Effect of Extreme Rainfall Intensity on Matric Suction and Ground Movement

E3S Web of Conferences 195, 01022 (2020) https://doi.org/10.1051/e3sconf/202019501022 E-UNSAT 2020

Muhammad Hazwan Zaki1, Mastura Azmi1,*, Siti Aimi Nadia Mohd Yusoff1,Muhd Harris Ramli1 and Mohd Azril Hezmi2

1School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia 2School of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

Abstract. Increased intensity of rainfall events due to extreme climate change has led to the substantial increase in the occurrence of disasters, especially in a tropical-climate country such as Malaysia. Rainfall- induced landslide has become one of the most common types of disasters, and its triggering factors are still uncertain and impossible to predict. In this study, the effect of extreme rainfall intensity on groundwater behaviour is addressed through laboratory-scale testing. The adopted rainfall intensity is 60 mm/h, which was the heaviest hourly rainfall intensity recorded in Sarawak on 3rd January 2016 and 80 mm/h, which was the corresponding value recorded in Penang on 10th October 2016. The simulation is conducted on four cases. The simulated rainfall exhibits a duration of 6 h. In addition, the overall trend of the matric suction measurement and soil moisture in all cases is discussed on the basis of the results obtained from laboratory studies. After the rain simulator stopped, the matric suction decreases, and it remains stagnant, followed by a significant drop in the reading. For all cases, failure occurs, albeit at different times with different volumes of mass wasting.

1 Introduction * 7 5%6 7 5 6 % / 2 % * * 8 * ( 8 * % ! " # $ % ( * & ' * ** ! * 1.1 Physical modelling % 8 ( * % % % ) * % 9 ( 8 * * % + % , ! - * % $.. % /.. 0% 1 2 3 -- 2 Methodology

2.1 Rainfall analysis data 1.2 Rainfall simulator ( 2 % % % % 2% * % % 4 56

* Corresponding author: [email protected]

© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). E3S Web of Conferences 195, 01022 (2020) https://doi.org/10.1051/e3sconf/202019501022 E-UNSAT 2020

% ( 5' 6 2.4 Soil-water characteristic curve : 2 M 5"A006 % Table 1. Comparison of rainfall intensity classification. Department of Irrigation & Yakubu et al. [7] Drainage, Malaysia, 2017 % 2 B % + 8C 8! Rainfall Rainfall Rainfall Rainfall "A00 % Intensity Intensity Intensity Intensity Classification (mm/h) Classification (mm/h) % + $ - "A00 Low <4 Light 1 < I < 10 Medium 4 < I <15 Moderate 10 < I < 30 High 15 < I < 30 Heavy 30 < I < 60 Very High 30 < I < 60 Very Extremely >60 >60 Heavy/Extreme High

2.2 Rainfall analysis + ; ) < = = $.9 > ' 5>'6 2 % "A00 2 Hydraulic Conductivity function % % 0.0006 0.0005

2.3 Soil physical characterization 0.0004 ' 0.0003 ; 0.0002

; 2 2 = 2 (m/sec) X-Conductivity Water

./ % 0.0001 2 0 --/8 0.01 0.1 1 10 100 1000

$8 + Negative Water Pressure (kPa)

> B %)* ?

2.5 Physical model 2 % + 3 2 4 %$ 5 6 -

" * % ?

56D 5$6" 5-6A

2 E3S Web of Conferences 195, 01022 (2020) https://doi.org/10.1051/e3sconf/202019501022 E-UNSAT 2020

2 %2 % % & % 2D 2 2 % ( 2 % 3. 5BA,6% 5&./ 6 % % 5+ /6 % ) % & F. % % BA,% ( .: 2 = 9. 3. 2 % ) 2 56 % % BA, % 2 % , 2 4 % % % * 2 2 % 5%6 2 2"' 2D3. 5%6 + 2 2"' 2D9. ) 2"' 2D $. % :% ) / ?.M$.H 2"'$ 2D$ 3 ' ( 2"'- 2D- % 2 $$ 2"'3 % 2D3 / % ( -. H 2 9. 5+ /%6 2"' 2D $. % / 2 .. ' 2"'$ 2D$ $ 2"'- 2D- 9 % ) 2'3 2D3 / % 8 ) A 8 52D6 2 % 2D >08/ ( 3 Results and discussions ?F &? &: 2 B=$ 82 ' % 3.1 Rainfall data analysis "A2/ 2 82 % :.88 3.1.1 Direct adoption ) 2 / ) $.9 ) D , % ' 5' 6

3 E3S Web of Conferences 195, 01022 (2020) https://doi.org/10.1051/e3sconf/202019501022 E-UNSAT 2020

F H F 2 2% ; 5" 6 - / # $.9 / " % 5+ ?6 2 ?.H.

3.1.2 Rainfall analysis 2 + 9 < % 8 8 ! .I % $.9 %% D 8 2.1 Physical modelling 2 % ( % 3.: 9.: $.H52% $6+ F3 % 2 $.9>' D $.H ! 3.: % $ D $.H % 2 9.: 2 2 % % * 2D 2"'2 % %& %% $.H98 . %$38 % * 2D 2"' I % 2 % % +2D 2"' 5+ :6 K $ 2D 2"'-K3 + L'0 '" % ! 2"' % ) L'0 '" 2 L'02DK$ 2D-K3 2D-K3 % % ! .I % $.9 % * 2

4 E3S Web of Conferences 195, 01022 (2020) https://doi.org/10.1051/e3sconf/202019501022 E-UNSAT 2020

2 % 0 $ 0 $ %

= 0 4D $.H 3.:

= $

= 0 $4D $.H 9.: ( $ 9.: $. H L 0 4 % D $.H 3.: 2% - * ( 3$/ - +

Volume of Mass Time of Case Wasting failure. (cm3) (h) Case 1 4.25 4 Case 2 10.0 2

2 ) ! L 0 $4D - $.H 9.: :/

5 E3S Web of Conferences 195, 01022 (2020) https://doi.org/10.1051/e3sconf/202019501022 E-UNSAT 2020

) References 2 ! 1. F. T. Tangang, L. Juneng, E. Salimun, K. Sei, & M. 8 % * Halimatun. Sains. Malays., 41(11), 1355-1366 (2012) - 3$/ 2 2. J. Suhaila, A. A. Jemain. Theor. Appl. Climatol., % % % 108, 235-245 (2012) 2 % % % 3. H. I. Ling, M. H. Wu, D. Leshchinsky, & B. % ' Leshchinsky. J. Geotech. Geoenviron. 135(6), 758- $ .. 767 (2009) - + $ 2 4. A. Tohari, M. Nishigaki, & M. Komatsu, (2007). J. ) Geotech. Geoenviron. 133(5), 575-587 (2007) 5. I. Abudi, G. Carmi, & P. Berliner, (2012). J. Hydrol. ! 454, 76-81 (2012) - /: ) 6. S. N. Mhaske, K. Pathak, & A. Basak, Catena, 172, 408-420 (2019) 2 ! 7. M. L. Yakubu, Z. Yusop, & M. A. Fulazzaky, - 8 % * Hydrolog. Sci. J. 61(5), 944-951 (2016) 8. M. Azmi, S. A. M. Yusoff, M. H. Ramli, & M. A. .. -2 L'0 Hezmi,. J. Geotech. Eng. 21, 6987-6997 (2016) 2 9. Meteorology, M. D. O. 2016. 2016 Annual Report % Department of Meteorology 2016 ed. Malaysia: % % Department of Meteorology. 2 % % % %

4 Conclusion 2 % % 8% ) % < % 8% 2 L'0 L'0 % 2D 2 L'0 ! 2 % L'0

Acknowledgements 2 1 " ' & 1"'5D16 ..H=)A)'H?3$39 1 2 ' 1"'-.3H=)A)'H9./.3.:H1.: