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Fall or tumble is one of the most common accidents in bridge construction. Failing to implement safety management and training effectively may result in serious occupational accidents. Current site safety management relies mostly on checklist evaluation; however, its effectiveness is limited by the experience and the abilities of the evaluators, which may not consistently achieve the goal of thorough assessment. Recently, several systematic safety risk assessment approaches, such as Fault Tree Analysis (FTA) and Failure Mode and Effect Criticality Analysis (FMECA), have been used to evaluate safety risks at bridge projects. However, these traditional methods ineffectively address dependencies among safety factors at various levels that fail to provide early warnings to prevent occupational accidents. In order to overcome the limitations of the traditional approach, in this paper a fall risk assessment model for bridge construction projects is developed by establishing a Bayesian network (BN) based on Fault Tree (FT) transformation. The model was found to provide much better site safety management ability by enabling better understanding of the probability of fall risks through the analysis of fall causes and their relationships in a BN. The system has been used to analyze and verify safety practices at five cantilever bridge construction projects currently under construction in Taiwan. It was found that BN analysis is consistent with the conventional safety performance assessment. In practice, based upon the BN analysis by inputting prior information about the site safety management, the probabilities of fall risks and their sensitive factors can be effectively assessed. Proper preventive safety management strategies could then be established to reduce the occurrences of fall accidents at the bridge construction projects. [ABSTRACT FROM AUTHOR]

Abstract: This paper tries to analyze common bridge construction defects, classify them into appropriate groups, and redefine them as a precautionary measure and means to improve quality in bridge construction. For this purpose, data on bridge construction since January 2007 were obtained from the evaluation report of the Public Construction Committee (PCC) of Taiwan. Bridge construction defects were classified according to their characteristics. A constraint-based clustering method and affinity diagram (KJ method) are proposed and used. This method can simultaneously treat mixed data types; moreover, it can incorporate user-specified constraints. The quality or safety issues, the unit-in-charge (Government authorities/project owners/contractor), and the properties of the defects (construction/audit/documents/others) are the sorting attributes. The constraint is avoiding empty clusters or clusters having very few objects. The results revealed five major defect classifications: safety and environment, construction site defects, supervision/control process, construction quality documents, and others. [Copyright &y& Elsevier]

Both mixed data types and cluster constraints are frequently encountered in the classification problems of construction management. For example, in a bridge let project, engineers generally group the bridges into several subgroups based on their proximities, structure type, material, etc. Moreover, constraints may be set for each cluster to ensure the project’s overall effectiveness. In this study, an effective clustering algorithm – the constrained k-prototypes (CKP) algorithm – is proposed to resolve the abovementioned problems. Several tests and experimental results have shown that CKP cannot only handle mixed data types but also satisfy user-specified constraints. In order to demonstrate the applicability of CKP, it is also applied to real-world problems in construction management. [Copyright &y& Elsevier]