figure 3.13. For the last twenty years, geophysical imaging has been developed and resistivity survey is the most applied geophysical method as it provides continuous information of the studied body. They also able to produce 2D and 3D images of the studied body based on resistivity values. Electrical tomography has become a new standard geophysical imaging technique due to its simplicity (Jongman and Garambois, 2007).

C1 P1 P2 C2

Figure 3.13: The position of current electrodes (C1 and C2) and potential electrodes (P1 and P2) (Loke, 2001).

There are many choices of electrode configuration that can be apply for 2D resistivity method (Figure 3.14). In order to ensure the reliability and success of resistivity method, the choice of electrode configuration must be design carefully depending the targets condition and objectives of survey such as the desired penetration depth, vertical and lateral resolution and ambient electrical noise (Jongman and Garambois, 2007).

C1 P1 P2 C2

Figure 3.15: Electrode configurations of 2D resistivity method (Loke, 2001).

Electrode configuration used in this research was Wenner-Schlumberger array, a hybrid array of Wenner and Schlumberger arrays emerged out of relatively recent work with electrical imaging surveys (Figure 3.15). This array is moderately sensitive to both horizontal (for low ‘n’ values) and vertical (for high ‘n’ values).

Therefore, in area at which exist vertical and horizontal geological structure, this array may come in handy as this array can be considered as a good compromise between the

Wenner and the Dipole-dipole array. The median depth of investigation is about 10% larger than the Wenner array for the same distance between the current electrodes (C1 and C2) for those have greater ‘n’ values than 3. The signal strength is higher than

Dipole-dipole array and twice of the Pole-dipole array but weaker that the Wenner array (Loke, 2001).

Figure 3.14: Wenner-Schlumberger electrode arrangement. The ‘n’ factor is the dipole separation factor (Loke, 2001).

3.4.2 Induced polarization (IP) method

The resistivity method is capable to differentiate between an unstable weathered material and stable bedrock due to the distinction in the material properties.

Unfortunately, these properties are difficult to differentiate between water saturated sediments, clays or graphite by using only the conventional resistivity method as they are characterized by the same range of resistivity. IP method is another electrical imaging method that can be apply in cases as those. The IP method may differ between clays, metallic ore or water saturated sediments due to the distinction in properties

(Keller and Frischknecht, 1966; Telford et al., 1996). However, the combination of both methods in slope stability studies is yet to be well-known (Marescot et al., 2008).

By definition, time domain polarization measures the slow decay of voltage in the ground after a cessation of an excitation current pulse (Sumner, 1976). In short, the induced polarization method reflects the degree of subsurface ability to store electric charge, analogous as a leaky capacitor. If the current was interrupted as the electric current passes through a rock or soil thus, a difference in potential which decay with time is observed. The rate of decay of this potential is depends on the lithology of the rock, its pore geometry and degree of water saturation (Kiberu, 2002). By means, high porosity and conductivity will decrease the induced polarization effect as both lead to a short circuit of energizing current through unblock paths (Griffiths and King, 1965).

3.4.3 Electrical properties of materials

Geophysical methods results are quantitative in term of mineral properties measurements. As an example, this research is using the electrical methods which is

2D resistivity and IP methods. Electrical surveys are capable of determining the subsurface anomalies or rock formation based on the ground surface measurement.

However, these ground measurements are related to various parameter such as fluid content, porosity and degree of water saturation in the rock. The values are also highly dependent on the degree of fracturing and weathering (Loke, 2001).

The variation in resistivity value of certain rocks are enormous that the values are overlapped between different rocks (Telford, 1990). Thus, it is very important to study the geology and geomorphology of the targeted area. The resistivity values

(Table 3.7) and chargeability values (Table 3.8) are taken from Telford (1990) as a reference in interpretation. Some of the value obtained may not be exactly the same as in the table due to the Malaysia’s complex geology as Malaysia located in tropical region.

Table 3.7: Resistivity of various rock and sediment (Telford, 1990). Material Resistivity (Ωm) Granite porphyry 4.5 x 103 (wet) – 1.3 x 106 (dry) Quartzite 10 – 2 x 108 Quartz diorite 2 x 104 – 2 x 106 (wet) – 1.8 x 105 (dry) Sandstone 1 – 6.4 x 108 Limestone 50 – 107 Andesite 4.5 x 104 (wet) – 1.7 x 102 (dry) Clay 1 – 100

Table 3.8: Chargeability of various material (Telford, 1990). Mineral/ material Chargeability (m/s) Granite 10-50 Quartzite 5-12 Gravel 3-9 Groundwater 0 Limestone/dolomite 10-20 Sandstone/siltstone 100-500

3.4.4 Data acquisition

Both 2D resistivity and IP method use the same equipment (Figure 3.16).

ABEM SAS4000 Terrameter is capable in measuring resistivity values in term of ohm meter (Ωm) and chargeability in term of millisecond (ms). The Terrameter is

55 connected to ABEM ES10-64C electrode selector that acts by selecting the current and potential electrode pairs based on the array configuration used. Two Multi Lund cables with length of 100 m each are connected to the electrode selector. The cables are connected to the Terrameter and 41 electrodes are placed on ground and clipped using jumper cable at every takeout (Figure 3.17). Spacing between electrodes are varies depending on the desired depth of investigation. By default, the maximum spacing between the electrodes is 5 m and the minimum is 0.5 m. Therefore, for 200 m survey line, 5 m electrode spacing was applied while for 100 m survey line, 2.5 m electrode spacing was used. All survey lines were conducted by using two Multi Lund cable.

In handling equipment, there are always precaution steps that need to be taken to ensure the equipment are in good condition and produce great results. The most important precaution step is not conducting resistivity and IP methods during thunder and lightning as these weather conditions may affect the result of resistivity and IP due to injection of current into the ground. Another important precaution step is to ensure the electrodes are planted firmly, so that it will have solid connection to the ground.

Figure 3.16: 2D resistivity and IP methods equipment.

Table 3.9: Name of equipment as labelled in Figure 3.16. No Equipment No Equipment 1 100 meter Multi Lund Cable 6 Stainless Steel Electrode 2 12V Battery 7 Jumper cable 3 ABEM ES 10 — 64C Electrode Selector 8 50 meter Measuring Tape 4 ABEM SAS4000 Terrameter 9 Hammer 5 Connecting Cable

Figure 3.17: 2D resistivity method setup on field.

3.4.5 Data processing

The result for 2D resistivity and IP methods are produced simultaneously using the same software. After finished with data acquisition in the field, the data was transferred from ABEM Terrameter for processing purposes. At the terrameter, the mode was set to RS232 Communication and connected to the computer by using a transfer cable. A program called ABEM SAS100/4000 Utilities was used for importing the data and converting it into .DAT format file. This format is needed for inversion step by using the Res2DInv program by Geotomo Software.

Res2DInv is a program to determine 2D resistivity model for the subsurface.

This inversion program uses non-linear least-square optimisation technique for inversion routine and forward modelling subroutine for calculating the apparent resistivity values. Aside from inverse apparent resistivity values, Res2DInv is also capable to inverse the chargeability of the subsurface.

In the software, the .DAT format file was read and 1st electrode position was changed to 0 because default setting of Res2DInv will assign the centre of profile as 0.

Next, an apparent resistivity profile was produced which also known as pseudosection.

In order to obtain true resistivity, an inversion is needed. After applying the least – square inversion and data conversion constraint for several iterations, three profiles were produced which were calculated apparent resistivity/chargeability, measured apparent resistivity/chargeability and inversion of resistivity/chargeability model with lowest root mean square (RMS) obtained. RMS error described as the misfit between the measured apparent resistivity/chargeability and calculated apparent resistivity/chargeability. The RMS error can be further reduced by removing all bad datum points or reducing the error in RMS error distribution bar chart. In order to include the topography to the inversion model, the .DAT format file was modified by adding the recorded distances and elevations.

Software from Surfer by Golden Software was use for editing and contouring purpose. Files with extension .xyz and .srf were obtained from the Res2DInv were gridded and plotted in the Surfer software and edited for interpretation. The data acquired from each site was processed and then interpreted accordingly.

3.5 Monitoring the study area

Slope monitoring is a common practice in geophysical survey where the survey will be conducted for a certain period of time. This aims to detect any changes within the subsurface or the material in the subsurface within that period of time. In dealing with such engineering and environmental issue such as slope failure, slope monitoring technique is very useful.

2D resistivity method and laboratory soil analyses were repeated starting from

August 2016 until January 2017. This period is chosen for monitoring purpose due to observation conducted by Department of Meteorological Malaysia during year 1951

58 to 1980 deducted that wettest period was during months of August to November and based from rainfall distribution in year 2014 to 2016 indicated that highest monthly rainfall was during September 2014, November 2015 and October 2016. The results acquired from each survey month were compared according to area to detect any changes within the subsurface and to conclude any potential slope failure in that particular areas.

3.6 Rainfall distribution

Rainfall distribution is one of the factor that affect the slope stability and thus, it is taken into consideration in this research. Table 3.10 shows the categories of precipitation and their description based on Department of Meteorological Malaysia,

(2018).

Table 3.10: Categories of precipitation and the description (Department of Meteorological Malaysia, 2018). Category Description Slight rain Rate of fall less than 0.5 mm per hour Moderate rain Rate of fall 0.5 mm to 4 mm per hour Heavy rain Rate of fall more than 4 mm per hour Slight showers Rate of fall less than 2 mm per hour Moderate showers Rate of fall 2 mm to 10 mm per hour Heavy showers Rate of fall 10 mm to 50 mm per hour Violent showers Rate of fall more than 50 mm per hour

Rainfall distribution data was obtained from Department of Meteorological

Malaysia and Department of Irrigation and Drainage Malaysia (Appendix B). There are several rainfall stations available in Penang Island and the nearest station to the study areas was selected for rainfall distribution observation in 2D resistivity monitoring.

There are many study concerning cumulative and daily rainfall used for slope stability analysis (Vennari et al., 2014; Huang et al., 2015). Rahardjo et al. (2001) also

59 mentioned that daily rainfall and cumulative rainfall both act as the triggering factor for slope failure. In this research, three types of rainfall distribution were chosen; cumulative rainfall of seven days including the survey day, rainfall distribution on 6th day (a day before survey) and rainfall distribution on 7th day (on the survey day). This aims to correlate the changes in 2D resistivity monitoring and the soil moisture content with the rainfall distribution.

3.7 Regression analysis

Regression analysis was applied to calculate an empirical correlation between geophysical results, laboratory test results of soil porosity and moisture content and also rainfall distribution. The correlation coefficient, R represents the best fit between the variables and indicates the strength of relationship between them. The value R lies between -1 < R < +1. The sign indicates the positive and negative correlation whereas the magnitude represents the correlation strength (Evan, 1996; Beldjazia and Alatou,

2016). The correlation coefficient, R can be squared to obtain correlation of determination, R2 which indicates the goodness of a fit of a model. This indicates how well the regression line if compare to the data points (Table 3.11).

Table 3.11: Strength of correlation (Evan, 1996). Correlation of Category Correlation coefficient, R determination, R2 Very weak 0.00 – 0.19 0.00 – 0.0361 Weak 0.20 – 0.39 0.04 – 0.1521 Moderate 0.40 – 0.59 0.16 – 0.3481 Strong 0.60 – 0.79 0.36 – 0.6241 Very Strong 0.80 – 1.00 0.64 – 1.0000

3.8 Summary

This chapter discussed about the methodology that has been executed in order to achieve the desired results. Starting from the geology of the Penang Island followed by the data acquisition and processing for geophysical methods of 2D resistivity and

IP methods and finally, the procedure and equations used in particle size distribution analysis and laboratory soil analysis (Figure 3.18). The chapter aims to show all the steps taken with detail elaboration during conducting this research.

Desk study • Study geology and geomorphology of Penang Island • Survey locations that favour landslide occurrence as study areas • Planning geophysical methods suitable for completing the research

Data acquisition

2D resistivity and induced Soil sampling polarization methods • Collect soil samples along the • Wenner – Schlumberger array survey lines • Survey line perpendicular to the slope • 2D resistivity monitoring within August 2016 – January 2017 month

Measurement of Data processing physical parameters

RES2DINV Laboratory tests

• Produced resistivity and • Measurement of soil moisture chargeability model inversion content, soil porosity, bulk density • Conduct PSD analysis on the soil samples

Data interpretation and empirical correlation of results

• Geophysical results correlated with laboratory tests results • Results correlated with rainfall distribution

Data presentation and thesis writing

Figure 3.18: Methodology flowchart for research.

CHAPTER 4

RESULTS AND DISCUSSION

4.0 Introduction

This chapter presents the results of the slope study conducted at three study areas in Penang Island. The research consists of combination between geophysical methods; 2D resistivity and IP method as well as geotechnical engineering methods;

PSD analysis and laboratory tests.

In order to complete the research, 2D resistivity and IP methods are conducted at highly suspected area for slope failure in Penang Island based on the previous studies and reported slope failures. Monitoring the study area by using 2D resistivity method was conducted within month of August 2016 to January 2017.

Aside from geophysical methods, laboratory tests were also conducted to provide additional information about the condition of the soil which led to the understanding of the subsurface at the study areas. Water content, porosity and bulk density of the soil are considered due to their influence towards slope failure probability.

4.1 Results

This research included several methods to study slope from laboratory tests to geophysical methods as mentioned in previous chapter. Therefore, this chapter will be explaining the results according to the study area and method used so that the flow of slope study is clear.

4.1.1 Sungai Batu

A 200 meter survey line was conducted perpendicular to the slope behind SK

Sungai Batu. 2D resistivity and IP method and slope monitoring using 2D resistivity method starting August 2016 – January 2017 were carried out. However, there is no

2D resistivity monitoring conducted in October 2016 due to equipment malfunction.

The soil samples were also obtained along the survey line to determine the soil moisture content, porosity and bulk density.

4.1.1(a) Laboratory test

Several soil laboratory tests were conducted and the results of average of moisture content, w, average porosity, ƞ, and average bulk density, ρbulk of Sungai Batu are presented in Table 4.1.

Table 4.1 shows the moisture content and porosity of the soil at this area in range of 14.65 – 25.35 % and 50.78 – 54.35 % respectively. Based on soil porosity by

Faur and Szabo (2011), soil at Sungai Batu can be classified as loose soil. The bulk density and particle’s density in range of 0.86 – 1.14 g/cm3 and 1.80 – 2.50 g/cm3 respectively. According to bulk density values that were arranged by Arshad et al.

(1996), the soil is ideal for plant growth and not compacted which allowed for easy water movement.

Table 4.1: Laboratory test for Sungai Batu study area.

Distance Moisture content, w Porosity, ƞ Bulk density, ρbulk (m) (%) (%) (g/cm3) 70 - 75 15.478 54.35 1.14 95 - 100 25.346 52.45 0.86 100 14.645 50.78 1.10 100 - 105 18.730 51.86 1.09 180 - 185 19.418 51.19 0.99 195 - 200 18.77 51.97 0.99

4.1.1(b) Particle size distribution (PSD) analysis

There were four soil samples taken from the study area along the survey line as representative to understand the soil condition at the study area and the PSD analysis results are presented in Table 4.2.

The distribution percentage of gravel, sand and clay/silt found to be varies. At distance 70 – 75 m, the composition of gravel, sand and clay/silt were 8.67%, 90.27% and 1.06% respectively. The Cu calculated was 1.45 and Cc was 8.00. Based on these values, the soil can be concluded as well-graded sand. At distance 95 – 100 m, the composition of gravel, sand and clay/silt were 25.25%, 74.21% and 0.54% respectively while at distance 100 – 105 m, the values were 14.67%, 84.42% and 0.91% respectively. However, the coefficients between them were greatly differ where Cc =

0.987 and Cu = 5.917 at distance 95 – 100 m and Cc = 2.82 and Cu = 6.00 at distance

100 – 105 m. These showed the gradation of soil at both locations was not the same where distance 95 – 100 m can be marked as poorly graded sand while distance 100 –

105 m was marked as well-graded sand. At distance 100 m, the percentage of gravel, sand and clay/silt were 4.82%, 93.03% and 2.15% respectively. The uniformity coefficient, Cc = 1.19 and curvature coefficient, Cu = 6.86 thus, the soil can be concluded as well – graded sand. As conclusion, the soil at Sungai Batu study area can be deduced as gravelly sand. Hashim et al. (2015) studied that most of the slope failures occurred in gravelly silt, therefore, can be related to Sungai Batu soil type which is gravelly type of soil.

Table 4.2: Particle size distribution analysis result at Sungai Batu study area.

Soil Distance (m) Composition Percentage (%) Coefficient Classification Gravel 8.67 Cc = 1.45 70 - 75 Sand 90.27 Well graded Cu = 8.00 Clay/silt 1.06

Continuation of Table 4.2

Gravel 25.25 Cc = 0.987 95 - 100 Sand 74.21 Poorly graded Cu = 5.917 Clay/silt 0.54 Gravel 4.82 Cc = 1.19 100 Sand 93.03 Well graded Cu = 6.86 Clay/silt 2.15 Gravel 14.67 Cc = 2.82 100 -105 Sand 84.42 Well graded Cc = 6.00 Clay/silt 0.91

The mean and sorting value of soil in Sungai Batu study area is presented in term of phi unit in Table 4.3. The average particle size for sample at distance 70 - 75 m was - 0.410 and the sorting was 1.269. This sorting can be classified as poorly sorted based on table of sorting by Folk and Ward (1957). The mean and sorting at distance

95 -100 m were - 0.380 and 0.315 respectively. Thus, the sample here was very well sorted. Next, sample at distance 100 m obtained mean value of - 0.333 and sorting of

0.663 which can be classified as the moderately well sorted. Sample at distance 100 –

105 m obtained the mean value of - 0.837 and sorting of 1.039, thus can be classified as poorly sorted. Therefore, the soil at Sungai Batu study area was range between poorly sorted to very well sorted and the mean lies within between -0.033 to – 0.837 indicated that the mean of the soil is very coarse sand.

Table 4.3: Mean and standard deviation of Sungai Batu soil samples.

Phi value Distance Mean Sorting Classification ɸ5 ɸ16 ɸ50 ɸ84 ɸ95 70 – 75 0 -1.96 -0.6 1.33 2.95 -0.410 1.269 Poorly sorted 95 -100 0 0 -1.35 0.21 1.73 -0.380 0.315 Very well sorted 100 0 0 -1.05 0.95 2.81 -0.033 0.663 Moderately well sorted 100 – Poorly sorted 0 -2.2 -1 0.69 2.09 -0.837 1.039 105

4.1.1(c) Rainfall distribution

The rainfall distribution is taken from the nearest Department of

Meteorological Malaysia rainfall station located at Bayan Lepas (5°17’49” N,

100°16’20” E) as shown in Figure 4.1.

Based on the cumulative rainfall of seven days including the survey day (Figure

4.1a), the rainfall was highest in October 2016 with 139.2 mm of total cumulative rainfall and next, November 2016 recorded 115.2 mm of total cumulative rainfall. This indicated that the cumulative rainfall was high during month of October 2016 and

November 2016.

Based on the rainfall distribution of 6th day, October 2016 recorded highest rainfall distribution with 11.8 mm and followed by January 2017 with 15.8 mm whereas rainfall distribution on 7th day, September 2016 shows highest rainfall which is 18.8 mm (Figure 4.1b).

According to the category of precipitation by Department of Meteorological

Malaysia, these intensities of rainfall can be classified as moderate rain as the rate of rainfall is between 0.5 to 4 mm per hour.

High moisture content is recorded when the rainfall distribution is high. The moisture content is high in October 2016 due to the high rainfall distribution in the period and the moisture content decreased when the rainfall distribution decreased.

The rainfall distribution on 6th day and 7th day also affected the moisture content as the value is slighly higher when there was rainfall on the 6th and 7th day as shown in

January 2017 and September 2016 respectively.

a) 160 30

140 25 120 20 100

80 15

60 10

40 Moisture content (%)

Rainfall distribution (mm) 5 20

0 0 August September October November December January Month

Cumulative rainfall Moisture content

20 30 18 25 16 b) 14 20 12 10 15 8 10

6 Moisture Moisture content (%)

Rainfall Rainfall distribution (mm) 4 5 2 0 0 August September October November December January Month Rainfall on 6th day Rainfall on 7th day Moisture content

Figure 4.1: Rainfall distribution at Sungai Batu. (a) Seven days cumulative rainfall (b) Rainfall distribution on 6th day and 7th day.

Increase in rainfall distribution will increase the probability of slope failure as the water will infiltrated and saturated the subsurface therefore increases the weight.

Unstable overburden soil prone to slide down due to increase in total soil weight and pore pressure within the subsurface thus, reducing the shear strength (Liu and Li, 2015;

Ismail et al, 2017).

4.1.1(d) Geophysical methods

Based on the resistivity inversion model (Figure 4.2a), three resistivity zones are found (Table 4.4). Resistivity value range between < 400 Ωm found at distance

100 – 120 m, interpreted as weak zone. The resistivity with value of 500 – 3000 Ωm found at distance of 40 – 90 m with depth of 5 m and interpreted as weathered granite zone. Resistivity with value 3000 – 20000 Ωm dominated from depth of 10 m and deeper and said to be granite bedrock which is comparable to Jinmin et al, (2013) granite bedrock resistivity value (>3000 Ωm). However, granite bedrock with resistivity value > 10000 Ωm found separated into two at distance 135 – 140 m. Thus, a fracture is detected here.

Table 4.4: Summary of resistivity value with interpretation and feature found at Sungai Batu study area.

Resistivity (Ωm) Interpretation < 400 Weak zone at distance 100 – 120 m with depth 5 – 10 m Weathered granite covered from distance 40 – 90 m with depth of 500 – 3000 5 m 3000 – 20000 Granitic bedrock at depth 10 – 20 m Feature Description Fracture The bedrock with value of > 10 000 Ωm is break into two

Based on the induced polarization inversion model (Figure 4.2b), there are three chargeability zones (Table 4.5). Chargeability range with value 10 – 30 ms is interpreted as fresh granite. Chargeability value of 3 – 10 ms at distance 30 – 80 m is interpreted as gravel based on the chargeability value of gravel by Telford et al. (1990) while at distance 100 m, is interpreted as clayey sand due to having low resistivity but high chargeability characteristic. Chargeability value ranges from < 3 ms can be seen almost everywhere. At distance > 100 m, low chargeability value is display due to the existence of fracture as interpreted in the resistivity section. While at distance 0 – 100 m, the same chargeability range can be found below the gravel area due to the

69 condition of the study area that located at highly jointed and fractured area based on

Ong (1993). Telford et al. (1990) stated that the polarization effect is affected by the type and concentration of rocks. Therefore, polarization effect will decrease as the rock increase in porosity due to more paths for the current to flow. The same principle is applied toward fractured and jointed rock which is porous thus, the chargeability is expected to be lower.

Table 4.5: Summary of chargeability value with interpretation at Sungai Batu study area.

Chargeability (ms) Interpretation Cause by fracture at location 135 – 140 m and highly jointed < 3 and fractured of subsurface condition Gravel is detected at distance 30 – 80 whilst clayey material is 3 – 10 identified at 100 m distance (low resistivity, high chargeability) 10 – 30 Fresh granite

The potential of slope failure in this study area is the existing of weak zone.

According to Bachmann et al, (2004), weak zone corresponds to highly fractured and weathered zone where the strength is reduced. Ronning et al, (2014) also mentioned that weak zone with resistivity value < 500 Ωm indicates unstable rock with fractures filled by clay. As this weak zone consists of highly fractures and clayey material, thus, whenever heavy rainfall happened, the rainwater infiltrated subsurface easily and weak zone will be saturated and become overload. The subsurface will not be able to sustain the weight and cause it to slide down.

70 b)

Figure 4.2: Inversion model of (a) 2D resistivity and (b) induced polarization method at Sungai Batu study area.

4.1.1(e) Slope Monitoring

Slope monitoring started in August 2016 and ended in January 2017. Based on the result in August 2016 (Figure 4.3), three resistivity zones are detected at the study area. Resistivity indicated by value of less than 400 Ωm is interpreted as weak zone detected at distance of 100 – 120 m with depth of 10 m. During rainy days, running water can be seen running down at this location. This location was coincided with the location for previous landslide occurred few months back at distance 110 – 140 m.

Resistivity zone is between 500 – 3000 Ωm interpreted as weathered granite can be detected at distance of 40 – 90 m with depth of 5 m. Granitic bedrock is indicated by resistivity with value > 3000 Ωm detected at depth of > 10 m along the survey line. No obvious signature of faulting or fractured is detected aside from weak zone which consists of highly fractured area.

Figure 4.3: 2D resistivity inversion model for August 2016 at Sungai Batu.

Based on the resistivity inversion model of September 2016 (Figure 4.4), weak zone recorded lower resistivity value compared with previous result. The same phenomenon happens to the weathered granite which lower resistivity value is detected within the same location. This indicates changes within the subsurface. A fracture is inferred starting to develop at distance of 130 – 140 m.

Figure 4.4: 2D resistivity inversion model for September 2016 at Sungai Batu.

Using the same resistivity range, the resistivity inversion model of November

2016 (Figure 4.5) shows further changes detected within the subsurface. Based on the resistivity value, the weak zone recorded lower resistivity value than previous month.

Weathered granite also recorded lower resistivity values than before. This shows that the changes are significant within the subsurface. Fractured detected at distance 130 –

140 m become obvious as the resistivity value > 10000 Ωm is clearly separated into two.

Figure 4.5: 2D resistivity inversion model for November 2016 at Sungai Batu.

The resistivity inversion model for December 2016 (Figure 4.6) indicated that the resistivity value at weak zone increases from previous months which indicates inconsistency in resistivity value. Fortunately, the rainfall distribution is considered in this research and rainfall during this month is lower from previous months. The weak zone which consists of joints and fractures dried and thus leaving the resistivity value to be slightly higher than the value during rainy season. The weathered zone shows consistent resistivity value which the values did not change very much. This indicated that this area weathering is extremely affected by the rainfall and still affecting the weathered zone although the rainfall distribution already decreased.

Figure 4.6: 2D resistivity inversion model for December 2016 at Sungai Batu.

The rainfall distribution did affect the resistivity values in January 2017 as

Figure 4.7 shows that the resistivity value was lower than previous month. Rainfall distribution causes the weak zone’s joints and fractures to be saturated. Granitic bedrock shows consistent feature from November 2016 where a fracture was determined at distance 130 – 140 m. Result shows that the fracture is wider and exposing resistivity value < 5000 Ωm. This indicated significant changes in this fracture.

Figure 4.7: 2D resistivity inversion model for January 2017 at Sungai Batu.

4.1.1(f) Changes within Sungai Batu study area subsurface

Within the subsurface of Sungai Batu survey line, three features are monitored throughout the whole monitoring period.

The first feature monitored is weathered granite zone with resistivity value ranged between 500 – 3000 Ωm found at > 10 m depth (Figure 4.8). Main concern in this area is at distance 30 – 80 m where the changes on the weathered granite can clearly been observed. The most significant changes can be seen from the resistivity values.

From Figure 4.9, the resistivity values are higher in August 2016 which is between 700 – 1000 Ωm. In the following months, the resistivity values are decreased to 650 – 800 Ωm (shaded region). This is affected by the rainfall distribution where the rainfall was high in September 2016 to November 2016. The rainfall triggered the weathering to take effect on the subsurface and lowers the resistivity values. Although rainfall was lower in December 2016 but weathering effect is still progressing within the subsurface which resulting the resistivity values remains low. However, the resistivity values are slightly higher if compared to September 2016 and November

2016 due to decreased in rainfall during December 2016. Increased resistivity values in January 2017 shows that the subsurface experienced changes due to rainfall.

Figure 4.8: Sungai Batu weathered granite zone changes throughout the monitoring period.

1400

1200 Weathered granite zone

1000 m) Ω 800

600

Resistivity Resistivity ( 400

200

0 30 33 36 39 42 45 48 51 54 56 59 62 65 68 71 74 77 80 Distance August September November December January

Figure 4.9: Monitoring resistivity values at distance 30 – 80 m at Sungai Batu study area. The second monitored feature is the weak zone located between 90 – 120 m

(Figure 4.10). Comparing size of the weak zone, it can be said that the size varies throughout the monitoring period. The weak zone is detected at distance 90 – 110 m with size 20 m on August 2016 but later detected at distance 95 – 115 m in September

2016 also with 20 m size. This position remained the same until November 2016. In

December 2016, the weak zone size reduced to 15 m at distance 95 – 110 m but later in January 2017, it is increased to 25 m at distance 90 – 115 m. One of the factor that affect the weak zone size is due to the rainfall distribution where the rainfall was high from September 2016 to November 2016. The rainfall distribution decreased in

December 2016 reduces the size of weak zone in this month but later rainfall distribution increased in January 2017 which resulting in the weak zone size to increase.

Aside from the distance, the rainfall distribution also affected the resistivity values. The weak zone is found deeper during September 2016 and November 2016.

This is due to the infiltrated rainwater into the ground which allowed the current to penetrate into deeper depth. Lower resistivity values at the weak zone were found in

September 2016, November 2016 and January 2017 where the rainfall distribution was higher. Based on Figure 4.11, the resistivity values are in decreasing trend (shaded region) from August 2016 to January 2017 except in December 2016 where the resistivity value is slightly higher.

Figure 4.10: Sungai Batu weak zone changes during the monitoring period.

1600

1400

1200

m) 1000 Ω

800 Weak zone

600 Resistivity Resistivity ( 400

200

0 90 93 96 99 102 105 108 111 114 117 120 Distance (m) August September November December January

Figure 4.11: Monitoring resistivity values at distance 90 – 120 m at Sungai Batu study area. Another feature monitored throughout the monitoring period is the granitic bedrock at distance 100 – 160 m (Figure 4.12). The changes of the fracture developed at distance 130 – 140 m is observed through the resistivity values where the granitic bedrock resistivity value is > 3000 Ωm.

Based on Figure 4.13, the fresh granitic bedrock with resistivity value > 10000

Ωm is found at distance 130 – 140 m in August 2016. However, in September 2016, the fresh granite is found at distance 120 m. Starting September 2016, the fresh granite is thinner at distance 130 – 140 m compare to others and therefore is inferred to be a fracture. In November 2016, the inference is proved where a fracture can be observed at distance 130 – 140 m (shaded region). This can be observed via resistivity values where the value is dropped from 10000 – 12000 Ωm in September 2016 to 6000 –

8000 Ωm in November 2016. This indicated that the subsurface undergoes changes since September 2016. The same condition can be observed in January 2017 with

79 wider separation although it does not appear in December 2016. As conclusion, a fracture was found developed at distance 130 – 140 m.

Figure 4.12: Sungai Batu granitic bedrock changes throughout the monitoring period.

18000 16000 Fractured zone 14000

m) 12000 Ω 10000 8000

6000 Resistivity Resistivity ( 4000 2000 0 110.0 112.9 115.7 118.6 121.4 124.3 127.1 130.0 132.9 135.7 138.6 141.4 144.3 147.1 150.0 Distance (m) August September November December January

Figure 4.13: Monitoring resistivity values at distance 110 – 150 m at Sungai Batu study area. 4.1.2 Bukit Relau

A 200 meter survey line was conducted perpendicular to the slope using 2D resistivity and IP methods. 2D resistivity also acts as monthly slope monitoring method starting from

September 2016 – January 2017 and the soil samples were taken along this survey line to determine the soil moisture content, porosity and bulk density. There is no 2D resistivity monitoring conducted in October 2016 due to equipment malfunction.

4.1.2(a) Laboratory test

The average of moisture content, w, average porosity, ƞ, and average bulk density,

ρbulk of Bukit Relau study area are presented in Table 4.6.

Based on the results, the moisture content and porosity of the soil at this area were in range of 21.08 – 26.31 % and 47.86 – 58.52 % respectively. From Faur and

Szabo (2011) soil porosity classification, soil at Bukit Relau can be classified as loose soil. The bulk density and particle’s density were in range of 0.95 – 1.09 g/cm3 and

2.16 – 2.47 g/cm3 respectively. Based on the bulk density, the soil is ideal for plant growth and not compacted which allowed for easy water movement.

Table 4.6: Laboratory test for Bukit Relau study area.

Distance Moisture content, w Porosity, ƞ Bulk density, 3 (m) (%) (%) ρbulk (g/cm ) 50 24.72 58.52 1.00 100 26.31 53.47 1.07 140 – 145 24.30 55.72 0.95 185 – 190 21.08 47.86 1.09

4.3.2(b) Particle size distribution (PSD) analysis

At this study area, four soil samples were taken at along the survey line for

PSD analysis as representative to understand the soil condition at the study area.

Based on Table 4.7, the distribution percentage of gravel, sand and clay/silt were consistent along the survey line. At distance 50 m, the composition of gravel, sand and clay/silt were 22.26%, 77.06% and 0.68% respectively. The Cu was 0.793 and Cc was 6.10. The soil condition is almost similar to distance 185 – 190 m which has composition of gravel = 22.77%, sand = 76.26% and clay/silt = 0.94% with Cu =

0.552 and Cc = 3.448. This marked that both locations fall under the same group of poorly-graded soil. This is different from distance 100 m where the composition of gravel, sand and clay/silt were 23.45%, 75.06% and 1.50% respectively with calculated Cu = 1.32 and Cc = 10.17 indicating that this location is well-graded soil. At distance 140 – 145 m, the composition of gravel, sand and clay/silt were 42.07%,

56.91% and 1.015% respectively with Cu and Cc both were nil indicated that the soil is poorly graded sand.

As conclusion, the soil at Bukit Relau study area can be deduced as gravelly sand based on the particle size distribution. A study by Hashim et al. (2015) found out that most of the slope failures occurred in gravelly silt, therefore, can be related to

Bukit Relau soil type which is gravelly type of soil.

Table 4.7: Particle size distribution analysis result at Bukit Relau study area.

Percentage Soil Distance (m) Composition Coefficient (%) classification Gravel 22.26 Cc = 0.793 50 Sand 77.06 Poorly graded Cu = 6.100 Clay/silt 0.68 Gravel 23.45 Cc = 1.320 100 Sand 75.06 Well graded Cu = 10.17 Clay/silt 1.50 Gravel 42.07 Cc = 0 140 - 145 Sand 56.91 Poorly graded Cu = 0 Clay/silt 1.015 Gravel 22.77 Cc = 0.552 185 - 190 Sand 76.26 Poorly graded Cu = 3.448 Clay/silt 0.94

The mean and sorting of Bukit Relau were calculated in term of phi unit. Based on Table 4.8, the average particle size for sample at distance 50 m was -0.150 and the sorting was 0.458. This sorting can be classified as well sorted based on table of sorting by Folk and Ward (1957). The mean and sorting at distance 100 m were -0.033 and

0.662 respectively. Thus, the sample here was moderately well sorted. Sample at distance 140 – 145 m obtained the mean of -0.577 and sorting of 0.336 which this sample can be classified as very well sorted. Sample at distance 185 – 190 m obtained mean value of -0.167 and sorting of 0.449 which can be classified as well sorted.

Therefore, the soil at Bukit Relau study area can be concluded to be ranged between moderately well sorted to well sorted soil and the mean value indicated that the mean of the particle is very coarse sand.

Table 4.8: Mean and standard deviation of Bukit Relau soil samples.

Phi value Distance Mean Sorting Classification ɸ5 ɸ16 ɸ50 ɸ84 ɸ95 50 0 0 -1.1 0.65 1.95 -0.150 0.458 Well sorted Moderately well 100 0 0 -1.05 0.95 2.8 -0.033 0.662 sorted 140 - Very well sorted 0 0 -1.85 0.12 2.02 -0.577 0.336 145 185 - Well sorted 0 0 -1.07 0.57 2.02 -0.167 0.449 190

4.1.2(c) Rainfall distribution

According to Vennari et al, (2014), there are several general criteria for representative rain gauge to be selected which is the geographic distance should not be more than 12 km and the elevation between the study area and the rain gauge are comparable. Wieczorek and Snyder (2009) also mentioned that the topographic difference between the rainfall station and the study area may cause inconsistency in the result. Due to the location of Bukit Relau which located on top of the hill, Kolam

Takungan Air Hitam (5°23’45” N, 100°15’55” E) rainfall station from Department of

Irrigation and Drainage is selected as it is located not more than 12 km distance from the study area and the elevation are comparable.

Figure 4.14a shows the cumulative rainfall of seven days including the survey day conducted indicates that the cumulative rainfall at Kolam Takungan Air Hitam was the highest during November 2016 with total cumulative rainfall of 284.5 mm and followed by September 2016 and October 2016 with total cumulative rainfall of 168.5 mm and 127 mm respectively.

Based on the rainfall distribution on the 6th day, January 2017 recorded the highest rainfall distribution with 43 mm of rainfall and followed by September 2016 with 25 mm. Rainfall distribution on the 7th day indicated that highest rainfall on

September 2016 with total rainfall of 32 mm (Figure 4.14b).

According to the category of precipitation by Department of Meteorological

Malaysia, these intensities of rainfall can be classified as moderate rain as the rate of rainfall is between 0.5 to 4 mm per hour.

The soil moisture content increased with increasing rainfall distribution as the value increased slightly from October 2016 to November 2016 but decreased in

December 2016 due to lack of rainfall in the period as shown by the cumulative rainfall. The moisture content increased in January 2017 due to rainfall on 6th day was high thus, increased the moisture content value.

a) 300 30

250 25

200 20

150 15

100 10 Moisture Moisture Content (%) Rainfall Rainfall Distribution (mm) 50 5

0 0 August September October November December January Month Cumulative rainfall Moisture content

b) 50 30 45 25 40 35 20 30

25 15 20 10

15 Moisture Moisture Content (%)

Rainfall Distribution (mm) 10 5 5 0 0 August September October November December January Month Rainfall on 6th day Rainfall on 7th day Moisture content

Figure 4.14: Rainfall distribution at Bukit Relau. (a) Seven days cumulative rainfall (b) Rainfall distribution on the 6th day and 7th day.

4.1.2(d) Geophysical methods

Based on the resistivity inversion model (Figure 4.15a), there are two resistivity zones (Table 4.9). The resistivity value 3000 – 10000 Ωm found at distance

70 – 200 m at depth of > 10 m is interpreted as granite bedrock. The same resistivity range was found at distance of 100 m with depth < 5 m and interpreted as boulder.

Resistivity between 750 – 3000 Ωm interpreted as weathered granite zone found at distance 0 – 70 m at depth < 15 m. Another feature that can be found from resistivity inversion model is a fracture at distance 60 m.

Table 4.9: Summary of resistivity value with interpretation and features found at Bukit Relau study area.

Resistivity (Ωm) Interpretation 750 – 3000 Weathered granite cover distance of 0 -70 m with depth < 15 m. 3000 - 10000 Granitic bedrock at depth > 10 m at distance 70 – 200 m Features Description Boulder Found at depth < 5 m at distance 100 m with high resistivity value Fracture Separating the granite into two at distance 60 m

The induced polarization inversion model (Figure 4.15b) shows three chargeability zones (Table 4.10). The chargeability with value 10 – 30 ms is interpreted as granite bedrock found at distance 120 – 160 m with depth > 15 m.

Chargeability value between 3 – 10 ms at distance 0 – 60 m with depth < 10 m is interpreted as gravel. Chargeability range < 3 ms is found approximately at 100 m distance was identified as granite (barren) due to high resistivity but low chargeability characteristic? The same chargeability range was found at distance 60 m. This is happened due to the effect of fractured, thus, allowing the current to flow through and reduces the accumulation of charge here.

Table 4.10: Summary of chargeability value with interpretation and features found at Bukit Relau study area.

Chargeability (ms) Interpretation < 3 Due to the fracture at distance 60 m 3 – 10 Interpreted as gravel 10 - 30 Granitic bedrock at depth > 15 and distance 120 – 160 m Feature Description Having low chargeability despite having high resistivity at distance Barren granite 100 m.

From the results, Bukit Relau study area was underlain by a large weathered granite rock. The value of weathering granite can be overlapped with finding by

Yaccup and Tawnie, (2015) where medium weathered granite has resistivity 1000 –

5000 Ωm and the value for boulders can be overlapped with finding by Muztaza et al,

(2017) where resistivity value for boulder is 1200 – 7000 Ωm. The potential of slope failure is caused by weathering effect as it can produced features such as boulders and fractures. If these features become unstable, phenomena such as landslide or rockfall were highly anticipated. The location of study area which is located within 2 km radius from faults of Penang Island can also be a factor for slope failure to occur.

Figure 4.15: Inversion model of (a) 2D resistivity and (b) induced polarization method at Bukit Relau study area.

4.1.2(e) Slope monitoring

Monitoring at Bukit Relau study area started in September 2016. The resistivity inversion model for September 2016 is shown in Figure 4.16. There are two resistivity zones found. Resistivity value > 3000 Ωm is resistivity zone consists of granite bedrock while 750 – 3000 Ωm is interpreted as weathered granite. The weathered granite can be found everywhere in the subsurface along the survey line with depth of

< 15 m. Resistivity value < 400 Ωm was not found here indicated the absence of low resistivity zone or water saturated zone.

As a whole, the subsurface was a massive granite rock that was weathered and fractured. At distance 60 m, a fracture is detected. Approximately 100 m distance, a boulder with resistivity value equivalent to the granite bedrock is identified. Granite bedrock is indicated by resistivity value > 3000 Ωm found at depth > 10 m at distance

70 – 200 m.

Figure 4.16: 2D resistivity inversion model for September 2016 at Bukit Relau.

Result for November 2016 is shown in Figure 4.17. Granite bedrock shows decreases in resistivity value. These changes can occur due to factors such as moisture content and rainfall distribution. Based on the rainfall distribution and moisture content of Bukit Relau in sub – chapter 4.1.2.(c), rainfall distribution in November 2016 is high thus, soil moisture content is high. Thus, this affect the resistivity value as the rainwater infiltrated the subsurface.

Figure 4.17: 2D resistivity inversion model for November 2016 at Bukit Relau.

In December 2016 (Figure 4.18), resistivity value from material within the fracture has decreased and the size of fracture is increased. Resistivity value of the boulder also decreased from previous month despite the rainfall distribution is not as high as in November 2016. Therefore, this indicated that the subsurface has weathered due to the rainfall. Another fracture was inferred at distance 140 m.

Figure 4.18: 2D resistivity inversion model for December 2016 at Bukit Relau.

Resistivity inversion model of January 2017 (Figure 4.19) recorded the highest

RMS error compare to others which was 9.6. This has affected the inversion model and resulting in the inversion model slightly different from others. However, this was still acceptable as it was below 10% (Martinho et al, 2004; Dimova et al, 2012).

January 2017 inversion has higher resistivity value for boulder and granite bedrock as well as lower resistivity value for the fracture. This occur due to the current flowing path which is the current tends to choose the lower resistance material over higher resistance materials (Valkenburgh et al., 1992). The resistivity at the fracture zone is much lower therefore attracted more current towards it and leaving the bedrock value slightly higher.

Fracture at distance 60 m shows changes as it becomes larger from previous months and a fracture is inferred at distance 140 m. These indicated that the subsurface experienced changes within these several months.

Figure 4.19: 2D resistivity inversion model for January 2017 at Bukit Relau.

4.1.2(f) Changes within Bukit Relau study area subsurface

Within the subsurface of Bukit Relau study area, a feature that was monitored throughout the monitoring period is the fracture at distance 60 m which separated the granite into two parts. Dearman et al, (1978) mentioned that weathering effect is more intense and penetrates to greater depth in fault zones and closely jointed rock, thus, this can be related to the fracture zone. As the fracture appeared to be tilted in the resistivity inversion model, therefore Figure 4.20 covered from 40 – 80 m for better observation. The resistivity value of material within the fracture decreased from 1500

– 2000 Ωm in September 2016 to 700 – 1500 Ωm in January 2017 (Figure 4.21). This indicated that the fracture become weaker with time and affected by rainwater. The rainfall distribution was high in September 2016 – November 2016. Further decrease of resistivity values despite less rainfall in December 2016 was caused by weathering to take place. The increases resistivity value of weathered granite due to the current

91 was attracted more towards lower resistance material as their path. Therefore, leaving other part to be higher resistivity values.

Figure 4.20: Bukit Relau fracture changes throughout the monitoring period.

7000

6000

m) 5000 Ω

4000 fracture 3000

2000 Resistivity Resistivity value ( 1000

0 40 42 45 47 49 52 54 56 59 61 64 66 68 71 73 75 78 80 Distance (m)

September November December January

Figure 4.21: Monitoring resistivity values at distance 40 – 80 m at Bukit Relau study area. 4.1.3 Air Hitam

A 100 meter survey line was conducted at this study area perpendicular to the slope using 2D resistivity and IP methods. A soil sample was taken at the centre of the survey line as representative to understand the soil condition at the study area.

Monitoring by using 2D resistivity method and PSD analysis were not conducted at this study area as the area was closed due to landslide occurrence. Therefore, this study area only discussed in term of laboratory tests for moisture content and porosity as well as geophysical methods of 2D resistivity and IP methods.

4.1.3(a) Laboratory test

The soil sample was taken along the survey line for testing average of moisture content, w, average porosity, ƞ, and average bulk density, ρbulk of Air Hitam were presented in Table 4.7. However, there is only a single soil sample taken due to the area has been closed due to landslide occurrence. The sample is taken at distance 50 meter.

From Table 4.11, the moisture content and porosity of the soil at this area are

20.145 % and 55.41 % respectively. Based on the soil porosity value, soil at Air Hitam can be group as loose soil based on soil classification by Faur and Szabo (2011). The bulk density and particle’s density are 1.00 g/cm3 and 2.25 g/cm3 respectively. The soil at Air Hitam study area is ideal for plant growth based on the bulk density value and not compacted which allowed for easy water movement.

Table 4.11: Laboratory test for Air Hitam study area.

Parameters with unit Value Moisture content, w (%) 20.145 Porosity, ƞ (%) 55.41 Bulk density, ρbulk (%) 1.00

4.1.3(b) Geophysical methods

Based on the resistivity inversion model (Figure 4.22a), two resistivity zones are detected (Table 4.12). Resistivity value < 400 Ωm is interpreted as saturated zone located at distance of 30 – 100 m with less than 10 m depth. Resistivity with value

3000 - 30000 Ωm is interpreted as granite bedrock located at > 10 m depth. The same resistivity range was detected between distance 20 – 30 m and identified as granite boulder.

Table 4.12: Summary of resistivity value with interpretation and features found at Air Hitam study area.

Resistivity (Ωm) Interpretation < 400 Saturated zone at distance of 30 – 100 m 3000 - 30000 Granitic bedrock at depth > 10 m Features Description Boulder Found at distance 20 - 30 m with high resistivity value

From the induced polarization inversion model (Figure 4.22b), two chargeability zones are found (Table 4.13). Chargeability with value 25 – 50 ms located at saturated zone at distance 30 – 75 m. This is interpreted as clay saturated

94 zone due to low resistivity value but high chargeability value. Chargeability value of

< 3 ms, found at distance 25 – 50 m at depth 10 – 15 m is due to the location locality which is highly jointed and sheared area therefore, results in lower chargeability value.

The same chargeability value recorded at distance 80 – 100 m. This is interpreted as water saturated zone.

Table 4.13: Summary of chargeability value with interpretation and features found at Air Hitam study area.

Chargeability (ms) Interpretation < 3 Water saturated zone at distance of 80 – 100 m 25 - 50 Clay saturated zone at distance of 30 – 75 m

Air Hitam subsurface consists of clay material. If the clay become saturated especially during heavy rain season, this will trigger a landslide to occur. Aside from that, the existence of boulders will cause rock fall. As mention by Azmi (2014), slope angle at this area is 19° - 25° which is considered as gentle to dangerous slope by

Ministry of Science, Technology and Environment Malaysia.

Figure 4.22: Inversion model of (a) 2D resistivity and (b) induced polarization method at Air Hitam study area. 4.2 Empirical correlation between soil moisture content and porosity

The empirical correlation between moisture content and porosity of soil

(Figure 4.23) at Sungai Batu study area represented by equation y = 3.9562x – 185.35 with regression 0.7199 whereas at Bukit Relau study area is represent by equation y =

0.3316x + 6.2054 and regression of 0.4684. Both study areas show very strong and strong correlation respectively according to the category from Evan (1996). Sungai

Batu study area indicated more significant correlation compare to Bukit Relau study area. Empirically, the soil moisture content increases as soil porosity increases. This is aligned with study conducted by Mukhlisin and Taha (2011). As the moisture content increase, it also allows greater damage when slope failure occurs as soil contained larger amount of water. Result obtained from Air Hitam unfortunately cannot be include because there was only a single sample from the study area therefore the data is insufficient.

20 y = 0.3316x + 6.2054 R² = 0.4684 15 y = 3.9562x - 185.35 R² = 0.7199

10 Moisture Moisture content (%) 5

0 40 45 50 55 60 Porosity (%) Sungai Batu Bukit Relau

Figure 4.23: Empirical correlation between porosity and moisture content at Sungai Batu and Bukit Relau study area.

4.3 Empirical correlation between geophysical data and laboratory tests

Results obtained from geophysical methods are correlated with the soil laboratory test results. This aims to observe the relationship between them. Due to the soil at Sungai Batu and Bukit Relau are gravelly sand soil, thus the results from study areas are combined.

Geophysical resistivity result is correlated with the moisture content and porosity obtained from the soil laboratory tests (Figure 4.24). Empirical correlation between resistivity and soil moisture content is indicated by equation y = -0.0113x +

35.701 and recorded regression, R2 = 0.4343. Based on Evan (1996), this can be considered as strong correlation and the resistivity is inversely proportional to moisture content as studied by Bery and Saad (2012). Another parameter studied is the soil porosity which recorded as y = -0.0047x + 59.267 and regression, R2 = 0.3062.

Correlation between resistivity and porosity can be considered as moderate correlation.

The relationship between resistivity and porosity is inversely proportional to each

97 other. Based on relationship obtained, resistivity increases if moisture content or porosity is low in percentage.

50 y = -0.0047x + 59.267 R² = 0.3062 40

30 y = -0.0113x + 35.701 Percentage (%) 20 R² = 0.4343

0 0 500 1000 1500 2000 Resistivity (Ωm) moisture content porosity

Figure 4.24: Empirical correlation between resistivity and soil laboratory tests.

Geophysical chargeability result is also correlated with moisture content and porosity to observe the relationship between them (Figure 4.25). Empirical correlation between chargeability and moisture content is represented by y = 1.5854x + 13.153 and regression, R2 = 0.5255. This correlation between them can be classified as strong and relationship between moisture content and chargeability is directly proportional to each other which is equal to result studied by Kiberu (2002). As for porosity, empirical correlation with chargeability is represented by equation, y = -0.7675x + 53.771 and the regression, R2 = 0.5858. Therefore, correlation chargeability and porosity is identified as strong and the relationship is inversely proportional to each other, aligned with study done by Telford et al. (1990). To conclude, the chargeability value increases with decreasing of soil moisture content and porosity.

y = -0.7675x + 53.771 50 R² = 0.5858

30 y = 1.5854x + 13.153

20 R² = 0.5255 Percentage (%)

0 0 2 4 6 8 10 12 14 Chargeability (ms) Moisture content Porosity Figure 4.25: Empirical correlation between chargeability and soil laboratory tests.

The empirical correlation successfully show that the geophysical method and soil laboratory test can support each other. Therefore, this indicated that implementation of geophysical methods for soil study is acceptable.

4.4 Empirical correlation between geophysical data and rainfall distribution

The geophysical results are also correlated with rainfall distribution in order to observe the rainfall distribution effect on resistivity and chargeability values.

Therefore, the cumulative rainfall of seven days including survey day is correlated with geophysical results. This rainfall distribution is chosen because cumulative rainfall affected the subsurface more than rainfall on the 6th day or 7th day.

The geophysical resistivity results are correlated with the cumulative rainfall of seven days including survey day shows that the relationship between the parameters is inversely proportional to each other (Figure 4.26). Represented by equation, y = -

2.4068x + 1315.1 with regression, R2 = 0.5663, the correlation can be classified as strong with outliers. Therefore, the resistivity value is expected to be lower when there is heavy rainfall.

1600

1400

1200 m)

Ω 1000 y = -2.4068x + 1315.1 800 R² = 0.5663

600 Resistivity Resistivity ( 400

200

0 0 20 40 60 80 100 120 140 160 Rainfall distribution (mm)

Figure 4.26: Empirical correlation of resistivity and cumulative rainfall of seven days including the survey day.

Next, the geophysical chargeability results are correlated with the cumulative rainfall of seven days including survey day recorded equation, y = 0.0178x + 2.7537 with regression, R2 = 0.2576 (Figure 4.27). Correlation between both parameters is identified as moderate correlation. Therefore, the rainfall distribution is directly proportional to the chargeability value. As conclusion, geophysical resistivity and chargeability values can be affected by rainfall distribution.

4 y = 0.0178x + 2.7537 R² = 0.2576 3

2 Chargeability Chargeability (ms)

0 0 20 40 60 80 100 120 140 160 Rainfall distribution (mm)

Figure 4.27: Empirical correlation of chargeability and cumulative rainfall of seven days including the survey day.

100

4.5 Summary

The implementation of geophysical methods of 2D resistivity and IP methods for slope study successfully mapped the subsurface of slopes in Penang Island.

Integrating both methods, any potential factors for slope failure such as weathering zone, boulders, saturated zone and weak zone were able to identified and the resistivity and chargeability values at the study areas were able to be characterized accordingly

(Telford et al., 1990; Jinmin et al., 2013; Ronning et al., 2014; Yaccup and Tawnie,

2015; Muztaza et al, 2017). Monitoring study areas by using 2D resistivity method produced intriguing results which is changes occur within the subsurface were highly affected by the surrounding conditions including rainfall distribution. As geophysical method common known to be site specific, therefore, the resistivity and chargeability obtained in this research at the study areas are categorised as shown in Table 4.14 and

4.15 respectively.

Table 4.14: Categories of resistivity value obtained in this research.

Resistivity range Resistivity value (Ωm) Interpretation Weak zone Low < 400 Saturated zone Intermediate 500 – 3000 Weathered granite zone High > 3000 Granitic bedrock

Table 4.15: Categories of chargeability value obtained in this research.

Chargeability range Chargeability value (ms) Interpretation Low 0 – 3 Water saturated zone Intermediate 3 – 10 Gravel Granitic bedrock High > 10 Clay saturated zone

Table 4.16 to 4.18 summarised the result of laboratory soil test at all three study areas with geophysical results of 2D resistivity and IP method. PSD analysis conducted successfully classified the soil at Sungai Batu and Bukit Relau as gravelly sand.

101

Table 4.16: Summary of result obtained from Sungai Batu study area.

Resistivity Chargeability Moisture content Porosity Bulk density PSD analysis Distance (m) Cc Cu Mean Sorting (Ωm) (ms) (%) (%) (g/cm3) (%) Gravel 8.67 70 - 75 786.31 13.29 15.478 54.35 1.14 Sand 90.27 1.45 8.00 - 0.410 1.269 Clay/silt 1.06 Gravel 25.25 95 – 100 1160.9 3.37 25.346 52.45 0.859 Sand 74.21 0.987 5.917 - 0.380 0.315 Clay/silt 0.54 Gravel 4.82 100 1832.8 0.7079 14.645 50.78 1.10 Sand 93.03 1.19 6.86 - 0.033 0.663 Clay/silt 2.15 Gravel 14.67 100 – 105 1350.7 4.66 18.73 51.86 1.09 Sand 84.42 2.82 6.00 - 0.837 1.039 102 Clay/silt 0.91

180 – 185 1252 4.26 19.418 51.19 0.99 195 – 200 1314.7 2.35 18.77 51.97 0.99

Table 4.17: Summary of result obtained from Bukit Relau study area.

Resistivity Chargeability Moisture content Porosity Bulk density PSD analysis Distance (m) Cc Cu Mean Sorting (Ωm) (ms) (%) (%) (g/cm3) (%) Gravel 22.26 50 1127 4.99 24.72 58.52 1.00 Sand 77.06 0.793 6.10 - 0.150 0.458 Clay/silt 0.68 Gravel 23.45 100 1365.7 0.9291 26.31 53.47 1.07 Sand 75.06 1.32 10.17 - 0.033 0.662 Clay/silt 1.50 Gravel 42.07 140 – 145 1199.4 3.85 24.29 55.72 0.95 Sand 56.91 0 0 - 0.577 0.336 Clay/silt 1.015 Gravel 22.80 185 – 190 1188.5 5.89 21.08 47.86 1.09 Sand 84.42 0.552 3.448 - 0.167 0.449 103 Clay/silt 0.91

180 – 185 1252 4.26 19.418 51.19 0.99 195 – 200 1314.7 2.35 18.77 51.97 0.99

Table 4.18: Summary of result obtained from Air Hitam study area.

Distance (m) Resistivity (Ωm) Chargeability (ms) Moisture content (%) Porosity (%) Bulk density (g/cm3) 50 507.27 37.55 20.145 55.41 1.00

Based on the result, this research obtained strength of correlation range from moderate to very strong correlation according to strength of correlation by Evan (1996) and the result obtained can verified with previous researchers (Telford et al., 1990;

Kiberu, 2002; Muklisin and Taha, 2011; Bery and Saad, 2012). Table 4.19 summarised the empirical correlation between the geophysical data, laboratory result of moisture content and porosity and the cumulative rainfall of seven days including the survey day.

Table 4.19: Summary of empirical correlation between geophysical data, laboratory tests and rainfall distribution.

Coefficient of Coefficient of Empirical Strength of Equation Correlation, Determination, Correlation Correlation R R2 Porosity vs y = 0.3316x + moisture content 0.6844 0.4684 Strong 6.2054 (Sungai Batu) Porosity vs y = 3.9562x + moisture content 0.8485 0.7199 Very strong 185.35 (Bukit Relau) Resistivity vs y = -0.0113x + 0.6590 0.4343 Strong moisture content 35.701 Resistivity vs y = -0.0047x + 0.5533 0.3062 Strong porosity 59.267 Chargeability vs y = 1.5854 + 0.7249 0.5255 Strong moisture content 13.153 Chargeability vs y = -0.7675x + 0.7654 0.5858 Strong porosity 53.771 Rainfall y = -2.4068x + distribution vs 0.7525 0.5663 Strong 1315.1 resistivity Rainfall y = 0.0178x + distribution vs 0.5075 0.2576 Moderate 2.7537 chargeability

Information obtained in this research successfully contribute to achieve the objectives of the research which is to detect any potential slope failures in the study areas and 2D resistivity and IP method successfully assess the slope in the study areas.

Thus, the study areas have the potential for slope failures occurrence based on the results of this research.

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CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

5.0 Conclusion

Slope failures are common in Penang Island especially during raining season.

Therefore, slopes at high potential areas for slope failure were studied by using geophysical method of 2D resistivity and IP methods with correlation of laboratory tests. Rainfall distribution was accounted for enhancing the slope failure detection.

This aims to detect any potential slope failure within the slope subsurface at the study area of Sungai Batu, Bukit Relau and Air Hitam. Therefore, any slope failure that affected by these parameters were identified.

As a conclusion, the geophysical methods and laboratory tests were succeeded to study the slope at Penang Island with the rainfall distribution support. The results showed that each area has different reasons and factors that make slope failure possible. The summary for the slope failure potentials and the factors are shown in

Table 5.1.

The moisture content and porosity results supported this research findings as they helped to further understand the soil condition at the study areas. Rainfall distribution results obtained from Department of Meteorology Malaysia and

Department of Irrigation and Drainage Malaysia also helped to support the interpretation as well as understand the phenomena behind each result. In order to understand the soil condition, PSD method carried out and the gradation and sorting of soil samples were calculated. The soil particle can also affect the slope ability to withstand erosion and also from sliding down.

105

From the geophysical results, the slope failure potential can be narrowed down.

At Sungai Batu study area, the slope failure is possible due to the existence of weak zone with resistivity value < 400 Ωm within the subsurface. The weak zone consists clayey material based on low resistivity but high chargeability characteristic that risen the slope failure potential as saturated clay may become overburden and caused it to slide down. Throughout monitoring period, the weak zone experienced changes due to the rainfall distribution. As the rainfall distribution increases, the weak zone become more saturated and recorded lower resistivity values. The weathered zone with resistivity value 500 – 3000 Ωm is also affected by the rainfall distribution where the resistivity value decreases as rainfall increase. PSD analysis and laboratory test for soil indicate that this soil at Sungai Batu is gravelly sand which is permeable soil which allow for water infiltration from rainfall.

Bukit Relau study area results indicated that this area was a massive granite rock that has been weathered. The result of monitoring also showed that the weathered zone with resistivity value of 750 – 3000 Ωm encountered changes. Material within the fracture at location 40 – 70 m indicated decreases in resistivity values and the size of fracture was increased throughout the monitoring period. Thus, the slope failure can be inferred to be caused by the weathering effect as the it could produce features such boulders and fractures. The rainfall distribution affected the weathering by the rainfall infiltrated the subsurface thus, further increases the weathering process. If these features were to become unstable, phenomena such as rockfall and landslide were highly anticipated. Based on PSD analysis and laboratory test, the soil at Bukit Relau is determine as gravelly sand which is permeable soil that allowed rainfall to infiltrate into the subsurface.

106

Lastly, Air Hitam study area successfully differentiate between water saturated zone and clay saturated zone as both have resistivity values < 400 Ωm. Through the application of IP method, these can be separated as water saturated and clay saturated have different chargeability value. Water saturated zone has chargeability value of 0 –

3 ms while clay saturated zone has chargeability value > 25 ms. This area was determined to be susceptible to slope due to existence of clay saturated zone. As the clay will become increase the soil weight upon high rainfall distribution therefore, become overburden to the slope.

Several correlations were achieved in this study. Geophysical result and the soil laboratory test concluded that the relationship between resistivity and soil moisture content and soil porosity is the resistivity increases as moisture content and porosity decreases. Whereas, the chargeability increases with increasing moisture content and decreasing porosity. The geophysical results were also correlated with the rainfall distribution. Empirically, the resistivity and chargeability values are lower with increasing rainfall distribution.

This research also successfully assesses the slope at the highly suspected area in Penang Island which is Sungai Batu, Bukit Relau and Air Hitam by using 2D resistivity and IP methods for slope failure potential.

107

Table 5.1: Summary of slope failure potentials and the factor for failure at the study areas.

Slope Moisture Bulk Study Rainfall Resistivity range (Ωm) Chargeability range (ms) Porosity Slope failure Factor for angle content density PSD area intensity (%) potentials failure (°) Low Intermediate High Low Intermediate High (%) (g/cm3)

15.478

Low

Gravellysand

50.78 50.78

3000

0.859 0.859

Sungai Sungai Batu

500

3 Weak zone, Permeable

< 400 <

< 3 <

moderate

– –

Presence of soil,

–

3000

20000

25.346 54.35

1.14 clayey High rainfall

material distribution

108

Low

Gravellysand

21.08 21.08 47.86

3000

Bukit RelauBukit Permeable

0.95 0.95

750

3 soil,

< 3 <

moderate

–

– –

Weathered High rainfall

3000

10000

25.31 58.52 1.09

30 granite distribution,

Weathering

effect

3000

Air HitamAir

20.145

< 400 <

55.41

1.00

< Clay

–

- - - -

High rainfall

30000

50 saturated

distribution

zone

5.1 Recommendations for future work

The findings of this research successfully show the effectiveness of 2D resistivity and IP method in identifying the slope failure potentials in Penang Island which is underlain by granitic bedrock. Therefore, the findings can be broadened by applying both methods to different geological settings such as limestone and shale in different locations in order to identify the slope failure potentials within the subsurface as slope failures in Malaysia are occurs not only within granitic bedrock but other types of bedrock as well.

Based on the results, the presence of clayey material within the subsurface is one of the factor that leads to slope failure as it become sticky and heavy during high rainfall distribution. Detailed study regarding the mechanism on how clay affect the slope failure should be conduct for better understanding.

By suggesting the possible future study on different geological settings and the effect of clay on slope failure, any future cases related to both issues can be overcome effectively.

109

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APPENDIX A Computation analysis of particle size distribution

Sungai Batu Sample October 2016 (at 95 – 100 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) A1 A2 Total (%) (mm) 4 4.75 224.82 181.19 406.01 25.249 25.249 74.751 Gravel 10 2 307.63 248.51 556.14 34.585 59.833 40.167 25.25 20 0.85 210.49 190.11 400.6 24.912 84.745 15.255 40 0.425 71.14 63.98 135.12 8.403 93.148 6.852 Sand 60 0.25 24.8 20.61 45.41 2.824 95.972 4.028 74.21 140 0.106 10.08 8.22 18.3 1.138 97.110 2.890 200 0.075 20.62 17.12 37.74 2.347 99.457 0.543 Clay/silt pan 0 4.73 4 8.73 0.543 100.000 0.000 0.54 1608.05

0.075 mm 0.425 mm 4.75 mm 100

Cumulative Cumulative finer (%) 30

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.6 Cc 0.987 D30 1.45 Cu 5.917 D60 3.55

Sample November 2016 (at 100 - 105 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) B1 B2 Total (%) (mm) 4 4.75 168.16 181.37 349.53 14.670 14.670 85.330 Gravel 10 2 328.52 515.4 843.92 35.420 50.090 49.910 14.67 20 0.85 246.68 437.98 684.66 28.736 78.826 21.174 40 0.425 100.08 181.56 281.64 11.821 90.646 9.354 Sand 60 0.25 36 60.89 96.89 4.067 94.713 5.287 84.42 140 0.106 15.4 23.39 38.79 1.628 96.341 3.659 200 0.075 27.7 37.71 65.41 2.745 99.086 0.914 Clay/silt pan 0 9.16 12.61 21.77 0.914 100.000 0.000 0.91 2382.61

0.075 mm 0.425 mm 4.75 mm 100

40 Cumulative Cumulative finer (%) 30

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.45 Cc 2.82 D30 1.85 Cu 6.00 D60 2.7

Sample December 2016 (at 70 – 75 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) C1 C2 Total (%) (mm) 4 4.75 470.320 534.530 75.885 8.669 8.669 91.331 Gravel 10 2 402.190 637.360 277.931 31.750 40.419 59.581 8.67 20 0.85 351.790 570.410 258.372 29.516 69.935 30.065 40 0.425 354.750 453.830 117.096 13.377 83.312 16.688 Sand 60 0.25 308.240 357.750 58.512 6.684 89.996 10.004 90.27 140 0.106 295.650 320.170 28.978 3.310 93.306 6.694 200 0.075 269.100 310.830 49.318 5.634 98.940 1.060 Clay/silt pan 0 256.190 264.040 9.277 1.060 100.000 0.000 1.06 875.370

0.075 mm 0.425 mm 4.75 mm 100

40 Cumulative Cumulative finer (%) 30

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.25 Cc 1.45 D30 0.85 Cu 8.00 D60 2

Sample January 2017 (at 100 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) D1 D2 Total (%) (mm) 4 4.75 22.19 46.24 68.43 4.822 4.822 95.178 Gravel 10 2 106.25 140.5 246.75 17.389 22.211 77.789 4.82 20 0.85 215.9 211.4 427.3 30.112 52.323 47.677 40 0.425 133.02 140.72 273.74 19.291 71.613 28.387 Sand 60 0.25 75.04 77.76 152.8 10.768 82.381 17.619 93.03 140 0.106 40.13 40.29 80.42 5.667 88.048 11.952 200 0.075 70.99 68.07 139.06 9.800 97.848 2.152 Clay/silt pan 0 16.24 14.3 30.54 2.152 100.000 0.000 2.15 679.76 739.28 1419.04 100

0.075 mm 0.425 mm 4.75 mm 100

40 Cumulative Cumulative finer (%)

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.175 Cc 1.190 D30 0.5 Cu 6.857 D60 1.2

Bukit Relau Sample October 2016 (at 140 - 145 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) A1 A2 Total (%) (mm) 4 4.75 573.68 459.01 1032.69 42.071 42.071 57.929 Gravel 10 2 305.17 301.63 606.8 24.721 66.792 33.208 42.071 20 0.85 237.71 229.38 467.09 19.029 85.821 14.179 40 0.425 85.29 73.43 158.72 6.466 92.287 7.713 Sand 60 0.25 36.16 27.64 63.8 2.599 94.886 5.114 56.913 140 0.106 18.79 13.4 32.19 1.311 96.198 3.802 200 0.075 39.61 28.8 68.41 2.787 98.985 1.015 Clay/silt pan 0 14.87 10.05 24.92 1.015 100.000 0.000 1.015 2454.62

0.075 mm 0.425 mm 4.75 mm 100

Cumulative (%) finer Cumulative 30

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.6 Cc 0.000

D30 2 Cu 0.000

D60 0

Sample November 2016 (at 50 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) A1 A2 Total (%) (mm) 4 4.75 266.38 248.89 515.27 22.256 22.256 77.744 Gravel 10 2 363.79 337.33 701.12 30.284 52.540 47.460 22.26 20 0.85 312.25 303.23 615.48 26.585 79.125 20.875 40 0.425 139.52 141.24 280.76 12.127 91.252 8.748 Sand 60 0.25 46.02 46.59 92.61 4.000 95.252 4.748 77.06 140 0.106 17.22 18.04 35.26 1.523 96.775 3.225 200 0.075 28.45 30.38 58.83 2.541 99.316 0.684 Clay/silt pan 0 7.28 8.55 15.83 0.684 100.000 0.000 0.68 2315.16

0.075 mm 0.425 mm 4.75 mm 100

Cumulative Cumulative finer (%) 30

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.5 Cc 0.793 D30 1.1 Cu 6.100 D60 3.05

Sample December 2016 (at 185 – 190 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) A1 A2 Total (%) (mm) 4 4.75 267.440 359.810 627.250 22.796 22.796 77.204 Gravel 10 2 373.710 415.900 789.610 28.697 51.493 48.507 22.80 20 0.85 383.880 414.240 798.120 29.006 80.499 19.501 40 0.425 147.480 151.750 299.230 10.875 91.374 8.626 Sand 60 0.25 47.120 49.880 97.000 3.525 94.899 5.101 76.26 140 0.106 19.950 20.870 40.820 1.484 96.382 3.618 200 0.075 35.500 38.060 73.560 2.673 99.056 0.944 Clay/silt pan 0 12.440 13.540 25.980 0.944 100.000 0.000 0.944 2751.570

0.075 mm 0.425 mm 4.75 mm 90

Cumulative Cumulative finer (%) 30

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.87 Cc 0.552 D30 1.2 Cu 3.448 D60 3

Sample January 2017 (at 100 m)

Mass retained on each sieve Sieve size Soil (g) Cumulative Cumulative Weight (%) category No. Aperture weight (%) finer (%) A1 A2 Total (%) (mm) 4 4.75 152.84 184.24 337.08 23.448 23.448 76.552 Gravel 10 2 161.11 235.68 396.79 27.602 51.050 48.950 23.45 20 0.85 144.4 218.33 362.73 25.233 76.283 23.717 40 0.425 65.99 92.38 158.37 11.017 87.300 12.700 Sand 60 0.25 28.97 36.66 65.63 4.565 91.865 8.135 75.06 140 0.106 14.21 17.06 31.27 2.175 94.041 5.959 200 0.075 30.43 33.73 64.16 4.463 98.504 1.496 Clay/silt pan 0 10.38 11.13 21.51 1.496 100.000 0.000 1.50 1437.54

0.075 mm 0.425 mm 4.75 mm 100

Cumulative Cumulative finer (%) 40

0 0.01 0.1 1 10 Particle diameter (mm) - log scale

D10 0.3 Cc 1.322 D30 1.1 Cu 10.167 D60 3.05

APPENDIX B Record of daily rainfall amount

Station : Bayan Lepas Elevation : 2.46 m Latitude : 5° 17' 49" N Year : 2016 Longitude : 100° 16' 20" E

Daily rainfall distribution (mm) Date JAN FEB MAR APR MAY JUN JUL 1 2.8 0.0 0.0 0.0 0.0 0.2 0.0 2 0.0 0.0 0.0 0.0 30.0 47.6 0.0 3 0.0 3.4 0.0 6.0 Trace Trace 0.0 4 0.0 0.0 0.0 0.0 14.6 0.0 0.0 5 0.0 25.0 0.0 Trace 0.4 0.0 0.0 6 3.4 Trace 58.6 0.0 0.2 Trace Trace 7 0.0 0.0 0.6 0.0 3.8 0.0 0.0 8 0.0 0.0 0.0 0.0 0.4 0.0 0.0 9 0.0 0.0 0.0 Trace 3.8 0.0 0.0 10 Trace 0.0 0.0 1.4 3.4 Trace 0.0 11 0.0 Trace 0.0 0.0 Trace 4.0 0.0 12 0.0 1.0 0.0 0.0 19.8 0.2 0.0 13 Trace Trace 0.0 0.0 2.0 0.0 2.8 14 11.2 0.0 Trace 0.0 0.2 Trace 0.4 15 11.0 0.8 0.0 0.0 20.2 0.0 1.6 16 0.0 0.0 0.0 0.2 Trace 5.8 10.4 17 4.8 0.0 0.0 Trace 0.2 43.4 2.8 18 6.2 0.0 0.0 Trace 0.4 0.0 200.8 19 0.0 0.0 0.0 0.0 Trace 24.4 31.6 20 0.0 0.0 0.0 4.2 42.8 0.8 8.0 21 0.0 0.0 0.0 0.0 1.6 0.0 0.0 22 Trace 0.0 0.0 Trace Trace 0.0 1.8 23 0.0 0.0 0.0 Trace 0.0 0.0 0.8 24 0.0 0.0 0.0 9.6 0.0 0.0 4.0 25 0.0 0.0 0.0 4.6 7.2 0.0 Trace 26 0.0 0.0 0.0 3.2 0.0 0.0 2.2 27 0.0 0.0 0.0 9.6 0.0 0.0 0.0 28 0.0 0.0 Trace Trace 21.4 0.0 20.6 29 Trace 0.0 0.0 4.0 26.8 0.0 0.0 30 0.0 0.0 3.4 0.2 Trace 1.6 31 Trace 0.0 0.0 Trace Total 39.4 30.2 59.2 46.2 199.4 126.4 289.4 No. of Raindays 6 4 2 10 20 8 14

Definition: Trace - Rainfall amount less than 0.1 millimetre

Station : Bayan Lepas Elevation : 2.46 m Latitude : 5° 17' 49" N Year : 2016 - 2017 Longitude : 100° 16' 20" E

Daily rainfall distribution (mm) Date AUG SEP OCT NOV DEC JAN FEB 1 0.0 84.6 0.0 1.6 3.8 Trace Trace 2 0.0 2.8 8.8 0.0 10.6 3.6 0.0 3 0.0 0.0 Trace 4.8 1.6 2.6 0.0 4 0.0 30.0 Trace 58.0 2.6 0.0 3.2 5 0.0 49.8 28.6 21.8 Trace Trace 16.4 6 1.2 5.0 2.0 44.4 0.0 1.8 0.0 7 0.0 0.0 0.0 5.4 0.0 4.8 N.A. 8 0.0 35.6 0.0 Trace 0.0 1.2 N.A. 9 2.4 0.0 17.8 Trace 0.2 0.0 N.A. 10 Trace Trace 5.8 0.2 12.4 0.0 N.A. 11 4.8 0.0 0.8 12.2 26.6 0.0 N.A. 12 2.2 0.0 0.8 6.2 10.4 0.0 N.A. 13 4.6 0.0 20.4 0.0 5.0 2.8 N.A. 14 0.0 0.0 8.8 19.8 1.6 3.6 N.A. 15 7.2 23.6 9.0 11.6 0.4 0.0 N.A. 16 0.0 2.8 87.8 Trace 0.0 9.8 N.A. 17 0.0 40.2 11.8 1.2 0.0 0.6 N.A. 18 1.2 9.2 0.6 1.8 Trace 15.8 N.A. 19 5.4 21.2 Trace 71.0 Trace 2.8 N.A. 20 Trace 6.6 4.2 2.4 6.4 2.4 N.A. 21 4.2 3.8 Trace 1.0 0.6 Trace N.A. 22 45.6 Trace 1.2 10.6 16.0 1.8 N.A. 23 45.0 0.8 91.6 9.6 0.6 102.6 N.A. 24 1.6 Trace Trace Trace 0.2 11.2 N.A. 25 17.2 1.0 11.6 16.0 0.0 Trace N.A. 26 0.8 0.4 Trace 7.8 Trace 18.6 N.A. 27 Trace 18.8 Trace 0.8 0.0 Trace N.A. 28 1.6 63.2 9.6 0.0 0.0 0.0 N.A. 29 Trace 4.8 26.0 0.2 0.0 0.0 N.A. 30 0.0 0.0 23.8 0.0 0.0 0.0 N.A. 31 4.4 49.4 0.4 9.0 N.A. Total 149.4 404.2 420.4 308.4 99.4 195.0 N.A. No. of Raindays 16 19 21 22 17 17 N.A.

Definition: Trace - Rainfall amount less than 0.1 millimetre N.A. - Not available

Source : Department of Meteorology Malaysia Station : Kolam Takungan Air Hitam Elevation : 231 m Latitude : 5° 23' 45" N Year : 2016 Longitude : 100° 15' 55" E

Daily rainfall distribution (mm) Date JAN FEB MAR APR MAY JUN JUL 1 N.A 0 0 0 0 0 2.5 2 0 0 0 0 0.5 0 0 3 0 0 0 5.5 0 44 0 4 0 0 0 2.5 0.5 0 0 5 0 0 0 0 7.5 0 0 6 0 8.5 0 0 0 0 0 7 0 0 10.5 0 12 0 0 8 0 0 0 0 4 0 0 9 0 0 0 0 0.5 0 0 10 0 0 0 0 0 0 0 11 0 0 0 12.5 5.5 0 0 12 0 0 0 0.5 0 0 0 13 0 0 0 0 28.5 20 0 14 0.5 10.5 0 0 1 0 1.5 15 1 0 0 0 0.5 0 1 16 7 5.5 0 0 24.5 0 1 17 3 0 0 0.5 3.5 79.5 10 18 34 0 0 2 3 83 26 19 48 1 0 0 0 2 114.5 20 1 0 0 0 0 46 13.5 21 0 0 0 16.5 10 0.5 7 22 0 0 0 0 4.5 0 0 23 0 0 0 0 0 0 4 24 0 0 0 0 0 0 4.5 25 0 0 0 17 0 0 11.5 26 0 0 0 0 7 0 0 27 0 0 0 1.5 0 0 6 28 0 0 0 0 2 0 0 29 0 0 37 19 24.5 0 0 30 0 0 0 45 0 0 31 0 0 1 0 Total 94.5 25.5 47.5 77.5 185.5 275 203 No. of Raindays 7 4 2 10 20 7 13

Definition: N.A. - Not available

Station : Kolam Takungan Air Hitam Elevation : 231 m Latitude : 5° 23' 45" N Year : 2016 - 2017 Longitude : 100° 15' 55" E

Daily rainfall distribution (mm) Date AUG SEP OCT NOV DEC JAN 1 0 10 0 24.4 0 0 2 0 96 0 1.5 0 0 3 0 5 1 0 0 3 4 0 0 5 27 0 51 5 0 31 1.5 97 0 0 6 0 96 6 22.5 0 0 7 0 38 1.5 124 0 18 8 0 0 0 7.5 0 5.5 9 0 51.5 0 0 0 11.5 10 0 0.5 28.5 6.5 26.5 0 11 5.5 0 1 1 15 0 12 1 0 0 3.5 31.5 0 13 7 0 9 3.5 20 0 14 1.5 0 5.5 2 11 0.5 15 0 0.5 23.5 8 1.5 3.5 16 0 23.5 6.5 40 1 1.5 17 0 0.5 47 7.5 0 0 18 0 87 19 0.5 17.5 3.5 19 0 25 0 41 0 3.5 20 3.5 32 1 74 4.5 11 21 8 0.5 6 0 6 5 22 0 20 0 0 0 0.5 23 0.5 0.5 0.5 0 29 1.5 24 20 9.5 86.5 0 0.5 102.5 25 8.5 0 1 0 0.5 10 26 26.5 0 2.5 0 0 0 27 1.5 3 0 0 0 25 28 0 3 1 0 0 0.5 29 1 74.5 0.5 0 0 0 30 10 20 113.5 0 0 0 31 0.5 37.6 0 Total 95 627.5 405.1 491.4 164.5 257.5 No. of Raindays 14 22 23 18 13 18

Source : Department of Irrigation and Drainage Malaysia APPENDIX C

LIST OF PUBLICATIONS

Conference Proceeding

1. Muhamad Iqbal Mubarak Faharul Azman, Azim Hilmy Mohamad Yusof, Nur Azwin Ismail, and Noer El Hidayah Ismail. (2017). Slope monitoring by using 2-D resistivity method at Sungai Batu, Pulau Pinang, Malaysia. AIP Conference Proceedings, 1861. doi: 10.1063/1.4990910.

Conferences

1. Faharul Azman, M. I. M., Mohamad Yusof, A. H., Ismail, N. A., and Muztaza, N. M. (2017). The application of 2D resistivity and induced polarization methods for slope study at Penang Island, Malaysia, 136th SEGJ Conference. Paper presented at International Conference Centre, Waseda University, Tokyo, Japan, 5 – 7 June 2017.

2. Muhamad Iqbal Mubarak Faharul Azman, Azim Hilmy Mohd Yusof, Nur Azwin Ismail, and Noer El Hidayah Ismail. (2017). Slope stability study at Jalan Tun Sardon and Sungai Batu, Pulau Pinang by using 2-D resistivity method. National Physics Conference (PERFIK) 2016. Paper presented at Pullman Hotel Kuala Lumpur Bangsar, 21 – 22 December 2016.