Real-Time Construction Site Safety Risk Detection for On-Foot Building Construction Workers Using RFID

Real-time Construction Site Safety Risk Detection for On-foot Building Construction Workers Using RFID

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Nabeel Ali Mahmood

Graduate Program in Civil Engineering

The Ohio State University

2016

Thesis Committee:

Dr. Tarunjit Butalia, Advisor

Dr. Rachel Kajfez

Dr. Charles Toth

Nabeel Ali Mahmood

2016

Abstract

The construction industry is deemed to be among one of the most dangerous industries for on-foot workers because of the unique nature and uncertainty of its environment. Although the construction industry is regarded as one of the main pedestals of economic development, the current safety evaluation solutions have not been developed well enough to adequately control safety risks. The large number of detrimental accidents indicate the need for replacing incomprehensive, inconsistent, and lagging risk evaluation approaches with a more consistent, uniform, and reliable technological solution for identification, prioritization, detection, evaluation, and control of safety risks. Radio Frequency Identification (RFID) based real-time risk detection solution for early identification and evaluation of predictable individual triggering risks presented in this research goes beyond the state-of-the-art of proximity sensing for safety risks. This study systemizes basic triggering risk events and uses fuzzy logic to play a key role of hosting the subjective linguistic risk evaluation values into computational values. Nevertheless, construction risks are not only limited to triggering risks as on-foot workers may be concurrently affected by several risks. The future work could achieve a real-time safety risk assessment system for the evaluation of the combined effect of comprehensive risk drivers concurrently being exposed to on- foot construction worker. This could help in systemizing the basic triggering, basic enabling, and conditional risk drivers into a fuzzy fault tree model being a holistic prognostic approach for real-time risk assessment intelligent system that could be integrated with the methodology of real-time interactive risk detection. The real- time evaluation solutions make risks more recognizable and measurable for on- foot workers at the time of exposure to enhance adequate responses and proactive decisions for risk control, accidents prevention, and health protection. ii

Dedication

Dedicated to the souls of all the innocent casualties from the past and future.

iii

Acknowledgments

I acknowledge the continuous support of my advisor, Dr. Tarunjit Butalia, advisory committee members, Dr. Rachel Kajfez and Dr. Charles Toth, and my family who enriched my potentials and enhanced me to explore.

Vita

January 2011...... Civil Engineering B.E.

December 2011 ...... Engineer Intern

August 2013 ...... LEED Green Associate

November 2013 ...... Project Management Professional

My key qualifications are the consolidation of engineering and leadership skills attained from working with professional and international organizations dealing with civil engineering and management disciplines. Moreover, I have a unique life story that drives my passion to strive for success in all my life events and projects.

Fields of Study

Major Field: Civil Engineering

Table of Contents

Abstract ...... ii

Dedication ...... iii

Acknowledgments ...... iv

Vita ...... v

Table of Contents ...... vi

List of Tables ...... ix

List of Figures ...... x

List of Abbreviations ...... xii

1 Chapter One: Introduction ...... 1

1.1 Introduction ...... 1

1.2 Statement of Problem ...... 5

1.3 Scope and Limitation of Study ...... 7

1.3.1 Objective of Study...... 7

1.3.2 Scope of Study ...... 8

1.4 Value of Research ...... 10

1.5 Research Outline ...... 13

2 Chapter Two: The Methodology for Safety Risk Detection Using RFID ...... 15

2.1 Methodology Outline ...... 15

2.1.1 Introduction ...... 15

2.1.2 Methodology Principle Steps ...... 15

2.2 The Methodology for the Analytical Research ...... 16

2.2.1 Introduction ...... 16

2.2.2 Risk Detection Process Design ...... 16

2.2.3 Risk Detection Intelligent System Research Methodology ...... 19

2.2.3.1 Introduction...... 19

2.2.3.2 Accident Causation Analysis ...... 19

2.2.3.3 Risk Drivers Systemization ...... 20

2.2.3.4 Risk Analysis ...... 22

2.2.3.5 Risk Detection Intelligent System ...... 24

2.2.4 Risk Detection Method Design ...... 24

2.2.4.1 RFID Technology Compatibility Analysis (State-of-the-art) ...... 24

2.2.4.1.1 Radio Frequency Identification (RFID) Overview ...... 24

2.2.4.1.2 RFID Technology Use in Risk Management ...... 27

2.2.4.2 Real-time Risk Detection Method Design ...... 32

2.2.4.2.1 Introduction ...... 32

2.2.4.2.2 Risk Prioritization ...... 33

2.2.4.2.3 Risk Detection ...... 46

2.2.4.2.4 Risk Evaluation ...... 48

2.2.4.2.5 Risk Control ...... 49

2.2.4.2.6 Detection Method Summary ...... 51

3 Chapter Three: Systemization of Construction Safety Triggering Risks ...... 52

3.1 Introduction ...... 52

3.2 Domino theory ...... 54 vii

3.3 Systemization of Construction Safety Risks ...... 56

3.3.1 Introduction ...... 56

3.3.2 Basic Triggering Construction Risk Events ...... 58

3.3.2.1 Basic Triggering Risk Events Classification ...... 58

3.3.2.1.1 Falls, Slips, Trips Risks ...... 61

3.3.2.1.2 Struck by/Caught in Falling Objects Risks ...... 66

3.3.2.1.3 Struck by/Caught in Moving Objects Risks ...... 68

3.3.2.1.4 Exposure to Harmful Substances or Environments Risks ... 70

4 Chapter Four: Simulation of Risk Detection Method ...... 74

4.1 Introduction ...... 74

4.2 Detection Method Simulation Program ...... 74

4.2.1 Simulation Program Process ...... 74

4.2.2 Simulation Program Algorithm ...... 75

4.2.3 Simulation Program Cases ...... 79

5 Chapter Five: Conclusion ...... 94

5.1 Summary and Conclusion ...... 94

5.2 Recommendations ...... 97

5.3 Future Work ...... 97

6 References ...... 99

viii

List of Tables

Table 2.1 Basic Risk Values According to Severity and Likelihood of Harm ...... 39

Table 2.2 Subjective Description of Risk Likelihood Values ...... 40

Table 2.3 Subjective Description of Risk Severity Values ...... 41

Table 2.4 Fuzzy Sets Truth Values Membership Functions for Basic Risk ...... 43

Table 2.5 Area Under a Curve Range Limits Linked with Truth Values ...... 49

Table 2.6 Defuzzified Risk Linguistic Values Declared from Intelligent System . 50

List of Figures

Figure 2.1 Diagnostic Risk Detection Process ...... 17

Figure 2.2 Process Design for the Prognostic Risk Detection System ...... 18

Figure 2.3 Process of Top-down Systemization of Risk Drivers ...... 22

Figure 2.4 The Fuzzy Logic Role for the Computation of Risk Values ...... 23

Figure 2.5 Baldwin Model Approach for Truth Values ...... 44

Figure 2.6 Fuzzy Sets Truth Values Graphs for Basic Risk Values ...... 45

Figure 2.7 Interaction Process between Objects with RFID Tag and Reader ..... 47

Figure 2.8 Process of Interaction between the System Components ...... 47

Figure 2.9 Fuzzy Logic Role for the Computation of Risk Values ...... 48

Figure 2.10 Process of the Interaction between Different System Components . 51

Figure 2.11 Process of Interaction of RFID Tag and Solution Elements ...... 51

Figure 3.1 : Heinrich’s Domino Theory ...... 55

Figure 3.2: Modified Heinrich’s Domino Theory ...... 56

Figure 4.1 Process of Interaction between Different System Components ...... 75

Figure 4.2 Programed Method Algorithm Flow Chart ...... 78

Figure 4.3 First Step in Risk Prioritization by Inspector, RFID Tag Defining starts with identification of Risk Subgroup ...... 81

Figure 4.4 Selecting the Risk type by Inspector for the Identification of Basic

Triggering Risk Event ...... 82

Figure 4.5 Selecting Risk Value by Inspector for the RFID Tag to be defined .... 83

Figure 4.6 As Risk Parameters are Set, the Inspector would Fetch from Database to Define RFID Tag ...... 84

Figure 4.7 Inspector would Push the Define Button if the Fetch Database

Indicated the Tag was Not Defined Previously ...... 85

Figure 4.8 RFID Tag is in worker range, Information is Received by Detection

Intelligent System on Right Side ...... 86

Figure 4.9 The Detection Intelligent System keeps a record of all Risks Detected by Worker during the Day ...... 87

Figure 4.10 One of the Risk Values Detected shown as Rotation Fuzzy Set ..... 88

Figure 4.11 One of the Risk Values Detected Shown as Rotation Fuzzy Set ..... 89

Figure 4.12 One of the Risk Values Detected Shown as Rotation Fuzzy Set ..... 90

Figure 4.13 One of the Risk Values Detected Shown as Rotation Fuzzy Set ..... 91

Figure 4.14 One of the Risk Values Detected Shown as Rotation Fuzzy Set ..... 92

Figure 4.15 One of the Risk Values Detected Shown as Rotation Fuzzy Set ..... 93

List of Abbreviations

RFID Radio Frequency Identification

RF Radio Frequency

UHF Ultra-High Frequency

OIICS Occupational Injury and Illness Classification System

RBS Breakdown Structure

BLS U.S. Bureau of Labor Statistics

NIOSH National Institute of Safety and Health

OSHA Occupational Safety and Health Administration

ILO International Labor Organization

WHO World Health Organization

HSE Health and Safety Executive

OSH Occupational Safety and Health

WHS Workplace Health and Safety

PPE Personal Protective Equipment

FFTA Fuzzy Fault Tree Analysis

FTA Fault Tree Analysis

xii

1 Chapter One: Introduction

1.1 Introduction

The construction industry is regarded as one of the main drivers of

economic development. However, the construction industry is deemed to be one

of the most hazardous industries. The unique nature and safety uncertainty of

construction environments make the construction industry one of the most danger

prone industries. The construction sector is recognized for having one of the worst

safety history records among all industries. Many building construction activities

are inherently risky to safety and health of on-foot workers. Construction work may

involve working at heights, working around moving objects, working around

hazardous substances, or even working around a combination of several hazards;

construction site may constitute complex work environments with multiple health

and safety risks (NIOSH, 2004; BLS, 1992-2014; ILO, 2005; Jason, 2008).

In spite of remarkable improvements made in the health and safety

performance for reducing safety risks on construction sites by awareness, rules,

regulations, and specifications as well as interactive solutions, still there are

unusually high number of injuries. High construction accidents trends indicate that

1 there is still an essential need to improvise solutions to protect the health and safety of on-foot workers at construction sites (Minchuk et al., 2009; Rajendran et al., 2008).

A construction project is characterized by its diverse operations and multitude of participants that are mostly working together for the first time.

Nevertheless, even though construction workers are performing specific construction activities, they are exposed to more risks than workers associated with other occupational industries. Construction comprises of a wide range of unique and uncertain activities (Hinze & Applegate, 1991; Lezotte et al., 2006; The

Center for Construction Research and Training, 2013).

Fatal and non-fatal accidents occur on work sites as a result of different risks mainly associated with the construction site environment, negligence of workers, insufficient personal and site safety measures. Construction workers may be exposed to different types of site hazards, such as falling from various elevations, struck by moving or falling objects, electrocutions, and many other hazards.

Similar to most occupational industries, safety risks with the likelihood of harm at different severity levels will not cease to exist in the construction industry.

Causes of construction site accidents have been analyzed and evaluated by several research studies (Hinze et al., 1998). It was found that among the key factors of construction accidents is deficiencies with risk management including

risk detection, evaluation, and assessment of constructed site safety risks (Haslam et al., 2005).

Construction accidents are not just a simple fault of injured worker; instead, it is a combination of causes and effects of different factors. Risk identification, analysis, and evaluation are necessary steps for construction safety. Safety risk assessment is comprehensive evaluation of potential identified risk events that are present or could occur at any point in time or space. Identification and structuring

are performed through risk analysis. While risk management is the set of strategies and procedures to control the risk level and to mitigate its consequences; risk

management is essential for minimizing or even eliminating safety risks. However,

reliable risk detection and assessment are essential to ensure risk recognition,

correct selection and implementation of risk management procedures (Jannadi

and Almishari, 2003; Dvir et al., 2002).

Nevertheless, the construction industry lacks reliable methods for safety

risk detection and assessment. Furthermore, construction safety can be complex

to the extent that the safety evaluation could be unpredictable by traditional

measures. Radio Frequency Identification (RFID) is among the most important

technologies that could be applied to safety evaluation due to its unique features:

non-contact automatic identification, ability to access and track multiples of RFID

tagged objects, and the ability to operate without having a direct line of sight. Thus,

in this research, real-time RFID technology for early identification and evaluation

is enhanced using fuzzy logic for quantifying the predicted risk evaluation values.

During construction, safety risk is generally assessed in a qualitative

linguistic fashion based on the subjective judgment of supervisors, engineers,

inspectors, owner's representatives, or other construction safety representatives.

It is unrealistic to make assumptions of crisp numbers for such uncertain situations.

Therefore, in an effort to develop a construction risk evaluation model to bridge the

gap between qualitative safety risk perspective and scientific risk management

approaches; fuzzy logic is introduced to play a key role in converting subjective

linguistic values into representative computational sets (Zadeh, 1965).

Fuzzy sets are used for risk input values to have more realistic evaluation

values than what the crisp numbers reflect. The fuzzy logic is used as the input to

the analysis; in turn, leading to more accurate risk emulation values. Conveying

the realistic risk evaluation values in real-time could lead to adequate responses

to control the risk level and to mitigate its consequences. Then the use of fuzzy

logic will be inevitable for the practicality of construction site risk evaluation (Zadeh,

1975; Zadeh, 1983).

This research achieves a real-time safety risk detection system for

evaluating risk events at building construction site for on-foot worker. It adds to the

state-of-the-art by developing an intelligent system that communicates risk

identities, types, and levels, exceeding the current detection solutions which can

only detect the proximity of objects. The methodology of real-time detection is

based on technology interaction for real-time identification of construction safety

risks, making safety risks more recognizable for on-foot workers at the time of risk

exposure. The risk detection values can assist in establishing proactive decisions

for the control of risks and protection of health. Moreover, this research is part of the overall goal to integrate a real-time interactive risk detection methodology with an analytical prognostic intelligent system risk assessment model that combines the risk values of major concurrent risk drivers at the time of exposure. The future work can include the development of an assessment model in which an intelligent system analyzes the driving factors contributing to safety risk assessment using fuzzy fault tree analysis method. Based on the risk analysis factors, risk assessment approach can be used to determine the combined degree of health and safety risk exposure for construction workers.

1.2 Statement of Problem

The lack of real-time visibility, indication, information about the site risks types and levels may put construction workers in dangerous situations (Hinze and

Teizer, 2011). Although, a review of the state-of-the-art of construction industry shows the use of technology for risk management, there is a limited use of technology application for construction risk evaluation. RFID systems are being widely researched for construction applications to achieve cost, time, space, and waste reductions through better managing construction site aspects, e.g., inventory, logistics, tracking, inspections. Tracking of objects is improved by RFID

which increases the certainties and information availability to mitigate operational

risks (Jaselskis and El Misalami, 2003; Caldas et al., 2006).

RFID applications promote construction safety through decreasing struck

by risk (Ruff, 2001; Ruff, 2004a, Ruff, 2004b); however, RFID applications are

limited to providing real-time proximity sensing alerts (Marks and Teizer, 2012).

The existing real-time detection solutions exclude providing individual risk-specific information and the combined effect of the risks when presented concurrently. The problem which this study is attempting to resolve is the lack of identification and evaluation of the individual risks within real-time risk detection solutions. The RFID

technology solution for real-time risk detection could be effective in providing early

identification and evaluation of construction safety risks, making safety risks more

recognizable and measurable for on-foot workers at the time of risk exposure.

However, it could be useful to integrate the RFID sensing system with an intelligent

system that can analyze and evaluate the detected risk information.

It is important to assure the consistency and comprehensiveness of

identified risks, and hence this study provides systemization of the basic triggering

events. Furthermore, this research is an effort to bridge the risk management gap

constituted from misinterpreting qualitative safety measurements by using fuzzy

sets concept for quantifying the truth values of risk evaluation. Safety risk levels

are qualitatively measured by most safety engineers because of the difficulties of

assigning crisp number for risk events. Therefore, fuzzy intervals are used to

convert subjective linguistic values into representative computational sets which

leads to more accurate and consistent risk emulation values. Ultimately,

determining and conveying site safety risk values in real-time to construction site stakeholders could have great impacts toward improving construction safety

evaluation and accident prevention.

1.3 Scope and Limitation of Study

1.3.1 Objective of Study

The main objective of this study is to achieve the concept of real-time safety risk detection method toward prompting better response measures for accident prevention. The principle objective remains to attain early identification

& evaluation of predictable risk making risk more recognizable & measurable for on-foot worker at time of exposure using reliable & consistent technology. The following points summarized some of the limitations of the scope:

• Who: on-foot worker

• Where: building construction site

• When: time of exposure

• What: basic triggering risk events, types and levels

• How: prognostic intelligent method & Fuzzy logic using RFID

1.3.2 Scope of Study

The scope of this research is to achieve the concept of real-time safety risk

detection system for on-foot workers at building construction sites. The

fundamental focus of this research is the method design and projection of real-

time detection of individual triggering risk using RFID technology. The ultimate

scope of the future research succeeding this research can be to achieve a real- time safety risk assessment system for evaluating the combined affect of risks and safety events concurrently being exposed to the construction worker. The future

work could design and examin holistic approach for real-time risk assessment

system that integrates the methodology of real-time interactive risk detection with

prognostic analytical risk assessment intelligent system model based on fuzzy fault

tree analysis.

For this research, the real-time risk detection methodology is RFID

technology based solution for achieving the identification and evaluation of

predictable triggering risk. The detection solution alerting on-foot building workers

at the time of risk exposure could make risk more recognizable and measurable.

The proposed risk detection solution can have an intelligent system model being the link for interpreting the RFID tag’s risk information to be delivered to the worker.

The intelligent system could deliver the individual risk value without further holistic risk assessment process for the combined effect of all interacting risk drivers.

This study is focused on the worker interaction with triggering risk events.

Triggering risks caused by the construction site environment which excludes the enabling risks caused by the workers characteristics. The value of the detected risk could be defined, as per the risk detection method design, as fuzzy set of linguistic value evaluated by the inspection engineer according to relevant judgments, standards, and specifications. Moreover, this study does not attempt to promote or analyze certain specifications or standards of construction risks or safety.

As the main scope of this study is to achieve consistent and reliable site safety risk detection model, basic triggering risk events are identified in the model without further classification. For example, if there exist a triggering risk of struck by moving equipment, the further classification and the specifications of the equipment are beyond the scope of this study.

To achieve the research scope, the following questions are answered in the chapters of this study:

1. What is the methodology for achieving the real-time risk detection solution

using RFID based technology?

2. How does real-time risk detection methodology define roles of on-foot

construction workers as an end user and the role of safety inspectors?

3. How could fuzzy sets represent subjective linguistic risk evaluation values

to be delivered to the worker?

4. What are the principle triggering risk events affecting health and safety of

on-foot workers at building construction site?

5. How does the real-time identification and evaluation of risk assist in risk

control?

6. What are the recommendations for enhancing the use of real-time risk

detection?

1.4 Value of Research

Despite the continuously changing and evolving construction working

environments and conditions, safety risk identification is generally performed

manually (Toole, 2002) in current construction industry without effective

technological tools. Consequently, the risk evaluation and assessment values are

inconsistently subjective and are not delivered promptly to combat safety risks

effectively (Hinze and Godfrey, 2003). Furthermore, the state-of-the-art

demonstrated in this research discusses several technology-based solutions that are limited to proximity detection without having integrated solutions to deliver real- time risk detection or risk assessment. This research proposes a RFID technology based solution for real-time interactive detection for early identification and evaluation of construction safety risks, making safety risks more recognizable and measurable for on-foot workers at the time of risk exposure. The early detection of

10 construction site risk levels is essential for having appropriate risk controls and response measures.

In order to build risk evaluation model for the occurrence of risk events, the way in which these events come into existence must be studied. By referring to the domino theory, this research sets an effort for interfacing the risk detection methodology with accident causation model. The importance of this effort recognized by several researches. Interfaces between risk evaluation techniques and accident causation models could be helpful in analyzing the causal factors and conditions affecting the possibility of accident occurrence. Furthermore, this research is an effort to bridge the risk management gap constituted from misinterpreting qualitative safety regulations and measurements by using fuzzy sets concept for representing risk evaluation truth values (Khanzode et. al., 2012).

The system could be utilized as an evaluation tool to gauge the progress and effectiveness of risk control programs by detecting risk at different instances of time and space. By pointing out in real-time the risks that need safety controls and preventive measure considerations, the safety management could use this solution in order to manage and calibrate health and safety programs implemented at construction site. Furthermore, the real-time risk detection system could be used by safety managers for safety sampling, meaning that the safety managers could inspect the site using the risk detection solution and keep a record of observed risk values for further evaluation. This could help to identify weak zones or less protected areas at construction sites. Thus, having a reliable risk detection tool will

11 support decision makers to take suitable essential provisions to control risks and prevent accidents. Ultimately, a more reliable and consistent real-time risk detection system will improve overall construction performance of safety and health records will result in humanitarian, economic, and social benefits.

Although the work developed in this research is intended to be a prognostic tool for risk detection, it may have secondary uses for accident and near miss incident investigations. Typically, the accident site should be investigated while the conditions are uninterrupted. By having the real-time risk detection solution and after collecting the accident risk driver facts, the victim’s perspective of the before- the-fact safety evaluation could be regenerated. In addition to the risk detection benefit to the construction industry, the risk evaluation intelligent system could be utilized as an educational tool to show the interactions between different risks of construction sites and may enhance understanding of risk transfer to accident event leading to fatality or injury.

In the current construction industry, the combined effect of the several concurrent risks at a construction site is unrecognized or underestimated.

However, future work of this research could have an added value since it could provide real-time risk assessment values by computing combined effect of concurrent risks at the time of exposure, yielding a thorough risk assessment for on-foot workers. The future work to be developed could be a holistic approach accommodating a prognostic risk assessment intelligent system that can compute the combined influence of the risk drivers by fuzzy fault tree analysis. The fuzzy

12 fault tree analysis which is valued as a uniform method for structuring workplace risks, could be an adequate tool for elaborating the intelligent system accounting for the combined effect of the different concurrent risk events, i.e., triggering, enabling, and conditional risk events. Ultimately, interactions among various

construction site risk events through fuzzy fault tree analysis could yield a reliable and consistent system for computing combined risk values.

1.5 Research Outline

This section is dedicated to demonstrating the outline of this thesis document. Five different chapters constitute this thesis; each of the chapter’s

content is described as below.

Chapter One provides an introduction to the research through

demonstrating construction industry background, construction safety problems,

motivation and justification of the study. Scope and limitations are clarified for the

research topic of real-time construction site safety risk detection. Research

objective, questions, and validation approach are stated within the scope and

limitations section.

Chapter Two provides the conceptual and theoretical framework of the real-

time risk detection methodology using RFID technology. The methodology

highlights the solution process, requirements, components, methods, and

expected outcomes. The design for the detection system is pivoted by the

13 prognostic risk analysis process. The risk detection process can show the fuzzy logic intelligent system as the analytical brain of the research. Ultimately, the real- time risk detection method is designed with the following steps: risk identification,

risk prioritization, risk detection, and risk control. The interactions between the

physical and intelligent components of the solution are illustrated in each of the

detection method steps. The state-of-the-art is presented to verify RFID

compatibility for real-time detection method. The methodology also refers to the

research path followed in the further chapters for achieving the detection solution.

Chapter Three provides the classification and description of the

construction site triggering risk drivers to provide risk breakdown structure used in

the detection method. The chapter elaborates on the accident causality models

that build up to the basis of risk drivers.

Chapter Four simulates real-time risk detection solutions for the elaborated

method and the systemized risk. The chapter elaborates on the subjective

linguistic risk evaluation values represented by fuzzy set membership function

models. It describes fuzzy logic approach adopted for this research. The simulation

of the intelligent system of the solution shows the fuzzification and defuzzification

of risk values as part of the risk detection method.

Chapter Five states the conclusion, recommendations, and proposed

future work. It discusses the simulation program performance and results.

2 Chapter Two: The Methodology for Safety Risk Detection Using RFID

2.1 Methodology Outline

2.1.1 Introduction

The methodology of this research demonstrates the intended vision of the

real-time safety risk detection system using RFID technology, referred as the

“solution”. Methodology also explains research path for achieving the solution with

reference to the chapters of this study. As the solution proposed in this research

is the real-time risk detection system including the set of procedures and methods

to operate the system; therefore, this methodology highlights the solution’s process

requirements, components, methods, steps, and expected outcomes.

2.1.2 Methodology Principle Steps

As this research is an analytical research effort, it is essential to define the

principle steps for the risk detection methodology as in the following order. The

methodology steps are defined and explained further in this chapter.

(1) The process design for the risk detection system.

(2) Developing risk detection intelligent system through site safety risk analysis.

(3) The method design for the solution of the real-time RFID risk detection

system. This step also includes the solution’s assembly design.

2.2 The Methodology for the Analytical Research

2.2.1 Introduction

This section demonstrates the steps of design for the solution of the real- time RFID risk detection. The design for the ultimate system solution can be

initiated by the design of the risk detection process showing the flow of the

prognostic detection approach steps. The detection process design shows the risk

detection intelligent system as the analytical brain of the research; thus, it is

essential to discuss the subject of the risk analysis approach used to achieve the

intelligent system. Thereafter, the method design of the real-time risk detection

solution is elaborated with the complete vision about the interactions between the

interactive and intelligent components of the solution.

2.2.2 Risk Detection Process Design

Risk detection is the process of evaluating the effect of the exposure to the

identified potential risks associated with the construction site. Although the risk 16 detection processes found in literature vary because of different applications and approaches, there are principle components generally found in risk detection systems. In contrast to prognostic risk detection, Figure 2.1 shows the general components flow for the iterative diagnostic risk detection process.

Event Risk Risk Risk Risk Control & Facts Identification Analysis Evaluation Monitoring

Figure 2.1 Diagnostic Risk Detection Process

However, the process for risk detection systems is dependent on its

applications; prognostic risks detection process is different than diagnostic; also

real-time detection solution will require a certain unique process to be addressed.

It is essential to demonstrate in Figure 2.2 the process design for the principle

steps of the real-time prognostic risk detection solution of this research. The

process is mainly split into two phases; the first phase is the analytical efforts to

reach the risk detection intelligent system through accident causation analysis, risk

drivers systemization, and risk analysis. The state-of-the-art would be discussed

under the technology compatibility analysis showing the added value of this risk

detection research. The method design of the real-time risk detection system can

be elaborated and simulated with specific steps: risk prioritization, risk detection,

risk evaluation, and risk control.

Accident Risk Drivers Risk Risk Detection Causation Systemization Analysis Intellegent System Analysis

Technology Compatibility Analysis Method Design for Real-time Risk Detection

Detection Method Simulation

Inspected Risk Risk Risk Risk Prioritization Detection Evaluation Control

Figure 2.2 Process Design for the Prognostic Risk Detection System

2.2.3 Risk Detection Intelligent System Research Methodology

2.2.3.1 Introduction

Developing the risk detection intelligent system is a crucial part for reaching

the real-time risk detection solution. In order to develop the risk detection intelligent

system, this research will go through several steps ordered as following: accident

causation analysis, risk drivers systemization, risk analysis, and risk detection intelligent system programming.

2.2.3.2 Accident Causation Analysis

Considering that the risk detection intelligent system could be a model to evaluate and predicted risk as a function of influencing factors, it is essential to have causation theory that can be the backbone of the prognostic risk detection model. Causation theories could define the relationships between a set of variables of a certain event. Shown in Figure 2.2, accident causation domain analysis mainly

includes reviewing several sources of literature that integrate risk drivers into

accident causation theory. Accident causation theories could be set a framework

for the identification and systemization of risk detection drivers. Thus, the principle

risk drivers could be comprehensively derived from mapping the influencing risk

factor of the accident causation theories.

The selection of accident causation models should be based on the compatibility with the intended prognostic risk detection approach. The modified

Heinrich’s domino theory is explained in this research; the theory agrees with the concept that the occupational risk is not just a result of a simple fault of the deceased worker; however, it is the combination of causes related to triggering work environments, enabling human actions, and lack of safety controls and

precautions. Nevertheless, this research is exclusively dealing with the detection

of the basic triggering risks, i.e., individual construction site environment risks.

After setting the framework of the construction site safety risk drivers, this leads to

the next step in Figure 2.2 of risk drivers’ systemization.

2.2.3.3 Risk Drivers Systemization

The accident causation analysis sets the main framework of the risk driver

for this research as basic triggering risk events. Further risk driver’s systemization

requires the identification of the risk sources, situations, or acts that might lead to

risk at a building construction site. As implied by the accident causation theory, risk

drivers are not limited to the basic risk events; they also include the conditioning

risk controllers and dampers such as safety measures and precautions which

control the risk effectiveness. Nevertheless, basic triggering risk events would be

the exclusive principle risk driver for risk detection of this research.

A long list could be derived from literature stating the basic triggering construction risks drivers from physical, chemical, biological or psychological factors. However, in order to have a comprehensive risk driver’s systemization for the purposes of this research, the triggering risk would be structured based on a risk breakdown structure (RBS). RBS could categorize the risk drivers into risk events categories. The aim of the research was not to reach an exhaustive list of detailed risk events but to principally define the common and possible basic triggering risk events. This research recognizes the difference between the risk events and root causes of risk.

Several literature studies, codes, and historical statistical information could be used be achieve the classification of risk drivers to a systemized structure

(OSHA, 1990). A top-down methodology process will be used for the risk driver’s systemization. Meaning that the risk drivers defined from the causation theory could be decomposed into the top level categories of risk drivers prior to defining subcategories and the detailed risk events, Figure 2.3 shows the methodology

process for top-down systemization of risk driver. Studies and references could be

selected to contribute to the systemization of risk drivers to best fit the prognostic

triggering risk detection approach. Hinze et al. (1998) and OSHA’s Occupational

Injury and Illness Classification System (OIICS) (BLS, 2012) events and source of

injury structuring would be remarkable references for the identification and

classification of the triggering risks, i.e., construction site environment risks.

Risk Drivers Categorization of Decomposition Structuring Identified Principle Risk Events into Risk Events Risk Events

Figure 2.3 Process of Top-down Systemization of Risk Drivers

2.2.3.4 Risk Analysis

Risk analysis sets the steps to quantify and compute risk in a comprehensive and meaningful way. Several risk analysis approaches and methods are found in literature; this study will demonstrate risk estimation method

used for occupational risk analysis.

The outcomes of the risk driver’s classification and systemization chapter

could be used for the risk analysis as shown in Figure 2.2. In this research, the risk

estimation method would be using fuzzy logic for accommodating the subjective

linguistic values. The fuzzy logic approach will be essential for the inspected risk

prioritization indicated in phase 2 of the methodology; fuzzy logic could facilitate

the quantification and computation of the qualitative linguistic risk inputs.

Figure 2.4 shows the fuzzy logic role for the computation of risk detection

values. After the safety inspector assigns a linguistic risk value for each of the

defined risks, fuzzification process takes place to convert the linguistic values into

fuzzy set values of the Baldwin rotational model to be stored on the RFID tag.

Therefore, after the detection process takes place, defuzzification of the results

22 could be performed according to the Baldwin’s rotational model initially used for the fuzzification process. The defuzzified risk value is a linguistic value that could be communicated to the user to be alerted.

Inspected Risk Risk Detection Fuzzy Risk Risk Subjective Value Fuzzification Process Output Defuzzification

Figure 2.4 The Fuzzy Logic Role for the Computation of Risk Values

The future work will include fuzzy fault tree analysis as a deductive process to develop the interactions and relations between the risk drivers’ events at several levels in order to achieve prognostic risk assessment computation system. The assessment values for the fault tree lower, intermediate and top undesired risk events could predict the risk values. Fuzzy fault tree analysis is the approach adopted in future research to develop a systematic quantification and accumulation of the combined basic and conditional risks events, unlike this research that is only concentrating on the basic triggering risk events detection only. In the future research, the overall construction site safety risk assessment value can be calculated from the fault tree analysis using fuzzy algebra.

2.2.3.5 Risk Detection Intelligent System

Ultimately, the risk detection intelligent system could be programmed to be a reflection of the fuzzy logic detection approach. The intelligent system could also be compatible with the real-time detection technology approach by incorporated features, i.e., database. The intelligent system could properly receive the risk values to be analyzed, thus the risk process shown in Figure 2.4 could be

performed. The output of the automated intelligence system could achieve the

research objective of providing the detected individual triggering risk value in a

timely fashion.

2.2.4 Risk Detection Method Design

2.2.4.1 RFID Technology Compatibility Analysis (State-of-the-art)

2.2.4.1.1 Radio Frequency Identification (RFID) Overview

Radio Frequency Identification (RFID) is among the most important technologies developed in the last century. Among RFID’s main features, the non- contact automatic identification and the ability to access and track multiples of

RFID tagged objects (Hunt et al., 2007). In addition, the RFID system could work

in many different environments and conditions. For instance, RFID system could

24 operate without having a direct line of sight and can even transmit through several materials, and RFID can operate effectively in wide temperature range (Furlani and

Stone, 1999; Sandip, 2005).

The wireless use of radio frequency electromagnetic fields is the main

technology used to transfer data from tags to readers. The data transferred is the

electronically defined and stored data in the RFID tags. RFID tags act as

transmitters; they are powered by RFID readers when at connection ranges

through electromagnetic induction of magnetic fields. When RFID reader is in the

connection range of RFID tags, the RFID tag transponder is excited to emit ultra-

high frequency (UHF) radio waves. RFID tags are mainly found in two types,

passive RFID tags and active RFID tags. The main difference between the passive

and active tags is the reading range of the tag; active tags are generally readable

at longer ranges than passive tags. Passive RFID tags have short read range from

1cm to 5m (Bhuptani and Moradpour, 2005). On the other hand, active RFID tags,

using power source, such as battery, could transmit its data at long ranges up to

hundreds of meters.

RFID tags comprise of small microchip and antenna. Each RFID tag has a

unique product identifier stored in the tag’s limited memory; the unique product

identifier can be hyperlinked to several related additional information through a

database. The unique product identifier could be used as a key into the database.

Although the tags can be found as read-only or read-write, the tag data is stored

in the tag’s non-volatile memory. The RFID tag’s circuit basically collects the Direct

Current (DC) power from the incident reader signal in range, then, the RFID tag processes the received radio-frequency signal and transmits its memory data through the tag’s antenna. Ultimately, the RFID reader sends an encoded radio signal to interrogate the RFID tag within the reading range, then the RFID tag broadcast radio signals with its unique product identifier which could be received by the interrogator RFID reader. The reader could be connected to computer intelligence system having the unique product identifiers database information for further data processing.

Considering that an RFID system, i.e., tags and readers, may be active or passive, fixed or mobile, and short range or long range; RFID technology is applied in many disciplines. Tags could be produced as flexible small printed circuits even with adhesive backing; this could increase their practical use and reliability for many applications. The RFID system is being used to track inventory, products, assets, baggage, cargo, etc., by fixing an RFID tag on the object to be tracked.

Moreover, the RFID is used in many other application, such as access control, travel control, contactless payment, toll collection, transportation control, infrastructure locating and identification, etc. Several industries, e.g., manufacturing, transportation, agriculture, as well as the construction industry proposes that the use of RFID technology in mobile supply chain management could reduce tracking time and control cost (Ngai and Riggins, 2008; Wang et al.,

2006).

RFID technology has been widely applied in many areas in the construction industry; several applications have utilized the RFID systems to provide the construction management with information to empower operation (Jaselskis and

Tarek, 2003; Song et al., 2006). The tracking of equipment and material is improved with tagging objects with RFID tags and using proper frequencies to track them. For instance, underground utilities locating and tracking was examined using proper RFID systems.

In general, RFID increases the certainties and information availability which may mitigate risks; this makes RFID an attractive technology to be used in risk management. The following section presents several studies related to risk management application of RFID system.

2.2.4.1.2 RFID Technology Use in Risk Management

2.2.4.1.2.1 Proximity Warning Systems

Several studies investigate the proactive alerting when safety risk is presented due to worker’s proximity to the construction equipment. Ruff (2004a) evaluated several technologies applied for proximity detection and warning solutions; the study evaluated the detection range, precision, indoor and outdoor adequacy, calibration ability, feasibility, identification ability, etc. (Ruff, 2001; Ruff,

2004a; Ruff, 2004b). The applied technologies include Global Positioning System,

Radio Detection and Ranging, Ultra-wideband, Laser, Sonar, Camera Vision

Detection. Ruff found several benefits for the use of each technology; however, several limitations were found for each of the technologies used for proximity sensing systems. GPS was found to function at the outdoor sites but could not function indoors. Moreover, Global Positioning System was found to be not suitable for short range proximity detection. Laser and Radio Detection and

Ranging technologies had the limitation of not being able to distinguish on-foot worker from other objects and the limitation of only being able to detect object for short ranges. Although the Vision Detection technologies can identify on-foot workers from other ground objects, it had poor visibility in dark and dusty sites as well as it required high data process time. Some technologies were found to be infeasible or/and impractical to be applied for proximity warning at construction sites, such as: Ultra-wideband, Sonar, and Laser.

Marks and Teizer (2012) evaluated the effectiveness of the use of the radio frequency remote sensing technology to promote safety in construction. The approach was based on providing real-time detection and alert when on-foot workers are in proximity to the construction equipment. Experimental testing were designed and performed to evaluate the effectiveness of the proposed solution, experimental arrangements were set to represent interactions between workers and several construction equipment, e.g., drum roller, dump truck, excavator, etc., at short, medium, and long range.

The proximity sensing was tested in two scenarios, first, when the Radio

Frequency receiver device is held with the mobile ground worker approaching a static construction equipment, the second scenario is when the device was fixed on the mobile construction equipment approach a static worker. The radio frequency proximity detection system was successful in most trials for the detection of equipment within certain proximity and activating an audible alert that sufficient for the construction environment. The distance between the equipment and worker was measured in both scenarios; consistent measures were observed.

The system proved its ability to sense successfully and alert the worker in real-time when the construction equipment is in proximity within certain celebrated ranges. Thus, the tested radio frequency based solution has the potential to improve workers safety at construction sites with equipping workers with mobile solutions. Nevertheless, many parameters may potentially influence the system and limit its use. One of the major limitations of this study is the need for evaluating the calibration of the optimum proximity alert distances considering the operator and worker reaction time, equipment speed, equipment braking distances, etc.

Sensitivity analysis may be performed using a prototype of the radio frequency solution in order to come up with recommended calibrations of alert distances.

Furthermore, several factors may have an effect on the effectiveness of the system and needs to be further evaluated, such as: ambient temperature and climate, complex environments and situations at construction sites, radio frequency system mounting position and orientation, combinations of several types of risks, workers

29 acceptance to use the detection solution and shortcomings of the use of the radio frequency solution. Ultimately, in order to elaborate a detection solution, performance evaluation may require the collection of multiple observations for the solution in real life construction practice at different environments while recording

shortcoming, slips, errors, and limitations.

Teizer et al. (2010) stated that the Radio Frequency (RF) technology could

promote construction safety through decreasing struck by risk by providing real- time proximity sensing alerts for equipment operators, the RF tool could act as an

additional protection tool that is capable of performing in construction

environments. However, Teizeir found several limitations to the Radio Frequency

system, such as: power source required at all times to power the system, the

receiver must be installed at the receiving end, construction environment and

equipment conditions may have an impact on the system effectiveness, and

calibration of the alert distance must consider equipment type, reaction times and

brake distances.

2.2.4.1.2.2 Construction Site Objects Tracking

The RFID systems are used for construction applications to achieve cost,

time, space, and waste reductions through better managing construction site

aspects, e.g., inventory, logistics, tracking, inspections. Inventory tracking is one

of the widest RFID applications in the construction industry; inventory tracking

30 could be applied to equipment, tool, materials, etc (Duke et al., 2005; Paithankar,

2011).

Auto-ID Labs introduced the concept of smart shelves that is being used by several companies, such as Walmart, to track every item from cradle to grave in

their system; this reduces the out of stock times and provides better control of theft

and miss-managements. For instance, the smart shelves concept could be linked

to intelligent systems alert management to indicate the need for restocking when

certain products are low in stock (Fleisch et al., 2012). In the same manner, RFID

system is used to provide information on the quantity of materials and equipment

delivered. By tagging all materials by RFID tags and controlling the delivery routes

with RFID reader gates, all materials being delivered to the construction site could

be detected by the RFID receivers. Furthermore, RFID tracking systems were

examined for construction fabrication and manufacturing plants, Ergen et al. (2007)

used RFID and GPS for the tracking and locating components in a precast storage

yard with minimal workers input. The field tests using prototype system showed

the feasibility and practicality of the system for tracking precast parts in the storage

areas.

Yu-Cheng Lin (2008) examined enhancing construction facility

management using RFID and web technology. The case study indicates that the

RFID technology integrated with wireless devices could be useful tools for

empowering the construction inspections and maintenance process. The system

facilitated acquisition, tracking, and sharing location data and information while

31 overcoming time and space constraints; furthermore, the systems provides a base to estimate the progress of the facility maintenance.

Yen-Pei Chen et. al. (2013) studied the application of mobile RFID system

for safety inspection management at construction site. Using RFID-enabled mobile

smartphones system, RFID scanner plugged into smartphones could read safety

information at the checkpoint locations distributed at several site locations at

construction site, it was proven that RFID system is effective for improving the

attainment of safety inspections data records and real-time inspection reporting.

Ultimately, the use of RFID system increased the spread and speed of spread of safety information for further risk monitoring and control. The experimental results validated that RFID technology have potential to enhance jobsite safety.

2.2.4.2 Real-time Risk Detection Method Design

2.2.4.2.1 Introduction

The method for real-time risk detection using RFID will follow the process

defined in Figure 2.2: Inspected Risk Prioritization, Risk Detection, Risk

Evaluation, and Risk Control. The method design would technically explain the

components of the solution and the practical method for the effective use of the

solution while explaining the role of the participants in order to ultimately deliver

the real-time risk detection, and thus control the risks.

There are three essential prerequisites that should be present for the

solution to deliver the risk detection values as intended:

1) Pre-site preparation of the intelligent system and database to properly

receive and interpret predefined risk values of the RFID tags.

2) Site safety engineer inspecting and prioritizing the risk using the

predefined unique RFID tags.

3) The worker on site appropriately wearing the solution’s assembly with an

activated risk detection intelligent system.

2.2.4.2.2 Risk Prioritization

2.2.4.2.2.1 Inspected Risk Prioritization by RFID Tags Method

Risk identification and prioritization is the most critical links in the chain of

risk detection process. Risk identification and prioritization should be consistent

and correct so that the succeeding steps can be concluded based on correct information. In order to prepare for the real-time risk detection solution, the construction safety inspection effort shall be performed by safety engineers for identifying and prioritizing individual triggering risk events presented on site, it is noted that this study only includes the basic triggering risks knowing that there are

33 other crucial basic risks and conditional risks. The risk prioritization process by

RFID tags is proposed as a consistent process in order to deal with the subjective risk values of the individual risk inspections. For this research’s method, risk prioritization is established by the safety engineer tagging each individual of risk events with a unique RFID tag that represents the subjective risk value of each of the identified individual risks. This section discusses the method for risk identification and prioritization held by safety inspectors prior to using the solution by construction workers.

Inspected Risk Prioritization is the stage where the safety engineer could place the unique RFID tags on the physical source of the risk and defines the RFID tags in the intelligent system’s database; the following step of risk detection occurs when the RFID tag could identify and prioritize the risk to the system solution when the RFID reader is within a certain range. The safety engineer conducting risk identification and prioritization should be competent, qualified and possess the appropriate knowledge about the construction site activities being inspected. The following subsections attempt to clarify how can the safety inspector identify and prioritize the risk values of the RFID tag.

2.2.4.2.2.2 Risk Identification Inspection

In order to follow a consistent and systematic approach to identifying the

inspected risk events, safety engineer/inspector can refer to the triggering risk

34 events as systemized in Chapter 3. The eleven basic risk events are thought to assist the inspector as a systematic checklist to identify risk events on site.

Inspections for basic triggering risks should consider the risks input of this

research solution considering that each risk driver event is being evaluated

independently from any other risk event. It is important to note that this research

does not specify or limit the inspection techniques, specifications, legal

requirements, etc. Considering that the risk events evaluation by inspectors is in

subjective values, the identification of the individual risk events could be performed

by multiple techniques. In order to enhance the ability for locating the risk sources,

some inspectors might implement a breakdown of worksite by activities, work zones, subcontractors, etc. However, some risks might be associated with multiple tasks, thus inspectors should assure that duplicates were removed. Inspectors can

ask themselves questions as: How could risk come into existence? Which sources of risks, i.e., hazards, could lead to an injury?

The safety inspector analyzes the observed data according to knowledge, experience, and project specifications and regulations which lead to the identification of the risk, thus prioritization of the risk. The inspector may identify basic risk from hazards information. The inspector should keep in mind that a hazards could be source, situation or act, or a combination having the potential to harm construction workers. Inspecting the basic risk sources could be performed by collecting information from the construction site environment: materials, tools, equipment, tools, scaffolding, electricity, chemicals, etc.; as well as any aspects of

35 work interactions, conditions, and root causes that may lead to safety risks. Along

with the inspectors expertise for safety inspections and monitoring on regular

bases, variety of sources may provide information used in risk identification

process, some of which are listed as following: safety specifications and legislated

requirements, codes and standards, industry’s safety practices, organizational

procedures and policies, project specific safety and traffic plans, interviews and

surveys with construction site participants, near misses and safety accidents

history and records, lessons learned database, materials and products safety

manufacturer information and safety data sheets, tool’s operating and instructions

manuals, safety specific test results, previous risk identification and assessment

documents, expert opinions, etc.

Range of foreseeable conditions might change along the stages in

construction process. Moreover, the risks are not only related to routine activities

at the construction site. Risk identification is an ongoing process; therefore, safety

inspections for basic risk identification should be performed regularly and

systematically whenever new information becomes available about risks or change

in risk. For instance, safety inspection can be useful at the start, progressing, and

finish of activities, tasks, phases, work zones, use of tools and equipment,

subcontractors work, etc. Furthermore, inspection could be needed after unusual

events such as extreme weather event, system failures, safety incidents and

accidents, etc.

2.2.4.2.2.3 Inspected Risk Prioritization

2.2.4.2.2.3.1 Introduction

The individual risk events evaluation values are very important input for the risk evaluation process. After identifying the risk events, the safety inspector can evaluate the individual basic risk in order to define the RFID tag in the intelligent system’s database. Assigning values to the risk events is a ranking approach to prioritizing risks in a way to set their impact on the general safety risk.

Subjective linguistic judgment values are the approach adopted in this research method to determine the level of risks by the inspector. Safety inspectors can define subjective linguistic risk evaluation values through analyzing available information from several related sources as discussed in the previous section of individual risk identification. However, the inspector could analyze the risk sources to determine several factors related to the risk evaluation, such as: the level of compliance/incompliance to the regulations and legislative requirements, or even the certainty of the predicted conditions and their relevance to the identified risk.

Further sections of this research elaborate fuzzy sets solution to accommodate the subjective nature of the site safety risk evaluation values set by inspectors. A series of linguistic evaluation truth values for basic events are represented by the rotational model fuzzy set truth values membership functions.

The inspector could select the individual risk evaluation values from the truth values having predefined fuzzy sets as described in this study.

2.2.4.2.2.3.2 Basic Risk Prioritization

In an attempt to evaluate the seriousness of the individual basic risks, inspectors can look for answers of questions to evaluate the likelihood and severity of the risks. Questions to evaluate the likelihood of risk could be: How frequent is the risk presented? How frequent does the workers interact with the risk? How

does the frequency of the risk the affects the harm it may cause? Did the risk ever

cause safety incident or accident? Likewise, question to evaluate the severity of

risk could be as: What is the severity of the harm that could result because of the

risk? What type of harm could the risk cause? What type of body could the risk effect? What variables and factors could influence the severity of harm that may occur, e.g., height, distance, etc.? Could the risk be harmful in causing other

systems to fail? (Victoria, 2007; Australia, 2011; Phoya, 2012)

The basic risk events evaluation values, ultimately, depend on the likelihood

that harm from a potential source will be realized taking into account the severity

of harm. Some studies have suggested risk evaluation scales based on likelihood

and severity (Pinto, 2012; Kirchsteiger, 1999; Esmaeili, 2012; Campbell, 2008).

For the purposes of this research, the following suggests an approach for risk

prioritization by a series of truth values as in Table 2.1.

Severity Likelihood of Harm of Harm Rare Unlikely Possible Likely Certain Very Extremely Absolutely Absolutely Fatal Risky Risky Risky Risky Risky Very Extremely Absolutely Serious Risky Risky Risky Risky Risky Extremely Moderate Fairly Risky Risky Risky Very Risky Risky Slightly Fairly Minor Risky Risky Very Risky Risky Risky Absolutely Slightly Fairly Trivial Risky Risky Not Risky Risky Risky

Table 2.1 Basic Risk Values According to Severity and Likelihood of Harm

This study suggests that the risk evaluation values, shown in Table 2.1 could be estimated from the severity of harm and likelihood of harm values.

Several studies have elaborated qualitative and quantitative scales to estimate the likelihood and severity of harm values. This study suggests sets of subjective scales to be considered for estimating the likelihood and severity values. Workmed method have proposed a quantitative measure of risk based on numerical scale of frequency, severity, exposure, deficiency, and other factors. Workmed method had the advantage of being adaptable to perform risk evaluation for different occupations including construction. Workmed method have used qualitative description corresponding to each of the numerical ratings of risk factors.

Considering the use of fuzzy logic in this study, fuzzy set truth values could be subjectively described (Campbell, 2008; Pinto, 2012; Rameezdeen et al, 2013).

Likelihood factors estimate the occurrence likelihood of accident by using relevant priority factors. These factors could be estimated on site according to the inspector’s judgment of the most likely accident mode. The subjective value takes into account the workers exposure and the potential for construction site injury or illness. Table 2.2 represents subjective description of risk likelihood values.

Likelihood Description of Harm

Certain Certainly occur frequently

Likely Most probably occur expectedly

Possible Might occur occasionally

Unlikely Possibly occur unexpectedly

Rare Most probably never occur

Table 2.2 Subjective Description of Risk Likelihood Values

The subjective judgment of level of severity may require knowledge of

relevant information such as: body location of injury, nature of the harm, accident

mode, construction site environment and conditions, and features of work activity

performed. The safety inspector may project the accident’s harm severity by

analyzing several factors, e.g. speed, height, weights, related to the amount of

energy absorbed or dissipated by the worker’s body. Moreover, the human body’s

40 biomechanical limits can be used to estimate the severity of harm. Table 2.3

represents subjective description of risk severity values.

Severity Description of Harm

Fatal Death, injury or illness with permanent total disability Injury or illness with permanent partial disability, or temporary total Serious disability Injury or illness with temporary partial disability, medical or Moderate hospital treatment required Minor injury or mild illness without any disability, only first aid Minor treatment required without any medical or hospital treatment

Trivial Neither injury nor health problem

Table 2.3 Subjective Description of Risk Severity Values

2.2.4.2.2.3.3 Risk Values Fuzzification of Sets

Fuzzy logic is the logic in which the truth values are fuzzy subsets with

linguistic labels, labels such as true, very true, fairly true, false, very false, fairly

false, etc. The fuzzy set theory can be used when it is unrealistic to evaluate the

probability of occurrence of an undesired event by using a crisp value without

taking into account the uncertainties related to each of the basic events. Fuzzy set

theory proposed a logic shift from ordinary crisp sets of the binary logic of a

41 membership value of either zero or one, to a fuzzy set where each member is assigned its own membership value ranging from zero to one (Al-Humaidi and

Hadipriono, 2010; Weber, 1991).

Fuzzification is the process taking place when converting crisp values into fuzzy values with different uncertainty values. In other words, it’s the process of mapping from an input of crisp universe of discourse into the degree to which a value belongs in a fuzzy set that describes the membership of the fuzzy input variable from a fuzzy interval (0, 1) (Zadeh, L. A., 1968; Zadeh, L. A. 1999;

Zadeh,1975).

In a rotational model, a fuzzy set can be represented by a linear line or a nonlinear line, the line can be connecting one or two points often referred as rotational points. Baldwin model presents the rotational model using some specific linguistic hedges. In Baldwin’s model, all linguistic hedges are represented by the powers of membership function. For example, if the membership function is modiﬁed with the linguistic hedge ‘fairly’, then the values of the function fairly are represented by the square root of the base term function. Baldwin Model is shown in Figure 2.5.

Due to the rotational model’s compatibility and suitability to the subjective nature of the risk evaluation values, rotational model fuzzy sets will be adopted to describe the truth values of the risk evaluation. Each of the basic events, as concluded from chapter 3, could be represented by fuzzy set truth values implying subjective linguistic values that can be expected from the inspector. Ultimately, the

42 unique product identifier for the RFID tags can refer to a specific fuzzy set and a

specific basic risk event. The following shows a series of subjective linguistic

evaluation values represented by fuzzy set truth value membership functions, this

process is known as the fuzzification of values.

As only the true values of Baldwin model are adopted for this study. Fuzzy

sets membership functions of truth values for the basic risk events evaluation

values are shown in an equation function form in Table 2.4 and graphical function

form in Figure 2.6.

Basic Risk Linguistic Fuzzy Truth Value Membership Function Evaluation Value

Absolutely Risky X = 1 {0 ≤ Y ≤ 1}

Very Risky Y = X2 {0 ≤ X ≤ 1}

Risky Y = X {0 ≤ X ≤ 1}

Fairly Risky Y = X1/2 {0 ≤ X ≤ 1}

Absolutely Not Risky X = 0 {0 ≤ Y ≤ 1}

Table 2.4 Fuzzy Sets Truth Values Membership Functions for Basic Risk

Graphical Baldwin Approach to Fuzzy Truth Values 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2 Fuzzy Fuzzy (Y) Set Membership Value

0.1

0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Fuzzy Set Element (X)

ABSOLUTELY TRUE VERY TRUE TRUE FAIRLY TRUE FAIRLY FALSE FALSE VERY FLASE ABSOLUTELY FLASE

Figure 2.5 Baldwin Model Approach for Truth Values

Fuzzy Sets Truth Values for Basic Risk Events 1.0

0.8

0.6

0.4

0.2 Fuzzy Fuzzy Set Membership Value

0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

-0.2 Fuzzy Set Element

ABSOLUTELY RISKY VERY RISKY RISKY FAIRLY RISKY ABSOLUTELY NOT RISKY

Figure 2.6 Fuzzy Sets Truth Values Graphs for Basic Risk Values

2.2.4.2.3 Risk Detection

The construction worker can wear the risk detection solution which constitutes of the RFID reader with the real-time risk detection intelligent system.

The solution is suggested to be attached to the helmet of the construction worker.

The solution attached to the workers on site is interactive; the RFID reader interrogates the tags to detect risk information from the RFID tags through their unique product identifier. When the RFID tags are in the worker’s RFID reading range, the RFID reader could detect the unique ID of the tags and transmit the ID to the intelligent system in order to interpret the unique ID to the risk information related to the RFID tag. Figure 2.7 and Figure 2.8 shows the transmission of information between the RFID tags to RFID reader when there is a risk event in the range of the host of the RFID reader devise.

Figure 2.7 Interaction Process between Objects with RFID Tag and Reader

Data Tag Reader Reader Excites Tag Processed by Transfers Receives Intellegent when in Range Data Risk Value System

Figure 2.8 Process of Interaction between the System Components

2.2.4.2.4 Risk Evaluation

As the Risk Detection takes place, the RFID reader could transmit the risk values, i.e., unique product identifier, to the system’s intelligent system as detected from the RFID tag. The intelligent system can process the detected risk input by matching the unique product identifier of the RFID tags with the subjective linguistic risk level values as predefined in the database.

Risk evaluation is the step were the fuzzy risk output referred as unique product identifier is being defuzzified in order to declare the meaningful risk value as in Table 2.6.

Inspected Risk Risk Detection Fuzzy Risk Risk Subjective Value Fuzzification Process Output Defuzzification

Figure 2.9 Fuzzy Logic Role for the Computation of Risk Values

Figure 2.9 shows the fuzzy logic role for the computation of risk detection values. Defuzzification of the results could be also performed according to the

Baldwin’s rotational model initially used for the fuzzification process in the step of

“Inspected Risk Prioritizing”. Each of the fuzzy subsets can be identified by area under a curve or integration within domain limits as identified in Table 2.4.

Table 2.5 shows each of the truth values linked with the integration value range limits represented by the area under a curve.

Area Under a Curve Resulting Risk Range Difference Defuzzification Range Linguistic Value

0.00 – 0.083 0.083 Absolutely Risky

0.083 – 0.42 0.34 Very Risky

0.42 – 0.58 0.16 Risky

0.58 – 0.917 0.34 Fairly Risky

0.917 – 1.00 0.083 Absolutely Not Risky

Table 2.5 Area Under a Curve Range Limits Linked with Truth Values

2.2.4.2.5 Risk Control

As the intelligent system generates the linguistic risk evaluation value as

shown in Table 2.6; thus, risk control could be introduced. The individual risk level

evaluation could be declared in real-time to the worker, i.e., the host of the solution,

as a sound message in order to alert the worker about the site safety condition. A

49 complementary risk control sound message may follow the risk level value to recommend the best practice respond that is adequate to the risk level value.

No. Resulting Basic Risk Linguistic Evaluation Value

1 Absolutely Risky

3 Very Risky

4 Risky

6 Fairly Risky

7 Absolutely Not Risky

Table 2.6 Defuzzified Risk Linguistic Values Declared from Intelligent System

The construction site safety risk detection alerts on-foot worker about

potential basic triggering risk events that are present or could occur. As a part of

the risk management plan, safety statements could be beneficial for controlling and

preventing the identified safety risks during this process. The resulting defuzzified

linguistic value, Table 2.6, could be accompanied with a risk control action

message to manage the presented level of risk.

2.2.4.2.6 Detection Method Summary

Figure 2.10 and Figure 2.11 shows the overall interactions process between

the different components of the system. It is noted that Figure 2.11 shows an arrow

representing internal links between “the solution” components and a waveshape

representing the wave link between tags and reader.

RFID Tags Intellegent Evaluation & RFID Reader Defined for Risk System Control Values Risk Detection Prioritization Computations Declared

Figure 2.10 Process of the Interaction between Different System Components

Figure 2.11 Process of Interaction of RFID Tag and Solution Elements

3 Chapter Three: Systemization of Construction Safety Triggering Risks

3.1 Introduction

Occupational accident can be defined as any event which interrupts the

normal work process caused by human factors, situational and environmental factors, or any combination of these factors which may result in personal injury or

death. Safety risks associated with activities, processes, or projects derive from

several causes related to work site conditions, materials, equipment, labor, safety

conditions, and many other factors. However, the workers diverse and multiple risk

exposure at site contribution to the overall safety. In order to build risk evaluation

model for the occurrence of risk events, the way in which these events come into

existence must be studied. This implies the need for identification of factors that

cause these events and the way they interact with each other (Rahim et al., 2008;

Hu et al., 2011). Any effective risk management program must include four phase

process which are: identification, evaluation, responding, and monitoring of risks.

This chapter provides a general identification and systemization of the construction

safety triggering risks related to on-foot construction workers. Outcomes of this

chapter can be used for building risk identification structure for the risk detection.

Although many construction safety risk causation models depend on historical accidents recodes, academic sources or even expert opinions (McClay,

1989a; McClay, 1989b; Hinze, J., & Russell, 1995); these sources may bias the

results by considering only certain events and conditions. The selection of accident

causation models should be mainly based on the intended usage. Khanzode et.

al. (2012) states that interfaces between risk assessment techniques and accident

causation theories would be helpful in prognostic risk evaluation approaches. This

interfacing could assist in examining the domino effect leading to accident event.

Construction site safety risk assessment drivers used for risk analysis can be

reliably derived from a comprehensive accident causality model such as the

domino theory.

Domino Theory model fits the construction safety field as it suggests that

the occupational fatality is not just a simple fault of the deceased worker; instead,

it is the combination of causes related to triggering work environments, enabling

human actions, and lack of safety controls and precautions. Therefore, a

fundamental accident causation model is essential for safety risk analysis process

in order to highlight the main types of risk causal events and factors. However, the

domino theory is highlighted in this study to demonstrate the triggering risk aspects

of the accident causality. The domino theory as a whole is not adopted as a base

for this study, but the Modified Domino Theory could be the basis for the future

work of real time risk assessment research that takes into consideration the

combined effect of the various multiple risks.

3.2 Domino theory

The domino accident causation model is one of the earliest theories for risk analysis. Heinrich (1959) observed a series of interrelated factors that include: causes of unsafe acts and conditions, workers safety when interacting with equipment, safety management and controls, impact of safety specifications and regulations, and costs of accidents. The nucleus of the domino theory started with the two factor model; it stated that uncontrolled risk exposure and unsafe worker actions are both the immediate causal factors that must be present together for an accident occurrence.

Furthermore, Heinrich et al. (1980) investigated the conditions and circumstances that cause industrial occupational accidents. The accident causality model proposes that the occurrence of any accident injury results from a

completed sequence of factors of which the last is the accident itself.

The domino model includes five factors in a sequence ending with an injury

as shown in Figure 3.1. The first domino indicates that the social environment and

ancestry may reflect negative character traits, e.g., stubbornness, recklessness;

these traits along with lack of knowledge and skill may lead workers to occupational safety faults. Fault of person is the second Domino in the sequence,

e.g., human faults, misjudgments, and safety precautions incompliance. The

second Domino factors can be a direct predecessor for unsafe act or condition

54 representing the third domino. Thus, the accidents are invariably driven directly by the unsafe acts, e.g., worker running on loose scaffolding.

Ultimately, Heinrich’s theory mainly conveys that injuries of accidents are caused by certain preceding factors and causes driven by people’s behavior. The theory proposes that the highest safety payoff comes from breaking the domino effect by rectifying the worker’s behavior and eliminating unsafe conditions.

Environment Human Unsafe Event Accident Injury & Ancestry Fault or Condition

Figure 3.1 : Heinrich’s Domino Theory

The modified Heinrich’s domino theory, which could be adopted for the

future research, elaborated a modified domino sequence for accident causality.

Several modern safety researchers have modified Heinrich’s domino theory to account for the importance of accident control aspects in accident causation and prevention. Heinrich et al. (1980) modified domino sequence is shown in

Figure 3.2; the modified sequence considered the lack of safety controls as a

conditional cause of safety accidents. The incorporation of the event of “lack of

control” reflects the role of safety management and control system in the sequence

of accident causation. The modified Heinrich’s domino theory suggests that basic

55 cause, i.e., personal and job factors, could be in play only if sufficient safety controls were missing. Moreover, the causality sequence makes the immediate causes as the direct predecessor of accidents.

Basic Immediate Lack of Control Accident Injury Causes Causes

Figure 3.2: Modified Heinrich’s Domino Theory

3.3 Systemization of Construction Safety Risks

3.3.1 Introduction

Occupational safety risks of any unique construction site are related to human behavior, site conditions and environment, and poor safety controls; while the symptoms of these safety risks are reflected in the unsafe work methods and procedures, and failure to use safe tools and equipment. It is well known that any

construction project has a unique and dynamic nature; however, most of the

construction sites share the same basic causes of safety risks leading to accidents.

Supported by several literature studies, scholars, codes, and statistical evidence,

56 this chapter presents classifications for the reasonable and possible risk triggering

basic safety risk events which can be used for the detection system solution.

Risk identification requires the proactive determination of all possible

sources, situations, or acts that might exist at construction site. A long list of safety

risk related to physical, chemical, biological, psychological, etc., factors could yield

from risk identification. Therefore, in order to better manage the identified risks,

there should be a framework for risk structuring. Structuring the information

provides an effective presentation and organization of the outcome of risk

identification. In this study, risk will be structured based on the Risk Breakdown

Structure (RBS) technique. RBS is a common and very practical tool that groups the identified risk events into different levels following a bottom-up approach.

As the modified domino causation theory suggests that there are multiple causes of accidents; the potential safety risk causes are classified in this research as basic and conditioning risk causes. Basic causes are the primary failures leading to accident occurrence; basic causes could occur either alone or in combination with conditioning causes. Basic events can be categorized under either enabling events or triggering events. While, conditioning causes are related to the safety controls on site.

Enabling events include all the causes that are directly related to the worker’s internal safety risk sourced from attitude, health, and skill level of worker.

While, triggering causes include all the external, i.e. work environment and conditions related, safety risk that can affect the worker’s safety and health.

Triggering causes are resulting from conditions other than the enabling causes; triggering causes are related to external causes that precipitate the accident, such as overloading on the roof or scaffolding, strong wind impact, inadequate strength, slippery conditions due to rain, snow, etc. Support related causes is suggested in

literature studies as a separate basic causal accident category; however, because

this study is safety risk detection oriented, the triggering events group will include

all causes which are support related (Chi et al, 2012; Hadipriono, 1992a;

Hadipriono, 1992b; Hadipriono et al., 1995a; Cheng et al, 2012).

In this research, it is important to note that the basic and conditional event

categories are meant to be mutually exclusive independent events in terms of

definition and root causes. For example, fall from elevation risk event category

should not be used to represent a risk rooting from safety measures on site or even

lack of experience and training, because the fall from elevation risk event is

attributed under the triggering events classification of falls, slips, and trips risks as

explained in further sections.

3.3.2 Basic Triggering Construction Risk Events

3.3.2.1 Basic Triggering Risk Events Classification

Several studies viewed safety risks as technical or human error (Chi et al.,

2005; Murie, 2007). In this study, triggering risk are the type of risks resulting from

58 conditions other than the internal human causes; triggering causes include the

possible construction site environment risks.

Construction sites have a long list of significant and different types of

triggering risks that the workers are exposed to. Several previous studies have

elaborated different safety risk classification system based on statistical, historical,

and expertise. Some researchers have classified health and safety risks into two

categories: physical safety risks and the ill health risks (HSE, 1998).

Since the Occupational Safety and Health Administration (OSHA) has been

established in 1971, fatalities in accident cases are generally reported to OSHA

within 8 hours of the occurrence. Databases for workplace risks have been

developed, which yielded widely publicized data and studies on the primary causes

of construction fatalities. OSHA states that in 2014 private industry, about 900 of

4,385 occupational worker fatalities were in construction; this attributes more than

20% of worker deaths to construction. OSHA has indicated that fall is the leading

cause of fatal injuries in construction with about 6,900 fatalities between 1992 and

2010. Moreover, OSHA attributes the construction deaths to specific leading

causes referred to as fatal four for being responsible for more than half of the

construction worker deaths in 2014. Fatal four causes are sorted in descending

order starting from the most leading cause with the highest deaths percentage:

falls 40%, electrocution 8%, struck by object 8%, and caught in/between 4%. (BLS,

2014a; BLS, 2014b; BLS, 2014c; BLS, 1992-2014; OSHA, 2013a; The Center for

Construction Research & Training, 2013)

Huang and Hinze (2003) analyzed the Integrated Management Information

System database of OSHA for about 7,500 accidents that occurred in the U.S. construction industry from 1990 to 2001. Fall accidents were the leading cause of accidents with the percentage of the total falls being 36%. Huang and Hinze (2003) five primary causes of accidents are sorted in descending order starting from the most leading cause with the highest deaths percentage: falls 36%, struck by 24%, others 15%, electrocution 13%, and caught in/between 12%.

Most construction site accident researchers have agreed that falling from

heights accident mode has the most likelihood and severity to cause health and

safety consequences for construction workers (Huang & Hinze 2003). Poon et al.

(2002) suggested that accidents attributed to falling from heights and struck by

objects constitute more than half of the total accidents at construction sites.

However, OSHA developed Occupational Injury and Illness Classification System

(OIICS) using a consistent method for classifying occupational injuries and

illnesses statistics in a hierarchical structure.

Classification of the basic causes of the construction safety risk can be

elaborated using the OIICS of OSHA. The OIICS is a comprehensive coding

system that can be referred to for multiple purposes such as characteristics coding

of injuries and fatalities. OIICS is adopted in this research as a tool necessary for

developing the classification for basic triggering causes of construction site risks.

The OIICS manual issued by OSHA in 2012 indices four code structures

used to classify the occupational injury or illness circumstances as following:

60 nature of injury or illness, source and second source of injury or illness, part of body affected illness, and event or exposure. Each of the code structures is

hierarchically categorized into subdivisions and groups. Event or exposure

structures follow the manner in which the accident or safety risk occurs, while the

source of injury or illness and secondary source structure identifies the

substances, equipment, or any factors that may precipitate the event. In contrast,

part of body affected and nature of injury structures demonstrate the affected body

system and symptoms of the injury or illness respectively which could better fit in

a diagnostic context (Bureau of Labor Statistics, 2012).

Considering that this research is elaborating a prognostic approach for risk

evaluation; therefore, event and source of injury OIICS structures can be most

harmonic with this research to represent the triggering basic causes. The following basic triggering events classification for this research is elaborated from adjusting the event structure in OIICS version 2.01 and using Hinze et al. (1998) construction specific accident classification system classification in order to best fit the definition of the triggering causes for the purposes of this study. This chapter provides the breakdown structure and definitions for basic triggering risks.

3.3.2.1.1 Falls, Slips, Trips Risks

Several research studies performed in different countries, e.g., USA, China,

Taiwan, New Zealand, have concluded that falls from heights are the leading risk

61 events on construction sites (Bentley et. al. 2006, Yung 2009). Fall accident statistics usually have the highest rate for seriously hospitalized injuries which makes the fall accidents one of the most costly among all other accident modes

(Gavious et al., 2009).

Huang and Hinze (2003) listed several physical factors leading to construction site falls, the study found that falls from roof is the leading cause of falls having about 28% of the total falls, the following shows Huang and Hinze

(2003) breakdown structure of fall accidents with its percentage from the total falls.

• Falls from roof, 28%.

• Falls from ladder, 11%.

• Falls from scaffold or staging, 13%.

• Falls from structure, 19%.

• Falls into opening, 78%.

• Falls from aerial lift, 3%.

• Falls from platform catwalk, 2%.

• Fall from vehicle or construction equipment, 2%.

Moreover, Huang and Hinze (2003) provides percentage breakdown of fall

accidents with respect to fall height intervals as following:

• Fall from 0 to 10 feet, 23%.

• Fall from 10 to 20 feet, 28%.

• Fall from 20 to 30 feet, 22%.

• Fall from 30 to 40 feet, 8%. 62

Due to the high risk of fall, the roofing workers are three times more likely to have fatal injuries than any other workers in the construction industry (BLS,

1992-2014; NIOSH 2004). Although falls might have several direct or complex

reasons; in many cases, falls are the result of slips or trips. Slips and trips can be

the initiating cause of other types of fall accidents namely, fall from heights.

Szymusiak and Ryan (1982) defined the slips as the cause of the reduction or loss

of friction between the worker and supporting surface; slips results in a temporary

loss of balance in a sliding motion. Many construction site surface materials could

be the cause to slips such as water, oils, gravel, and pipes. In the contrast, trips

are caused by a sudden high friction or obstruction between the worker and

supporting surface resulting in the loss of the worker’s balance. Many construction

site hazards could cause trips, such as tools, equipment, cables, conduits, holes,

openings, bumps. Slips and trips are considered as one of the most common

construction site risks. According to Lipscomb et. al. (2008) slips and trips

constitute about 20% of all injury accidents in USA workplaces. Although slips and

trips could occur because of inappropriate footwear of worker, the inappropriate

footwear would not be under this category, but rather it would be included within

the conditional events considering that the basic and conditional events categories

are meant to be mutually exclusive

Although the major reference for the triggering events risks is the OIICS,

however, this classification would arrange the event structure in a harmonic way

to fit the path of this research. In this research, falls, slips, trips risks category

63 includes four subgroups of major triggering risk events in construction: fall from

elevation, fall from scaffolding or staging, fall from ladder, and cave-in.

3.3.2.1.1.1 Fall from Elevation

Fall from elevation risk includes falls, slips, and trips on the same level and falls to lower levels. The OIICS defines fall to lower level as the instances in which the risk is produced by the impact between the worker and another surface of lower elevation when gravity generates the worker's impact motion. Fall on same level

applies to risk events when a worker has a loss in equilibrium from falls, slips, and

trips generated by gravity at the same level. It is clear that this event category

includes fall on same level and fall to lower level where the two cases could

encounter different risk values; risk values system adopted in this research is

explained in other chapters.

This risk event category includes any event that applies to the spirit of its

definition and excludes any fall event that is categorized under any of the other

falls, slips, and trips risks event categories, i.e., fall from scaffolding or staging, fall

from ladder, and cave-in. For example, this category excludes falls from any

surface that can be described as scaffolding or staging; nevertheless, this category

includes falls from structures such as roofs, elevator shafts, and openings.

3.3.2.1.1.2 Fall from Scaffolding or Staging

Fall from scaffolding or staging risk events are related to the scaffolding or

staging component, e.g., limited area scaffolding; that could cause a worker to fall,

slip, or trip for a reason other than the failure of structural components. The aforementioned exclusion of this category is covered under the cave-in events.

3.3.2.1.1.3 Fall from Ladder

Fall from ladder risk events are related to worker on ladder falling, slipping, or tripping from ladder for a reason other than conditional or enabling causes.

Improper placement of ladder, unstable or uneven ground under the ladder are examples of risks related to this category; however, a worker not maintaining three contact points with the ladder at all times is considered to be an enabling cause, thus does not fit in this category.

3.3.2.1.1.4 Cave-in

A worker may also fall due to failures related to support or component of support that is being platform for the worker. This failure may cause the total collapse of the entire support structure or some of its supporting components in which the support cannot perform its intended function, leading the worker to lose

65 of stability and fall, slip, or trip from the support. An example of causes leading to cave-in risk is loss of stability due to missing connections of the support. External causes could also lead to the failure of the supporting platform, such as the impact

of construction equipment to the supporting structure or even strong winds. In

general, trenches, excavations, and tunnels have tendency for cave-in risk when

they are not fully supported (Hinze et al., 1998). However, this risk event category

does not include the risk of the worker being struck by falling objects; rather it

includes the risk of the workers to fall due to support related failure only. Moreover, the collapse of a temporary or existent site structure could cause the cave-in fall

of worker.

3.3.2.1.2 Struck by/Caught in Falling Objects Risks

Struck by accident mode is one of the fatal four and is considered the

second highest cause of construction related deaths in the US. Struck by

construction accidents have different modes, i.e., struck by falling objects and struck by moving objects. Nevertheless, most of struck by fatalities involve heavy equipment. Most of the struck by and caught in injuries and fatalities result from being struck by moving heavy equipment or machine, struck by falling objects, e.g., crane, boom, load, etc., and struck by trench cave-in (Hinze et al., 2005; Hinze et al.,1998).

Different construction activities might expose workers to different struck by risks. For instance, working at an excavation zone could increase the risk of being struck by moving heavy excavation equipment, swinging backhoes, backing equipment, overturned vehicles, etc. In contrast, when a worker is below a crane or scaffold, the risk of being struck by falling objects increases. Thus, in order to clearly differentiate between the different struck by risks, two struck by categories have been considered: Struck by/caught in falling objects risks and struck by/caught in moving objects risks. This section includes the breakdown of the struck by falling objects risks into struck by falling equipment and struck by falling material. This category includes the caught in risk of being squeezed, crushed, or compressed between two or more objects when the forcible impact with the falling or moving object would be accompanied by another object, e.g., ground, in which the worker is caught in between.

3.3.2.1.2.1 Struck by Falling Equipment

Struck by falling equipment during construction activities applies to risks constituted when the falling motion of construction equipment may produce forcible impact with worker. This category includes risk constituted from falling equipment by gravity, energized, and operated equipment. Struck by falling equipment can happen on construction sites in many ways, such as dropping, thrown, discharged, falling, slipping, collapsing, etc. Falling equipment at construction site can be heavy

67 equipment, such as cranes, or even light equipment such as screwdriver.

Therefore, the risk value assigned to a certain risk should take into account all

related triggering factoring, such as the height of the fall, weight, etc.

3.3.2.1.2.2 Struck by Falling Material

Struck by falling material category is mostly introduced to differentiate the materials risk from the equipment risk. Struck by falling material during construction activities applies to risks constituted when the falling motion of construction materials may produce a forcible impact with worker. This category includes risk constituted from falling material by gravity or energized by any means.

Struck by falling materials can happen on construction sites in many ways, such as dropping, thrown, discharged, falling, slipping, collapsing, etc., which includes the risk of being struck by collapsing or falling materials due to cave-ins. The falling material at construction site could include steel, concrete, glass, windows, etc.

3.3.2.1.3 Struck by/Caught in Moving Objects Risks

3.3.2.1.3.1 Struck by Moving Equipment

As mentioned earlier, many of struck by, caught in, crushed by risks at construction sites involve heavy equipment and machinery (Haslam et al., 2005),

especially reversing and overturning machinery hitting or crashing on-foot workers 68 at close proximity. McCann (2006) found that struck by incidents were related to construction heavy equipment and vehicles, such as: backhoes, loaders, bulldozers, rollers, trucks, forklifts, private vehicles, etc.

Struck by moving equipment during construction activity applies to risks formed when horizontal motion, such as moving, rolling, sliding, shifting, or slipping, of equipment may produce a forcible impact or contact with a worker. This event category includes struck by due to loss of control, sudden starts and stops, or collision involving powered or non-powered transportation or non-transportation equipment. This category involves events when the motion producing the contact or collision is that of the worker or equipment. In general, the motion produced by a worker could be in the form of bumping into equipment, kicking equipment, or pushed or towards equipment. However, this category does not include struck by risks rooting from worker’s enabling causes, such as lack of skill or even suicidal actions.

This category includes the caught in risk of being squeezed, crushed, or compressed between two or more objects from which construction equipment being involved. Clearly, the caught in accident mode is different than struck by, however, the key factor in distinguishing between struck by events and caught in events is whether the forcible impact with the equipment alone would cause the injury. Moreover, the event should considered to be caught in when the injury may be created as result of crushing between objects, from which one being equipment.

3.3.2.1.3.2 Struck by Moving Material

Struck by moving material category is mostly introduced to differentiate the materials risk from the equipment risk. Although most of moving material risk at construction site is due to gravity force, such as rolling barrels on an inclined plane,

this category include the risk of moving material due to any force either operated

or non-operated. Struck by moving material during construction activities applies to risks formed when moving, rolling, sliding, shifting, or slipping material in

horizontal motion may produce forcible impact or contact with worker. This category involves events when the motion producing the contact or collision is that of worker or material. This category includes the caught in risk of being squeezed,

crushed, or compressed between two or more objects from which construction

material being involved. The moving material in construction site could include barrels, boxes, glass, sheets, etc.

3.3.2.1.4 Exposure to Harmful Substances or Environments Risks

Exposure or contact with harmful substances or environments risks include

exposure to fire or explosions or extreme temperatures, contact with electric

current, and contact with chemical substances. Electrical shocks or contact with

electric current is the most common basic event under the subgroup of exposure

to harmful substances or environments.

3.3.2.1.4.1 Exposure to Fire/Explosions

Fires and explosions exposure risks are the second most common basic event under this subgroup after contact with electric current risks. This category concerns the risk potential sources of fire or explosions. There are several sources of the fire and explosions risk at construction site. Pressurized, energized, electrified, flammable, temperature or pressure sensitive sources when affected by certain circumstances may lead to fire or explosion, e.g., explosion of fuel tank or the rupture of utility gas lines. This category applies to unintentional explosions or fires that are source related and unrelated to worker enabling causes.

Contact with temperature extremes applies under this event category.

Temperature extreme exposure may come from several sources, such as:

temperature from the environment, and contact with hot or cold objects, e.g.,

torches, welding, stoves, furnaces, freezers, liquid nitrogen, etc.

3.3.2.1.4.2 Contact with Electric Current

Electricity as a power source is widely used on construction sites, however,

it has the potential to be fatal. Construction workers may face many events of electrical risks on hourly basis. Contact with overhead electric power lines constitute one of the most common sources of electric shock injuries because of high voltage that can cause electrocution (Hinze et al.,1998).

Workers contact with electricity may be direct from the power source, e.g., contact with live wire, contact with electric fence; or contact may also happen indirectly, e.g., contact with conductive material or tool touching live electricity

source, contact with crane touching power line, or contact with wet surface transmitting electricity. Thus, both of the electric power system and construction tools may also be the source leading to the exposure to electric current. Although power tools can be source of the risk under this category, however, the risk of misusing electrical equipment is not included under this category as it is considered to be under enabling causes.

A reliable risk assessment system should take into consideration the instances when a worker is affected by two or more risks. This is considered one of the limitations of this study considering individual triggering events only. For example, when an electric shock initiates a chain of events leading to the fall of the worker, then both risks of electrocution and fall from elevation should be taken into consideration to come up with a general risk assessment value.

3.3.2.1.4.3 Contact with Chemical Substances

Construction site could contain several different chemicals which may constitute risks to the health and safety of workers. Exposure or contact with hazardous or toxic chemical substances, such as silica dust, asbestos, sewer gas, solvents, pesticides, natural gas, carbon monoxide, or any gas causing the

72 depletion of oxygen in space, etc., could cause very serious illnesses such as

Asphyxiation (Hinze et al., 1998). The exposure to chemical substances could be through inhalation, absorption, skin contact, ingestion, or injection.

Dust is one of the common sources of chemical exposure risk at

construction sites. The dust’s toxicological properties depend on its type: physical,

chemical or mineralogical. However, the exposure risk value could also be

determined by the dose whether from concentrated or diluted exposure. In the case of dust that is released into a space or atmosphere, the risky exposure mode

could be inhalation. Chemicals exposure may cause different range of illness, e.g.,

headaches, dizziness, faintness, deafness, respiratory diseases, skin problems,

etc. (Murie, 2007). In general, the short-term effects of chemicals could affect judgment and rationality of workers; nevertheless, the chemical exposure may lead to long-term illnesses and diseases such as damages to the central nervous system, liver, kidneys, etc.

4 Chapter Four: Simulation of Risk Detection Method

4.1 Introduction

This chapter presents a programmed model for real-time risk detection. The model is intended to show the process of interaction between different system components at different stages of performance. Real-time risk detection system can be simulated with the method’s specific steps: risk prioritization, risk detection, risk evaluation, and risk control.

The sections of this chapter demonstrates process, algorithm, and cases of the simulation program.

4.2 Detection Method Simulation Program

4.2.1 Simulation Program Process

The process of the simulation program is intended to reflect the detection methodology process as explained in previous chapter. As this study is limited to the triggering risk events, the process starts when the inspector performs the risk

74 prioritization by defining RFID tags with unique product identifiers specifying risk level values and types, as shown in Figure 4.1. When the RFID tag is within the

detection range, the unique product identifier is selected in the intelligent system

that can properly receive the risk values to be analyzed. The intelligent system of

the detection solution is demonstrated to be the brain of the system - that receives,

analyzes, and delivers the risk evaluation and control information in real time. For

this study, the risk evaluation information represents the individual triggering risk

types and levels. Ultimately, the subjective linguistic risk evaluation values would

be declared to the worker. Moreover, the simulation program can simulate the risk

evaluation levels by their rotational fuzzy set membership functions.

RFID Tags Intellegent Evaluation & RFID Reader Defined for Risk System Control Values Risk Detection Prioritization Computations Declared

Figure 4.1 Process of Interaction between Different System Components

4.2.2 Simulation Program Algorithm

This section presents a flow chart shown in Figure 4.2 demonstrating the

real-time risk detection programed method algorithm. The flow chart includes three

75 phases divided by role. Site setup by safety inspectors, real-time detection by worker, and real-time evaluation by intelligent system are the three algorithm phases referred to as phase one, phase two, and phase three respectively. Shapes of each junction box shown in Figure 4.2 represents either process, decision, or

data junction. Rectangular shape represents process junctions, diamond shape

represents decision junction, and the parallelogram represents data item junction.

The algorithm of this program starts with process of inspector at site, as

shown in Figure 4.2, implying the inspector intention for risk prioritization as shown

in Figure 4.1 illustrating the method’s process. If all the risk is prioritized then the

algorithm decision junction leads to the second phase of the program which is real- time detection by worker, otherwise, the inspector would go through the following steps until all the predictable risk is prioritized: search RFID tag values, fetch RFID tag from database, and define RFID tag. Define RFID tag includes printing the

RFID tag with the fetched RFID tag values and placing it at the risk location. Phase two starts with the process of the worker being at site. Select risk process represents risk detection, it is triggered when the decision of risk in range is activated considering the RFID reading range. If there is no other risk in range then phase three will be triggered, otherwise, the other risk in range will go through the process of select risk. The role of the intelligent system is initiated when the unique product identifier input from the detected RFID tag is sent to the data item in order to fetch risk value from database. In turn, the intelligent system will compute the

risk evaluation value by comparing the unique product identifier against the defined

RFID values by safety inspector in phase one. The last step in phase three is to

declare risk evaluation values as computed from the previous process. However,

the loop will go back to activating the decision junction if any other risk is in range

which will trigger the select risk process and thus phase three. It is noted that the

inspector at site can initiate the site setup phase for risk prioritization at any instant

of time while the worker at site can initiate the real-time detection phase for risk

detection at any point of space.

Real-Time Risk Detection Method Program Flow Chart Site Setup Real-Time Detection Real-Time Evaluation [By Safety Inspector] [By Worker] [By Intelligent System]

Unique Product Inspector at Site Worker at Site Identifier Input Yes

No Fet ch Risk All Risks Risk in Range? Value from Prioritized? Database

No Yes

Search RFID Compute Risk Select Risk Tag Values Evaluation Value

Fetch RFID Yes No Tag from Other Risk Declare Risk Database in Range? Evaulation Value

Define RFID Tag

Figure 4.2 Programed Method Algorithm Flow Chart

4.2.3 Simulation Program Cases

The program was developed using C Sharp (C#) programming language and Visual Studio as the Integrated Development Environment (IDE). The program was developed and designed to model the real-time method process and algorithm

as discussed in previous sections.

This section shows the simulation program cases run as designed on the

Integrated Development Environment. Figure 4.3 shows the first step in risk

prioritization, when RFID tag defining starts by the identification of risk subgroup

using the drop down menu. Figure 4.4 shows selecting the risk type by inspector

for the identification of basic triggering risk event. Figure 4.5 shows the selection

of risk value by inspector for the RFID tag to be defined. Figure 4.6 shows the step

after risk parameters are set, the inspector would fetch from database to search if

the values where previously defined. Figure 4.7 shows the inspector pushing the

define button if the tag was not defined previously, the real-time detection method

requires the inspector to print and attach the defined tag to the risk location.

Figure 4.8 shows the start of phase two algorithm process when the RFID

tag is in the worker's range. The RFID tag is selected and information is received

by the detection intelligent system on right side. The RFID tag information

appearing on the right side of the screen implies that the role of the intelligent

system was initiated when the unique product identifier input from the detected

RFID tag was sent for fetching risk values from database. In turn, the intelligent

79 system computes the risk evaluation value by comparing the unique product identifier against the defined RFID values in phase one. Thus the risk evaluation values are declared as computed from the previous process. Figure 4.9 shows the detection intelligent system keeping a record of all risks detected by worker during the day. Figure 4.10 to Figure 4.15 shows some of the risk values detected

illustrated in the rotation fuzzy set model form. Figure 4.10, Figure 4.11, and

Figure 4.12 shows the risk event fall from elevation of the falls, slips, and trips risks

subgroup, the figures shows three different risk values of fall from elevation risk

that the worker encountered at different instances of time and different points of

space. Figure 4.13 shows the rotational fuzzy model of the risk event of fall from

scaffolding or staging with a risk value of very risky. Figure 4.14 shows the

rotational fuzzy model of the risk event of cave-in with a risk value of risky.

Figure 4.15 shows the rotational fuzzy model of the risk event of fall from ladder

with a risk value of fairly risky.

First Step in Risk Prioritization by Inspector, RFID Tag Defining starts with identification of Risk Subgroup Risk of identification with starts Defining Tag RFID Inspector, by Prioritization Risk in Step First

3 . 4 Figure Figure 81

g Riskg Event Selecting the Risk type by Inspector for the Identification of Basic Triggerin Basic of Identification for the Inspector by type Risk the Selecting

4 . 4 Figure Figure

Selecting Risk Value by Inspector for the RFID Tag to be defined be to Tag RFID the for Inspector by Value Risk Selecting

5 . 4 Figure Figure

Define RFID Tag

Fetch from Database to from Database Fetch ould ould As Risk Parameters are Set, the Inspector w Inspector the Set, are Parameters Risk As

6 . 4 Figure Figure

Inspector would Push the Define Button if the Fetch Database Indicated the Tag was Not Defined Defined Not was Tag the Indicated Database Fetch the if Button Define the Push would Inspector

7 . 4 Figure Figure

eceived by Detection Intelligent System on Right Side on Right System Intelligent Detection by eceived R RFID Tag is in worker range, Information is is Information range, worker in is Tag RFID

8 . 4 Figure Figure

The Detection Intelligent System keeps a record of all Risks Detected by Worker during the Day the Worker during by Detected Risks all of a record keeps System Intelligent Detection The

9 . 4 Figure Figure

Figure 4.10 One of the Risk Values Detected shown as Rotation Fuzzy Set

Figure 4.11 One of the Risk Values Detected Shown as Rotation Fuzzy Set

Figure 4.12 One of the Risk Values Detected Shown as Rotation Fuzzy Set

Figure 4.13 One of the Risk Values Detected Shown as Rotation Fuzzy Set

Figure 4.14 One of the Risk Values Detected Shown as Rotation Fuzzy Set

Figure 4.15 One of the Risk Values Detected Shown as Rotation Fuzzy Set

5 Chapter Five: Conclusion

5.1 Summary and Conclusion

Considering the damages caused by construction accidents, there is need for advancing safety measure and evaluation systems towards safety risks control to protect health and safety of on-foot workers. It is more effective to proactively combat safety risks at source rather than dealing with accident problems after they occur. The lack of real-time visibility, indication, and information about the site risks types and levels may put construction workers in dangerous situations.

This study has achieved the concept of real-time safety risk detection by proposing an integrated structured method for evaluating risk events at building construction site for on-foot worker. The achievements of RFID applications promote it to be applicable for construction risk management. The detection method adopted the RFID technology for being proven to be consistent, uniform, and reliable identification solution due to RFID unique features and performance at construction site environment, such as: the non-contact automatic identification, the ability to access and track multiples of RFID tagged objects, and the ability to operate without having a direct line of sight. This solution design goes beyond the

94 state-of-the-art by systemizing the identification, prioritization, detection, evaluation, and control of safety risks. It adds to the state-of-the-art by developing

an intelligent system that communicates in real-time risk identities, types, and

levels at different stages and phases of construction. This study exceeds the

current detection solution which can only detect the proximity of objects which

excludes providing individual risk-specific information. The use of fuzzy logic is complementary with the triggering basic risk structure for enhancing the consistency and comprehensiveness of risk evaluation subjective values delivered in real-time. Furthermore, the use of subjective evaluation values could bridge the

risk management gap constituted from misinterpreting qualitative risk values,

safety regulations and measures.

The simulation of the real-time detection system was implemented in a model with the intelligent system being the brain of the system that receives, interprets, and delivers the risk evaluation and control information in real time. This could make safety risks more recognizable and measureable at the time of risk exposure which can assist in establishing proactive decisions for risk control, accident prevention, and health protection. Real-time prognostic risk detection is among the strategies that could be adopted to effectively mitigate safety risks at early time or even before exposure.

As shown in the detection method simulation chapter, the detection

Intelligent System keeps a record of all risks detected by worker during the day.

Thus, the system could have diagnostic uses along with its prognostic primary use.

The system could be utilized to track and monitor the risk types and levels affecting workers to employ, manage, and calibrate health and safety programs at construction sites. Moreover, monitoring the risk detection record could give indication about progress and effectiveness of risk control programs by detecting risks at different instances of time and points in space. Furthermore, risk record might be helpful for accident and near miss incident investigations. Detection system record could help to identify weak zones or less protected areas at site.

Thus, having a reliable risk detection tool will support decision makers to take suitable and proactive provisions to control risks and prevent accidents.

The elaborated detection system can indicate that the system design was

based upon some guidelines: validity, significance, simplicity, representation,

reliability and consistency, and feasibility. The system design shows that it could

be reasonable to use such system at the construction site environment while

accommodating on-foot building construction worker activities and mobility. At least, the system could not constitute a hazard to any of the workers or restrain their movement. The simulation of the detection method indicates that the system could be representative by allowing engineers to add and modify input information in order to assure accurate representation of the site risks. Furthermore, the simulation indicates reliability and consistency for the system’s intelligent system accuracy in processing inputs and delivering risk values at proper events;

consistency of the system is maintained, when the exact same steps are repeated

the system gives the same results.

5.2 Recommendations

Although, the design of the prognostic risk detection system is projected to be a significant strategy for its added value, however, experimental setup is

recommended to examine and validate the system’s validity, significance,

simplicity, representation, reliability and consistency. An experimental setup is also

recommended to conclude the optimum risk alerting range for on-foot workers.

Surveys could be conducted within the construction industry to determine the social acceptance of the use of such risk detection solutions. A feasibility study could assist the elaboration of a business plan for the real-time risk detection solution.

5.3 Future Work

Future work could include experimental setup to validate the design guidelines and to determine the optimum alerting range for the on-foot worker. The future work risk analysis approach could overcome the limitations of this study of the detection of triggering risk events only. Evaluation of safety risks should not be limited to triggering risks. The on-foot worker may be concurrently affected by several risks. In the current construction industry, the combined effect of the several concurrent risks at a construction site is unrecognized or underestimated. The future work could achieve a real-time safety risk assessment

97 system for the evaluation of the combined value of comprehensive risk drivers concurrently being exposed to on-foot construction workers. This could reflect the

domino theory principle of construction accident being the result of a combination

of causes and effects of different factors.

The future work could design and examine holistic approach for real-time

risk assessment system that integrates the methodology of real-time interactive

risk detection with prognostic analytical risk assessment intelligent system model

based on fuzzy fault tree analysis. The fuzzy fault tree analysis, valued as a

uniform method for structuring workplace risks, could be an adequate tool for

elaborating the intelligent system accounting for the combined effect of the

different concurrent risk events, i.e., triggering, enabling, and conditional risk

events. The risk drivers could be systemized into a fuzzy fault tree model to

constitute a holistic prognostic approach for real-time risk assessment intelligent

system. The real-time assessment solution approach could be a reliable and

consistent system for computing combined degree of health and safety risk

exposure which makes site safety conditions realistically recognized and

measured for construction workers.

6 References

Abdelhamid, T. S., & Everett, J. G. (2000). Identifying root causes of construction accidents. Construction Engineering and Management, 126(1), 52-60.

Albert, A., & Hallowell, M. R. (2012). Hazard recognition methods in the construction industry. In Construction Research Congress (pp. 407-416).

Ale, B. J. M., Baksteen, H., Bellamy, L. J., Bloemhof, A., Goossens, L., Hale, A., & Whiston, J. Y. (2008). Quantifying occupational risk: The development of an occupational risk model. Safety Science, 46(2), 176-185.

Alhajeri, M. (2011). Health and Safety in the Construction Industry: Challenges and Solutions in the UAE (Doctoral dissertation, Coventry University).

Al-Humaidi, H. M., & Hadipriono Tan, F. (2010). A fuzzy logic approach to model delays in construction projects using rotational fuzzy fault tree models. Civil Engineering and Environmental Systems, 27(4), 329-351.

Andersson, L. (1986). A new method based on the theory of fuzzy sets for obtaining an indication of risk. Civil Engineering Systems, 3(3), 164-174.

Australia, S. W. (2011). How to manage work health and safety risks: Code of practice. Safe Work Australia.

Ayyub, B. M. (2014). Risk Analysis in Engineering and Economics. Second Edition. CRC Press.

Baradan, S., & Usmen, M. A. (2006). Comparative injury and fatality risk analysis of building trades. Construction Engineering and Management, 132(5), 533-539.

Bentley, T. A., Hide, S., Tappin, D., Moore, D., Legg, S., Ashby, L., & Parker, R. (2006). Investigating risk factors for slips, trips and falls in New Zealand residential construction using incident-centred and incident-independent methods. Ergonomics, 49(1), 62-77.

Bhuptani, M., & Moradpour, S. (2005). RFID field guide: deploying radio frequency identification systems. Prentice Hall PTR. 99

Bureau of Labor Statistics (1992-2014). Census of fatal occupational injuries (CFOI) - current and revised data. U.S. Department of Labor, on the internet at [http://www.bls.gov/iif/oshcfoi1.htm#charts] (visited 06.26.2016).

Bureau of Labor Statistics (2012). Occupational injury and illness classification manual. U.S. Department of Labor, Washington DC.

Bureau of Labor Statistics (2014a). Census of fatal occupational injuries summary. U.S. Department of Labor, USDL-15-1789, on the internet at [http://www.bls.gov/news.release/cfoi.nr0.htm] (visited 06.26.2016).

Bureau of Labor Statistics (2014b). Table A-1: Fatal occupational injuries by industry and event or exposure. Census of Fatal Occupational Injuries, U.S. Department of Labor, on the internet at [http://www.bls.gov/iif/oshwc/cfoi/cftb0286.pdf] (visited 07.10.2016).

Bureau of Labor Statistics (2014c). Fatal occupational injuries by selected characteristics 2003-2014. Census of Fatal Occupational Injuries, U.S. Department of Labor, on the internet at [http://www.bls.gov/iif/oshwc/cfoi/all_worker.pdf] (visited 07.10.2016).

Campbell, J. M. (2008). Safety Hazard and Risk Identification and Management In Infrastructure Management (Dissertation). The University of Edinburgh.

Chapman, C., & Ward, S. (2004). Why risk efficiency is a key aspect of best practice projects. International Journal of Project Management, 22(8), 619-632.

Cheng, C. W., Leu, S. S., Cheng, Y. M., Wu, T. C., & Lin, C. C. (2012). Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry. Accident Analysis & Prevention, 48, 214-222.

Chi, C. F., Chang, T. C., & Ting, H. I. (2005). Accident patterns and prevention measures for fatal occupational falls in the construction industry. Applied Ergonomics, 36(4), 391-400.

Chi, S., Han, S., & Kim, D. Y. (2012). Relationship between unsafe working conditions and workers’ behavior and impact of working conditions on injury severity in US construction industry. Construction Engineering and Management, 139(7), 826-838.

Condea, C., Thiesse, F., & Fleisch, E. (2012). RFID-enabled shelf replenishment with backroom monitoring in retail stores. Decision Support Systems, 52(4), 839- 849.

100

Dumrak, J., Mostafa, S., Kamardeen, I., & Rameezdeen, R. (2013). Factors associated with the severity of construction accidents: The case of South Australia. Australasian Journal of Construction Economics and Building, 3(4), 32.

Ergen, E., Akinci, B., & Sacks, R. (2007). Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS. Automation in Construction, 16(3), 354-367.

Esmaeili, B. (2012). Identifying and quantifying construction safety risks at the attribute level (Doctoral Dissertation), University Of Colorado, Boulder.

Faber, M.H. and Stewart, M.G. (2003). Risk Assessment for Civil Engineering Facilities: Critical Overview and Discussion. Reliability Engineering and System Safety, 80(2) 173-184.

Frank, T., Brooks, S., Creekmore, R., Hasselbalch, B., Murray, K., Obeng, K., Reich, S. and Sanchez, E., (2008), Quality Risk Management Principles and Industry Case Studies, pp. 1-9.

Furlani, K. M., & Stone, W. C. (1999). Architecture for discrete construction component tracking. The 16th IAARC/IFAC/IEEE International Symposium on Automation and Robotics in Construction, 289-294.

Gambatese, J. A., Behm, M. and Rajendran, S. (2008). Design’s role in construction accident causality and prevention: Perspectives from an expert panel. Safety Science, 46, 675-691.

Gavious, A., Mizrahi, S., Shani, Y. & Minchuk, Y. (2009). The costs of industrial accidents for the organization: Developing methods and tools for evaluation and cost-benefit analysis of investment in safety. Loss Prevention in the Process Industries, 22, 434-438.

Güdemann, M. (2011). Qualitative and quantitative formal model-based safety analysis: push the safety button (Doctoral dissertation). Magdeburg University.

Gürcanli, G. E., & Müngen, U. (2009). An occupational safety risk analysis method at construction sites using fuzzy sets. International Journal of Industrial Ergonomics, 39(2), 371-387.

Hadipriono F.C., Vargas C.A., and Yoo W.H. (1995a). Safety first: a fault tree expert system for construction falls. Volume I: the knowledge acquisition. Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH).

101

Hadipriono F.C., Vargas C.A., and Yoo W.H. (1995b). Safety first: a fault tree expert system for construction falls. Volume II: the knowledge structure. Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH).

Hadipriono F.C., Vargas C.A., and Yoo W.H. (1995c). Safety first: a fault tree expert system for construction falls. Volume III: The Knowledge Base. Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH).

Hadipriono, F. C. (1992a). Expert system for construction safety. I: Fault-tree models. Performance of Constructed Facilities, 6(4), 246-260.

Hadipriono, F. C. (1992b). Expert system for construction safety. II: knowledge base. Performance of Constructed Facilities, 6(4), 261-274.

Hadipriono, F. C., & Ross, T. J. (1991). A rule-based fuzzy logic deduction technique for damage assessment of protective structures. Fuzzy Sets and Systems, 44(3), 459-468.

Hadipriono, F. C., & Toh, H. S. (1989). Modified fault tree analysis for structural safety. Civil Engineering Systems, 6(4), 190-199.

Hallowell, M.R. ( 2008). A Formal Model for Construction Safety and Health Risk Management. Doctorial Thesis; Oregon State University, Oregon.

Haslam, R. A., Hide, S. A., Gibb, A. G., Gyi, D. E., Pavitt, T., Atkinson, S., & Duff, A. R. (2005). Contributing factors in construction accidents. Applied Ergonomics, 36(4), 401-415.

Hatipkarasulu, Y. (2010). Project level analysis of special trade contractor fatalities using accident investigation reports. Safety Research, 41(5), 451-457.

Health and Safety Executive (HSE). (1998). Managing Health and Safety: Five Steps to Success. Health and Safety Executive, London, UK.

Heinrich, H. W. (1959). Industrial Accident Prevention. A Scientific Approach. New York, McGraw-Hill.

Heinrich, H. W., Petersen, D. C., Roos, N. R., & Hazlett, S. (1980). Industrial accident prevention: A safety management approach 5th edition. McGraw-Hill Companies, New York.

Hinze, J. W., & Teizer, J. (2011). Visibility-related fatalities related to construction equipment. Safety Science, 49(5), 709-718.

102

Hinze, J., & Russell, D. B. (1995). Analysis of fatalities recorded by OSHA. Construction Engineering and Management, 121(2), 209-214.

Hinze, J., and Godfrey, R. (2003). An evaluation of safety performance measures for construction projects. Construction Research, 4(01), 5-15.

Hinze, J., and Wiegand, F. (1992). Role of designers in construction worker safety. Engineering and Management, 118(4), 677–684.

Hinze, J., Huang, X., & Terry, L. (2005). The nature of struck-by accidents. Construction Engineering and Management, 131(2), 262-268.

Hinze, J., Pedersen, C., and Fredley, J. (1998). Identifying root causes of construction injuries. Construction Engineering and Management,124(1), 67-71.

Hinze, Jimmie and Appelgate, Lisa (1991). Costs of construction injuries. Construction Engineering and Management, 117:3(537), 537-550.

Hollnagel, E. (2008). Risk+ barriers= safety?. Safety science, 46(2), 221-229.

Hu, K., Rahmandad, H., Smith‐Jackson, T., & Winchester, W. (2011). Factors influencing the risk of falls in the construction industry: a review of the evidence. Construction Management and Economics, 29(4), 397-416.

Huang, D., Chen, T., & Wang, M. J. J. (2001). A fuzzy set approach for event tree analysis. Fuzzy sets and systems, 118(1), 153-165.

Huang, X., and Hinze, J. (2003). Analysis of Construction Worker Fall Accidents. Construction Engineering and Management, 129:262-271.

Hunt, V. D., Puglia, A., & Puglia, M. (2007). RFID: a guide to radio frequency identification. Wiley-interscience, John Wiley & Sons.

International Labour Organization (2005). World day for safety and health at work 2005: A Background Paper. International Labour Office, Geneva.

Jannadi, O. A. and Almishari, S. (2003). Risk assessment in construction. Construction Engineering and Management, 129(5): 492-500.

Jannadi, O. A., & Almishari, S. (2003). Risk assessment in construction. Construction Engineering and Management, 129(5), 492-500.

Jaselskis, E. J. and El Misalami, T. (2003). Implementing radio frequency identification in the construction process. Construction Engineering and Management, 129(6), 680-688.

103

Jaselskis, E. J. and El-Misalami, Tarek (2003). Implementing Radio Frequency Identification in the construction process. Construction Engineering and Management,129(6), 680-688.

Jason, A. (2008). Organizing informal workers in the urban economy: The case of the construction industry in Dar es Salaam. Habitat International, (32) 292-202, Tanzania.

KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. (2011). Risk assessment model selection in construction industry. Expert Systems with Applications, 38(8), 9105-9111.

Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Elsevier Science Inc.

Khan, F. I., & Abbasi, S. A. (1998). Techniques and methodologies for risk analysis in chemical process industries. Journal of Loss Prevention in the Process Industries, 11(4), 261-277.

Khanzode, V. V., Maiti, J., & Ray, P. K. (2012). Occupational injury and accident research: A comprehensive review. Safety Science, 50(5), 1355-1367.

Kirchsteiger, C. (1999). On the use of probabilistic and deterministic methods in risk analysis. Loss Prevention in the Process Industries, 12(5), 399–419.

Kuchta, D. (2001). Use of fuzzy numbers in project risk (criticality) assessment. International Journal of Project Management, 19(5), 305-310.

Levitt, R. E. (1987). HOWSAFE: a microcomputer-based expert system to evaluate the safety of a construction firm. In Expert Systems in Civil Engineering (pp. 55-66). ASCE.

Liao, C. W., & Perng, Y. H. (2008). Data mining for occupational injuries in the Taiwan construction industry. Safety Science, 46(7), 1091-1102.

Lin, Y. C. (2008). Enhancing facility management using RFID and web technology in construction. INTECH Open Access Publisher.

Lingard, H., & Holmes, N. (2001). Understandings of occupational health and safety risk control in small business construction firms: barriers to implementing technological controls. Construction Management & Economics, 19(2), 217-226.

Lipscomb, H. J., Dale, A. M., Kaskutas, V., Sherman‐Voellinger, R., & Evanoff, B. (2008). Challenges in residential fall prevention: insight from apprentice carpenters. American Journal of Industrial Medicine, 51(1), 60-68.

104

Lipscomb, Hester J., Glazner, J., Bondy, J., Guarini, K. and Lezotte, D. (2006). Injuries from slips and trips in construction. Applied Ergonomics, 37, 267-274.

Liu, J., Yang, J., Wang, J., Sii, H. and Wang, Y. (2004). Fuzzy Rule-Based Evidential Reasoning Approach for Safety Analysis. International Journal of General Systems, 33(2–3), 183–204.

Lubega, H., Kiggundu, B. M., & Tindiwensi, D. (2000, November). An investigation into the causes of accidents in the construction industry in Uganda. 2nd International Conference on Construction in Developing Countries.

Maglaras, G. (1995). Experimental Comparison of Probabilistic Methods and Fuzzy Sets for Designing under Uncertainty. Virginia Polytechnic Institute and State University, Blacksburg, Virginia. Doctorate.

Markowski, A. S., Mannan, M. S., & Bigoszewska, A. (2009). Fuzzy logic for process safety analysis. Loss Prevention in the Process Industries, 22(6), 695- 702.

Marks, E., and Teizer, J. (2012). Proximity sensing and warning technology for heavy construction equipment operation. Construction Research Congress, 981- 990, West Lafayette, Indiana.

Mattila, M., Hyttinen, M., & Rantanen, E. (1994). Effective supervisory behaviour and safety at the building site. International Journal of Industrial Ergonomics, 13(2), 85-93.

McAndrew, S. T., Anumba, C. J., Hassan, T. M., & Duke, A. K. (2005). Potential use of real-time data capture and job-tracking technology in the field. Facilities, 23(1/2), 31-46.

McCann, M. (2006). Heavy equipment and truck-related deaths on excavation work sites. Safety Research, 37(5), 511-517.

McClay, R. E. (1989a). Toward a more universal model of loss incident causation-Part I. Professional Safety, 35(1), 15-20.

McClay, R. E. (1989b). Toward a more universal model of loss incident causation-Part II. Professional Safety, 35(2), 34-39.

Morata, T. C., Themann, C. L., Randolph, R. F., Verbsky, B. L., Byrne, D. C., & Reeves, E. R. (2005). Working in noise with a hearing loss: perceptions from workers, supervisors, and hearing conservation program managers. Ear and hearing, 26(6), 529-545.

105

Murie, F. (2007). Building safety—An international perspective. International Journal of Occupational and Environmental Health, 13(1), 5-11.

National Institute for Occupational Safety and Health (2004). Worker Health Chartbook 2004. U.S. Department of Health and Human Service, DHHS (NIOSH) Publication No. 2004-146, Cincinnati, Ohio.

Ngai, E., & Riggins, F. (2008). RFID: Technology, applications, and impact on business operations. International Journal of Production Economics, 112(2), 507- 509.

Nilsen & Aven (2003). Models and Model Uncertainty in the Context of Risk Analysis. Reliability Engineering and System Safety, 79.309-317.

Nunes, I. L. (2005). Fuzzy multicriteria model for ergonomic workplace analysis and risk analysis. In Information Technology, Knowledge Management and Engineering for Enterprise Productivity and Quality of Working Life (International Conference: Computer-Aided Ergonomics and Safety-CAES'05, pp. 25-28.

Occupational Safety and Health Administration (2013a). Commonly Used Statistics. U.S. Department of Labor, on the internet at [https://www.osha.gov/oshstats/commonstats.html] (visited 07.17.2016).

Occupational Safety and Health Administration (2013b). OSHA's Fall Prevention Campaign. U.S. Department of Labor, on the internet at [https://www.osha.gov/stopfalls] (visited 07.17.2016).

Occupational Safety and Health Administration (2013c). Top 10 Most Frequently Cited Standards. U.S. Department of Labor, on the internet at [https://www.osha.gov/Top_Ten_Standards.html] (visited 07.17.2016).

Occupational Safety and Health Administration (OSHA). (1990). Analysis of construction fatalities - The OSHA data base 1985–1989. U.S. Department of Labor, Washington, D.C.

Occupational Safety and Health Administration (OSHA). (2014). OSHA Construction eTools. U.S. Department of Labor, on the internet at [https://www.osha.gov/dts/osta/oshasoft] (visited 08.14.2016).

Paithankar, A. (2011). Hazard identification and risk analysis in mining industry (Doctoral Dissertation), National Institute of Technology Rourkela.

106

Phoya, S. (2012). Health and Safety Risk Management On Building Construction Sites In Tanzania: The Practice of Risk Assessment, Communication and Control, Chalmers University Of Technology.

Pinto, A. F. D. N. (2012). Development of a fuzzy qualitative risk assessment model applied to construction industry.

Pipitsupaphol, T., & Watanabe, T. (2000). Identification of root causes of labor accidents in the Thai construction industry. 4th Asia Pacific Structural Engineering and Construction Conference (APSEC 2000), 13-15.

Rahim A., Hamid, A., Zaimi M., Majid,A. and Singh, B (2008) Causes of Accidents at Construction Sites. Malaysian Journal of Civil Engineering, 20(2), 242-259.

Raz, T., Shenhar, A. J., Dvir, D. (2002). Risk management, project success, and technological uncertainty. R&D Management, 32, 101–109.

Ridley, J., & Channing, J. (Eds.). (2008). Safety at work-Seventh Edition. Routledge.

Ringdahl, L. H. (2001). Safety Analysis Principles and Practice in Occupational Safety. Second edition, Taylor & Francis, London.

Rozenfeld, O., Sacks, R., Rosenfeld, Y. And Baum, H., (2010). Construction Job Safety Analysis. Safety Science, 48 (4), 491–498.

Ruff, T. M. (2004a). Evaluation of devices to prevent construction equipment backing incidents. SAE Commercial Vehicle Engineering Congress and Exhibition, 2004-01-2725, Chicago, Illinois.

Ruff, T. M. (2004b). Advances in proximity detection technologies for surface mining equipment. 34th AIMHSR.

Ruff, Todd M. (2001). Monitoring blind spots: a major concern for haul trucks. Engineering and Mining, 202(12), 17–26.

Sandip, L. (2005). RFID sourcebook. Prentice Hall PTR, Massachusetts.

Sawacha, E., Naoum, S., & Fong, D. (1999). Factors affecting safety performance on construction sites. International Journal of Project Management, 17(5), 309-315.

Song, J., Haas, C. T. and Caldas, C. (2006). Tracking the location of materials on construction job sites. Journal of Construction Engineering and Management, 132(9), 680-688. 107

Szymusiak, S. M., & Ryan, J. P. (1982). Prevention of slip and fall injuries: part II. Professional Safety, The American Society of Safety Engineers, 27(7), 30-35.

Teizer, J., Allread, B. S., Fullerton, C. E., & Hinze, J. (2010). Autonomous proactive real-time construction worker and equipment operator proximity safety alert system. Automation in Construction, 19(5), 630-640.

The Center for Construction Research and Training (2013). The Construction Chart Book - The U.S. Construction Industry and its Workers. Fifth Edition, Silver Springs, MD.

Toole, T. (2002). Construction site safety roles. Construction Engineering and Management, 128(3), 203–210.

Toole, T. M., Gambatese, J. A., & Abowitz, D. A. (2016). Owners’ Role in Facilitating Prevention through Design. Journal of Professional Issues in Engineering Education and Practice, 04016012.

Vargas, C. A. (1998). Investigating construction falls using fault tree analysis and developing a prototype tool to reduce falls using expert system and computer assisted instruction methods. Ohio State University.

Victoria, W. (2007). Controlling OHS hazards and risks: a handbook for workplaces. In Controlling OHS hazards and risks: a handbook for workplaces. Worksafe.

Wang, L. C., Lin, Y. C., & Lin, P. H. (2007). Dynamic mobile RFID-based supply chain control and management system in construction. Advanced Engineering Informatics, 21(4), 377-390.

Wang, Y., Zhang, Y., Poon, S. W., & Huang, H. (2002). A study of construction site accident statistics. Triennial Conference CIB W099: Implementation of Safety and Health on Construction Sites, pp. 223-227.

Ward, R. B. (2012). Revisiting Heinrich's law. Chemeca 2012: Quality of life through chemical engineering, 23-26, New Zealand, 1179.

Weber, S. (1991). Uncertainty measures, decomposability and admissibility. Fuzzy Sets and Systems, 40(2), 395-405.

White, D. (1995). Application of systems thinking to risk management: a review of the literature. Management Decision, 33(10), 35-45.

World Health Organization. (2009). Basic Documents, Forty-seventh Edition.

108

Wu, W., Gibb, A.F. and Li, Q., (2010). Accident Precursors and Near Misses on Construction Sites: an Investigative Tool to Derive Information from Accident Databases. Safety Science, 48 (7), 845–858.

Yen-Pei Chen et. al. (2013). Application of Mobile RFID-Based Safety Inspection Management at Construction Jobsite. INTECH Open Access Publisher.

Yu, H. (2009). A knowledge based system for construction health and safety competence assessment. University of Wolverhampton.

Yung, P. (2009). Institutional arrangements and construction safety in China: an empirical examination. Construction Management and Economics, 27(5), 439- 450.

Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.

Zadeh, L. A. (1968). Probability measures of fuzzy events. Mathematical Analysis and Applications, 23(2), 421-427.

Zadeh, L. A. (1971). Quantitative fuzzy semantics. Information sciences,3(2), 159-176.

Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.

Zadeh, L. A. (1983). The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy sets and systems, 11(1), 199-227.

Zadeh, L. A. (1999). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 100, 9-34.

109