Yang Miang Goh

Case-Based Reasoning Approach to Construction Safety Hazard Identification: Adaptation and Utilization

Risk assessment, consisting of hazard identification and risk analysis, is an important process that can prevent costly incidents. However, due to operational pressures and lack of construction experience, risk assessments are frequently poorly conducted. In order to improve the quality of risk assessments in the construction industry, it is important to explore the use of artificial intelligence methods to ensure that the process is efficient and at the same time thorough. This paper describes the adaptation process of a case-based reasoning (CBR) approach for construction safety hazard identification. The CBR approach aims to utilize past knowledge in the form of past hazard identification and incident cases to improve the efficiency and quality of new hazard identification. The overall approach and retrieval mechanism are described in earlier papers. This paper is focused on the adaptation process for hazard identification. Using the proposed CBR approach, for a new work scenario (the input case), a most relevant hazard identification tree and a set of incident cases will be retrieved to facilitate hazard identification. However, not all information contained in these cases are relevant. Thus, less relevant information has to be pruned off and all the retrieved information has to be integrated into a hazard identification tree. The proposed adaptation is conducted in three steps: (1) pruning of the retrieved hazard identification tree; (2) pruning of the incident cases; and (3) insertion of incident cases into the hazard identification tree. The adaptation process is based on the calculation of similarity scores of indexes. A case study based on actual hazard identifications and incident cases is used to validate the feasibility of the proposed adaptation techniques.

Case-Based Reasoning for Construction Hazard Identification: Case Representation and Retrieval

This paper proposes a case-based reasoning (CBR) approach to construction hazard identification that facilitates systematic feedback of past knowledge in the form of incident cases and hazard identification. This paper focuses on two of the key components of the CBR approach: (1) a detailed knowledge representation scheme, developed based on the modified loss causation model, to codify incident cases and past hazard identification and (2) an intelligent retrieval mechanism that can automatically retrieve relevant past cases. The detailed knowledge representation scheme presented herein is designed to model both incident cases and hazard identification so that both types of knowledge repository can be retrieved simultaneously and adapted for use. The scheme also includes a linguistic structure used to facilitate indexing of cases. The retrieval mechanism is based on the concept of similarity scoring. In this paper, a novel scoring technique based on semantic networks is presented. A case study is presented to demonstrate and validate the proposed approach.

Neural network analysis of construction safety management systems: a case study in Singapore

A neural network analysis was conducted on a quantitative occupational safety and health management system (OSHMS) audit with accident data obtained from the Singapore construction industry. The analysis is meant to investigate, through a case study, how neural network methodology can be used to understand the relationship between OSHMS elements and safety performance, and identify the critical OSHMS elements that have significant influence on the occurrence and severity of accidents in Singapore. Based on the analysis, the model may be used to predict the severity of accidents with adequate accuracy. More importantly, it was identified that the three most significant OSHMS elements in the case study are: incident investigation and analysis, emergency preparedness, and group meetings. The findings imply that learning from incidents, having well-prepared consequence mitigation strategies and open communication can reduce the severity and likelihood of accidents on construction worksites in Singapore. It was also demonstrated that a neural network approach is feasible for analysing empirical OSHMS data to derive meaningful insights on how to improve safety performance.

Overview and Analysis of Ontology Studies Supporting Development of the Construction Industry

AbstractBeing information-intensive, the construction industry has the feature of multiagents, including multiparticipants from different disciplines, multiprocesses with a long-span timeline, and multidocuments generated by various systems. The multistakeholder context of the construction industry creates problems such as poor information interoperability and low productivity arising from difficulties in information reuse. Many researchers have explored the use of ontology to address these issues. This study aims to review ontology research to explore its trends, gaps, and opportunities in the construction industry. A systematic process employing three-phase search method, objective analysis and subjective analysis, helps to provide enough potential articles related to construction ontology research, and to reduce arbitrariness and subjectivity involved in research topic analysis. As a result, three main research topics aligned with the ontology development lifecycle were derived as follows: information ...

Knowledge, Attitude, and Practice of Design for Safety: Multiple Stakeholders in the Singapore Construction Industry

AbstractThis paper aims to investigate the design for safety (DfS) knowledge, attitude, and practice (KAP) of multiple stakeholders, including architects, civil and structural (CS) engineers, mecha...

Editorial for special issue - 'systems thinking in workplace safety and health'.

Workplace safety and health (WSH) is an important issue in all ndustries and across the globe. The International Labor Organiation (2003) estimated that work-related accidents and illnesses ake 2 million lives each year and have an annual cost of US

Construction accident narrative classification: An evaluation of text mining techniques

1.25 rillion. A significant contribution of workplace deaths comes from eveloping countries, but major accidents like the Macondo oil pill and the Santiago de Compostela derailment in 2013 reminded he world that WSH performance remains a global challenge. The tudy of WSH has evolved from focusing on employee’s behavior nd ancestry to the utilization of engineered equipment, and more ecently, to the influence of systems on individual and group behavor and performance. However, due to the cross-disciplinary nature f WSH, the study of systems influence on WSH has been scattered cross different research domains with varying terminologies for he same ideas. This special issue of ‘systems thinking in workplace safety nd health’ aims to provide an avenue for WSH researchers and ractitioners from different domains to present the most recent evelopments of systems thinking to improve WSH. Fifteen papers ere accepted for publication and it can be observed that the term ystems thinking is intertwined with the concept of safety culture, afety climate, macroergonomics and management systems. The apers in this special issue include theoretical discussions on defiitions and concepts related to systems thinking in WSH, advanced ools and techniques to promote and instill systems thinking, pplication of systems thinking concepts on specific hazards and ndustries and studies on safety management systems and proesses. The following provides an overview of the papers published n this special issue.

Factors influencing unsafe behaviors: A supervised learning approach

Learning from past accidents is fundamental to accident prevention. Thus, accident and near miss reporting are encouraged by organizations and regulators. However, for organizations managing large safety databases, the time taken to accurately classify accident and near miss narratives will be very significant. This study aims to evaluate the utility of various text mining classification techniques in classifying 1000 publicly available construction accident narratives obtained from the US OSHA website. The study evaluated six machine learning algorithms, including support vector machine (SVM), linear regression (LR), random forest (RF), k-nearest neighbor (KNN), decision tree (DT) and Naive Bayes (NB), and found that SVM produced the best performance in classifying the test set of 251 cases. Further experimentation with tokenization of the processed text and non-linear SVM were also conducted. In addition, a grid search was conducted on the hyperparameters of the SVM models. It was found that the best performing classifiers were linear SVM with unigram tokenization and radial basis function (RBF) SVM with uni-gram tokenization. In view of its relative simplicity, the linear SVM is recommended. Across the 11 labels of accident causes or types, the precision of the linear SVM ranged from 0.5 to 1, recall ranged from 0.36 to 0.9 and F1 score was between 0.45 and 0.92. The reasons for misclassification were discussed and suggestions on ways to improve the performance were provided.

Incident Causation Model for Improving Feedback of Safety Knowledge

Despite its potential, the use of machine learning in safety studies had been limited. Considering machine learnings advantage in predictive accuracy, this study used a supervised learning approach to evaluate the relative importance of different cognitive factors within the Theory of Reasoned Action (TRA) in influencing safety behavior. Data were collected from 80 workers in a tunnel construction project using a TRA-based questionnaire. At the same time, behavior-based safety (BBS) observation data, % unsafe behavior, was collected. Subsequently, with the TRA cognitive factors as the input attributes, six widely-used machine learning algorithms and logistic regression were used to develop models to predict % unsafe behavior. The receiver operating characteristic (ROC) curves show that decision tree provides the best prediction. It was found that intention and social norms have the biggest influence on whether a worker was observed to work safely or not. Thus, managers aiming to improve safety behaviors need to pay specific attention to social norms in the worksite. The study also showed that a TRA survey can be used to extend a BBS to facilitate more effective interventions. Lastly, the study showed that machine learning algorithms provide an alternative approach for analyzing the relationship between the cognitive factors and behavioral data.