A case based reasoning approach to construction safety risk assessment

A CASE-BASED REASONING APPROACH TO CONSTRUCTION SAFETY RISK ASSESSMENT GOH YANG MIANG NATIONAL UNIVERSITY OF SINGAPORE 2004 A CASE-BASED REASONING APPROACH TO CONSTRUCTION SAFETY RISK ASSESSMENT GOH YANG MIANG (B.Eng.(Hons.), NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF CIVIL ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2004 ACKNOWLEDGEMENT This research would not have been possible if not for the help of numerous people. Most importantly is the support and understanding that my wife and my family gave me throughout the PhD candidature. Without their support (especially my wife’s) I would not have sustained through the stress and frustration of this research. I would also like to express my appreciation for the guidance given by my academic supervisor, Associate Professor David Chua. I thank him for the frequent discussions and valuable advice. I am also grateful that despite his busy schedule he managed to allocate time to read and comment critically on my thesis. This thesis also utilised a large amounts of input from industry experts and practitioners. These people include, Mr. Lim Poo Yam and his colleagues of Land Transport Authority (LTA), Mr. Ho Siong Hin and his colleagues of the Ministry of Manpower (MOM), Mr. Harry Ho, Mr. Chin Chee Chow and their colleagues of Singapore Construction Safety and Consultancy (SC2) Pte Ltd, Mr. Tan Kai Hong and his colleagues of SembCorp Engineers and Constructors, and Mr. Jason Oh of IES, EEHS Technical Committee. Special acknowledgement is also given to UK safety expert, Mr. John Anderson, who advised me on various issues relating to the research. I am also very grateful to all the individuals that helped the research in one way or another, but they were not named due to space constraints or their request for anonymity. I also hope that the ideas proposed in this thesis will bring about improvement to the level of safety on construction sites and help prevent unnecessary loss of lives. I dedicate this thesis to my baby girl, Goh Yu Le, who has brought much joy into my life, and my wife, Khew Hui Fong, for her unwavering support. i TABLE OF CONTENTS ACKNOWLEDGEMENT………………………………………………………… i TABLE OF CONTENTS…………………………………………………………. ii SUMMARY……………………………………………………………………… . vii LIST OF FIGURES.………………………………………………………………. ix LIST OF TABLES.……………………………………………………………… . xiii NOMENCLATURE ……………………………………………………………… xv CHAPTER INTRODUCTION ………………………………………………… 1.1 POOR SAFETY PERFORMANCE IN THE CONSTRUCTION INDUSTRY ……………………………………………………………………. 1.2 THE NEED FOR CONTINUAL IMPROVEMENT AND FEEDBACK CAPABILITIES ……………………………………………………………… . 1.3 OBJECTIVES OF RESEARCH …………………………………………… 1.3.1 Components of the SKMS …………………………………………… 1.4 SCOPE OF RESEARCH…………………………………………………… 1.5 RESEARCH METHODOLOGY…………………………………………… 10 1.6 ORGANISATION OF THESIS ……………………………………………. 12 CHAPTER LITERATURE REVIEW ………………………………………… 14 2.1 INTRODUCTION …………………………………………………………. 14 2.2 REVIEW OF RISK ASSESSMENT METHODOLOGIES ……………… 14 ii 2.2.1 Fault Tree Analysis and Event Tree Analysis ……………………… . 15 2.2.2 Failure Modes and Effects Analysis (FMEA), and Hazard and Operability Study (HAZOP) ……………………… .…………………… . 16 2.2.3 What-if analysis ……………………………………………………… 17 2.2.4 Job Hazard Analysis (JHA) ………………………………………… . 18 2.3 REVIEW OF RELEVANT COMPUTER-BASED TOOLS FOR THE CONSTRUCTION INDUSTRY ……………………………………………… 19 2.3.1 IKIS-Safety ……….………………………………………………… 19 2.3.2 Design-for-Safety-Process Tool ……………………………………… 20 2.4 TOOLS FOR MANAGEMENT OF SAFETY KNOWLEDGE ………… . 21 2.4.1 Database Management Systems ………… .………… .………… 22 2.4.2 Knowledge-Based Expert System ……… .………… .………… . 23 2.4.3 Artificial Neural Networks ……… .………… .………… 25 2.4.4 Case-Based Reasoning Systems … .………… .………… 26 2.5 CONCLUSIONS .………… .………… … .………… .……… 30 CHAPTER 3. THE MODIFIED LOSS CAUSATION MODEL …… .……… 32 3.1 INTRODUCTION .………… .………… … .………… .……. 32 3.2 RELEVANT WORKS ……… .………… … .………… .…… 32 3.3 THE MLCM ……… .………… … .………… .……………… 34 3.4 APPLICATION IN INCIDENT INVESTIGATION ……………………… 40 3.4.1 MLCM Investigation Approach ……………………………………… 40 3.4.2 Structure for Incident Investigation Information …………………… 42 iii 3.4.3 MLCM Taxonomy …………………… …………………… ……… 46 3.5 APPLICATION IN SAFETY PLANNING ……………… ……… …… 53 3.5.1 Risk Assessment …………………… …………………… ……… . 53 3.5.2 Risk Control Selection ……………… …………………… ……… 57 3.6 CONCLUSIONS ……….……… …………………… ……… . 59 CHAPTER 4. KNOWLEDGE REPRESENTATION AND CASE RETRIEVAL …… .……… …… .……… …… .……… …… .…………… 61 4.1 INTRODUCTION .………… .………… … .………… .……. 61 4.2 KNOWLEDGE REPRESENTATION OF INCIDENT CASES AND RISK ASSESSMENT TREES .………… .………… … .………… .… . 61 4.2.1 Modelling Approach for the Lessons Learned .… .………… .… . 62 4.2.2 Modelling Approach for the Context of Lessons Learned …… .… . 63 4.2.2.1 Indexing Vocabulary …………………………………………… 64 4.2.2.2 Indices …… .… .…… .… .…… .… .…… .… 65 4.2.3 Implementation of the SKMS Case Base …………………………… 69 4.3 CASE RETRIEVAL .………… .………… … .………… .… 71 4.3.1 Overview of Case Retrieval Approaches .… .………… .… . 71 4.3.2 Similarity Functions for Nominal Attributes … .………… .… 74 4.3.3 Similarity Scoring in the SKMS … .………… .………………. 78 4.3.4 Global Similarity Score … .………… .……………………… . 85 4.3.5 Implementation of Case Retrieval in SKMS ……………………… . 88 4.4 CONCLUSIONS .………… .………… … .………… .……… 89 iv CHAPTER 5. ADAPTATION AND UTILISATION OF RETRIEVED CASES …… .……… …… .……… …… .……… …… .……………………. 91 5.1 INTRODUCTION ………… .………… … .………… .…… 91 5.2 ADAPTATION DURING HAZARD IDENTIFICATION ……………… 92 5.2.1 Adaptation of Retrieved Risk Assessment Tree …………………… . 93 5.2.2 Adaptation of Retrieved Incident Cases …………………… . 96 5.3 ADAPTATION DURING RISK ANALYSIS …………………… 100 5.3.1 Adaptation for Estimation of Likelihood Values …………………… 103 5.3.2 Statistical Model of Construction Incidents …………………… 104 5.3.2.1 The Poisson Process Model ……………………………………. 107 5.3.2.2 Partitioned Poisson Model …………………………………… . 110 5.3.3 Bayesian Approach for Adaptation of Likelihood Values …………. 113 5.4 CONCLUSIONS .………… .………… … .………… .……… 119 CHAPTER 6. VALIDATION CASE STUDY ………………………………… . 121 6.1 INTRODUCTION ………… .………… … .………… .…… 121 6.2 CASE BASE FOR CASE STUDY ………… … .………… .… 121 6.3 CASE STUDY ………… .………… … .………… .……… 123 6.3.1 Case Retrieval ……… .………… … .………… .……… 124 6.3.2 Hazard Identification .………… … .………… .………… 130 6.3.3 Risk Analysis ……… .………… … .………… .……… . 133 6.3.3.1 Adjustment of likelihood values to ensure consistency ……… . 135 v 6.3.3.2 Bayesian updating ……… .……… .……… .……… 137 6.4 DISCUSSIONS ……………………………………………………………. 145 6.4.1 Retrieval ……… .………… … .………… .…………… 147 6.4.2 Hazard Identification ……… … .………… .…………… 148 6.4.3 Risk Analysis .… .……… … .………… .……………… 148 6.5 CONCLUSIONS .………… .………… … .………… .……… 150 CHAPTER 7. CONCLUSIONS AND RECOMMENDATIONS ……………… 151 7.1 CONCLUSIONS .………… .………… … .………… .……… 151 7.2 LIMITATIONS AND RECOMMENDATIONS .… .………… .……… 156 REFERENCES ……………………………………………………………………. 161 LIST OF PUBLICATIONS ………………………………………………………. 170 APPENDIX 1: THE MODIFIED LOSS CAUSATION MODEL (MLCM) TAXONOMY …………………………………………………………………… . 171 APPENDIX 2: STATISTICAL RESULTS OF ANALYSIS ON 140 FATAL ACCIDENTS …………………………………………………………………… 178 APPENDIX 3: SEMANTIC NETWORKS ……………………………………… 184 APPENDIX 4: VALIDATION OF THE POISSON DISTRIBUTION FOR CONSTRUCTION INCIDENTS ………………………………………………… 188 APPENDIX 5: RISK ASSESSMENT TREE AFTER HAZARD IDENTIFICATION ADAPTATION ……………………………………………. 196 APPENDIX 6: RESULTS OF BAYESIAN UPDATING ………………………. 206 vi SUMMARY The construction industry is renowned for its poor safety records. One of the main strategies that can help to improve the safety performance of the industry is to ensure continual improvement of project safety management systems (SMS). This research proposes two levels of safety knowledge feedback that can facilitate the continual improvement of SMS. The first level of feedback refers to effective and thorough incident investigation after incident occurrence. The incident investigation should lead to an evaluation and improvement of the SMS that had failed and caused the incident. The second level of feedback is focused on ensuring that valuable safety knowledge in the form of safety plans and incident investigation reports are made available and useable for new project safety planning processes. Effective implementation of the second level of feedback would facilitate transfer of safety knowledge across projects and learning from past mistakes. To facilitate the two levels of feedback, this research developed an incident causation model, known as the Modified Loss Causation Model (MLCM), which can be used to structure a thorough incident investigation process (first level of feedback) and act as a knowledge framework that facilitates the feedback of safety knowledge during new project safety planning (second level of feedback). The MLCM had been developed based on an in-depth literature review and evaluation of 140 actual accident cases obtained from Singapore’s Ministry of Manpower. To realize the second level of feedback, a novel case-based reasoning (CBR) approach of risk assessment was developed. The CBR approach was designed to facilitate the Job Hazard Analysis (JHA) method of risk assessment so that the approach is aligned vii with the norm of structuring construction project plans based on activities. The key components of the CBR approach are: (1) a detailed MLCM-based knowledge representation scheme that can be used to capture and abstract key safety knowledge from incident cases and past risk assessments, (2) a case retrieval mechanism based on customized similarity scoring functions, (3a) hazard identification adaptations that facilitate automatic deletion of irrelevant parts of retrieved cases and integration of all relevant cases, and (3b) risk analysis adaptation that uses the Bayesian approach to integrate both subjective and objective estimates of likelihood to produce a balanced estimation of risk values. The CBR approach is implemented in a prototype system known as the Safety Knowledge Management System (SKMS). The prototype SKMS was applied on a case study to validate the proposed concepts. The case study is based on a typical work scenario in the construction industry and the case base contained 59 incident cases and 10 risk assessments obtained from different industry sources. The case study shows that based on the relatively small amount of cases, the SKMS is able to retrieve and fully utilize available cases to produce a reasonably thorough risk assessment tree. The case study also demonstrates that a balanced estimation of risk based on both objective and subjective sources can be derived and used to systematically prioritise safety efforts on site. viii BIBLIOGRAPHY 106 IEEE International Conference on Robotics and Automation, vol. 1, pp. 339–405, 1998. [26] Q. Zeng, C.L. Teo, B. Rebsamen, and E. Burdet, “Design of a collaborative wheelchair with path guidance assistance,” IEEE International Conference on Robotics and Automation (ICRA), pp. 877–882, 2006. [27] B. Rebsamen, A Brain Controlled Wheelchair to Navigate in Familiar Environments, Ph.D. Thesis, Department of Mechanical Engineering, National University of Singapore, 2008. [28] J. Borenstein, and L. Feng, “Measurement and correction of systematic odometry errors in mobile robots,” IEEE Transactions on Robotics and Automation, vol. 12, no. 6, pp. 869–880, 1996. [29] J.W. Yee, Collaborative Wheelchair Assistant using Barcode Localization, Bachelor Thesis, Department of Mechanical Engineering, National University of Singapore, 2006. [30] C.K. Chui, and G. Chen, Kalman filtering: with real-time applications, 2nd ed. Berlin: Springer-Verlag, 1991. [31] A. Micaelli, and C. Samson, “Trajectory tracking for unicycle-type and twosteering-wheels mobile robots,” Tech. Rep. 2097, INRIA-Sophia Antipolis, pp. 1–17, 1993. [32] B. Long, B. Rebsamen, E. Burdet, C.L. Teo, and H.Y. Yu, “Elastic path controller for assistive devices,” In Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology, 2005. [33] B. Long, B. Rebsamen, E. Burdet, and C.L. Teo, “Development of an elastic path controller,” in IEEE International Conference on Robotics and Automation (ICRA), 2006. [34] M.G. Cox, “The numerical evaluation of B-splines,” J. Institute of Mathematics and its Applications, 10:134–149, 1972. NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE BIBLIOGRAPHY 107 [35] C. de Boor, A Practical Guide to Splines, Springer-Verlag, 1978. [36] H. Asada, C.C. Federspiel, and S. Liu, “Human centered control in robotics and consumer product design,” ASME Journal of Dynamic Systems, Measurement, and Control, vol. 115–2(B), pp. 271–280, 1993. [37] A. Bonci, S. Longhi, A. Monteriu, and M. Vaccarini, “Navigation system for a smart wheelchair”, Journal of Zhejiang University: Science, vol A, no. 2, pp. 110–117, 2005. [38] A. Civit-Balcells, F. 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Teo, “Collaborative path planning for a robotic wheelchair,” Disability and Rehabilitation: Assistive Technology, 3(6): 315-24, 2008. • Q. Zeng, C.L. Teo, B. Rebsamen, and E. Burdet, “A collaborative wheelchair system,” IEEE Transactions on Neural Systems and Rehabilitation, vol. 16, no. 2, April. 2008. • Q. Zeng, E. Burdet, and C.L. Teo, “Evaluation of a collaborative wheelchair system in cerebral palsy and traumatic brain injury users,” Neurorehabilitation and Neural Repair, 2008. Conference papers • Q. Zeng, E. Burdet, B. Rebsamen, and C.L. Teo, “Design of a collaborative wheelchair with path guidance assistance,” in IEEE International Conference on Robotics and Automation (ICRA), pp. 877–882, 2006. • Q. Zeng, C.L. Teo, B. Rebsamen, and E. Burdet, “Evaluation of the collaborative wheelchair assistant system,” in IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 601–608, 2007. NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE List of Publications 110 • Q. Zeng, E. Burdet, B. Rebsamen, and C.L. Teo, “Experiments on collaborative learning with a robotic wheelchair,” in International Convention for Rehabilitation Engineering & Assistive Technology (i-CREATe), pp. 57–62, 2007. • Q. Zeng, E. Burdet, and C.L. Teo, “User evaluation of a collaborative wheelchair system,” in 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’08), pp. 1956–1960, 2008. • Q. Zeng, C.L. Teo, and E. Burdet, “Is the collaborative wheelchair adapted to cerebral palsy and traumatic brain injury subjects?,” in 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’08), pp. 1965–1968, 2008. In addition, collaborative work with Brice Rebsamen in the context of his PhD thesis [27] resulted in one journal paper [47] and three conference papers [48, 49, 50]. NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE 111 Appendix A. List of Existing Robotic Wheelchairs During the last decade, a great effort was concentrated world-widely towards developing automated wheelchair with some degree of navigational intelligence. This section gives a brief review of other research on robotic wheelchairs, identified by a search of the literature. (1) TAO Project (Applied Artificial intelligence, Inc., Canada) Tao project [8] developed an add-on intelligent system that can be installed on any standard powered wheelchair with minimum modifications. The TAO wheelchair performs landmark-based navigation in autonomous mode. The system uses two processor boxes: one for vision and one for non-vision behavior generations. Two CCD color cameras are used for vision system. Several bump sensors and 12 infrared sensors are equipped for detecting obstacles close to the chair. A subsumption approach has been implemented, under which several behaviors emerge, including collision avoidance, door passage and wall following. A keypad and miniature television set are installed temporally to enter instructions and for monitoring. The user can override the control in autonomy mode with a joystick. (2) Navchair (University of Michigan, U.S.) The Navchair [9] navigates indoor environments with three operating modes: general obstacle avoidance, door passage and automatic wall following. Sonar sensors have been used to create a map of the chair’s surroundings. The system NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE Appendix A. Existing Research on Robotic Wheelchairs 112 provides shared-control where a human is responsible for path planning and most of the navigational responsibilities while the NavChair could automatically adapt the correct operating mode based on user behavior and environmental status. The user can use either a joystick or voice commands as the access method. (3) Omni (University at Hagen, Germany) The OMNI project [10] aimed at developing an advanced wheelchair with high maneuverability and navigational intelligent, thus well suited for vocational rehabilitation. The chair can move in any direction, with the linear motion being combined with a rotation around its center. The system assists control through obstacle avoidance, walling following, door passage and limited back tracing of the most recent manoeuvres. The drive of this system is based on a custom-designed omni-directional wheelchair. Ultrasonic and infrared sensors have been used for environmental analysis. A modular human-machine-interface is able to connect different input devices, subject to different users’ abilities. (4) Sharioto (K.U. Leuven, Belgium) Sharioto [11] is a semi-autonomous wheelchair. Different types of distance sensors are used to detect features in the environment (ultrasonic sensors, infrared sensors, and a ‘Lidar’ infrared scanner) and a gyroscope for heading correction. The system provides behaviors including collision avoidance, obstacle avoidance, wall following, docking at a table. Moreover, the system implicit helps its user by estimation of the user intentions on these behaviors. The behavior changes are triggered by user signal and sensor information. Work continues on designing good activation function for each behavior. (5) VAHM (University of Metz, France) VAHM [12] operates in a semiautonomous or autonomous mode. Mode decisions are made manually. VAHM uses multiple representations of environment (topological, metric) and infrared beacons for path planning. In semiautonomous mode, the system provides wall following and obstacle avoidance. In autonomous mode, the system performs global path planning w free from non-modeled objects. Ul- NATIONAL UNIVERSITY OF SINGAPORE SINGAPORE Appendix A. Existing Research on Robotic Wheelchairs 113 trasonic transducers and contact sensors are installed for accurate position and local primitives. The human-machine-interaction is carried out through a LCD screen. The robot’s reachable environment may be represented geometrically seen from above. The user can point the navigation target with a proportional sensor or through a screen scanning stopped by a switch. (6) SmartChair (University of Pennsylvania, U.S.) The SmartChair [13] navigates autonomously with a map of the environment. The system consists of a vision-based virtual interface and a suite of sensors (laser range finder and shaft encoders). An omni-directional camera, mounted over the user’s head, allows the user to view 360 degrees around the chair. A projector system displays the map image on the laptray and enables the user to intervene the system in real time by clicking on a point on the map image during the execution of an autonomous task. (7) Senario (TIDE, Finland) The Senario wheelchair [14] navigates indoor environment in a semi-autonomous or fully autonomous mode. In semi-autonomous mode, the system accepts incremental commands from the user, and in fully autonomous mode, the system accepts commands like ‘go to goal’ and then automatically plans and executes a path to the destination, avoiding all obstacles and risks on the way. There are 13 ultrasonic sensors, two in[...]... data flow diagram of the SKMS 7 Retrieval Incidents knowledge base Incidents Retrieve relevant cases Safety plans Safety plans knowledge base Case Base Codified incident case Retrieved cases Adaptation Adaptation of relevant cases Incident Investigation Case- Based Reasoning Safety plans Adapted solutions Safety Planning Investigation reports Completed safety plan Codify safety plans Codify incident case. .. causation model and a common knowledge representation scheme to abstract and capture safety knowledge in incident investigation reports and past safety plans; 2 to propose an intelligent retrieval method that can automatically identify and retrieve relevant past experiences; 3 to propose adaptation strategies to contextualise the retrieved cases for: (a) hazard identification, and (b) risk analysis; and... hierarchies Such an approach allows meticulous analysis to be executed 2.2.2 Failure Modes and Effects Analysis (FMEA), and Hazard and Operability Study (HAZOP) Failure Modes and Effects (FMEA) and Hazard and Operability Study (HAZOP) are similar risk assessment approaches Both adopt a systematic component-bycomponent evaluation of an engineering system, where the effects, probability and severity of a. .. analysis (ETA) and probabilistic risk analysis (PRA) Qualitative methods include methods like hazard and operability study (HAZOP), what-if analysis, and job hazard analysis (also known as job safety analysis) (Harms-Ringdahl 1993; Ayyub 2003) However, whether the risk assessment method is quantitative or not often depends on whether the risk assessment team utilises a quantitative scale when estimating... feedback can then be achieved To demonstrate the feasibility of the proposed approach, a prototype SKMS will be developed and verified through a case study (Objective 4) 6 Based on the literature review (chapter 2) on Information Technology (IT) and Artificial Intelligence (AI), Case Based Reasoning (CBR) (sub-branch of AI) has similar foundational principles as the proposed approach and will be able to. .. Incident Causation Model Research Methodology for Research & Development of MLCM Validate Proposed Model - Research design: Case studies - Method of data collection: Analysis of past documents Apply Model to Develop Approach to Construction Risk Assessment Propose Approach to Construction Risk Assessment Research Methodology for Research & Development of CBR Approach Validate Proposed Approach - Research... methodologies range from quantitative to qualitative types Quantitative methods usually quantify the risk values based on measurable frequency and severity scales, while qualitative methods uses broad non-measurable categories to indicate the level of risk, frequency and severity Quantitative methods include methods 14 such as failure modes and effects analysis (FMEA), fault tree analysis (FTA), event tree analysis... abstracted into a manageable codified form, with the appropriate indexes tagged to the case to facilitate retrieval Besides codification, the retrieval mechanism also requires careful considerations In order to recall sufficient and appropriate cases the retrieval mechanism must be able to handle inexact matching intelligently Past cases that are retrieved will need to be adapted to the current context... and safety planning Incident investigation acts as a source of data for the SKMS, where investigation reports are fed into the SKMS On the other hand, safety planning teams use adapted solutions from the SKMS, and at the same time they also provide the completed safety plans as input to the case base Thus, safety planning acts both as a sink interface and a source interface Figure 1.3 Context level data... case studies, this portion of the research used a relatively large number of cases to validate the MLCM However, the large number of cases is warranted because statistics need to be generated from the cases studies for analysis Furthermore, it may be argued that the 140 cases is still a relatively small sample (as in most case studies) compared to the wide variety of construction incidents 10 Research . that facilitate automatic deletion of irrelevant parts of retrieved cases and integration of all relevant cases, and (3b) risk analysis adaptation that uses the Bayesian approach to integrate. (CBR) approach of risk assessment was developed. The CBR approach was designed to facilitate the Job Hazard Analysis (JHA) method of risk assessment so that the approach is aligned vii with. key safety knowledge from incident cases and past risk assessments, (2) a case retrieval mechanism based on customized similarity scoring functions, ( 3a) hazard identification adaptations that