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A construction project is mostly initiated by the needs of the client. In order to satisfy the client’s requirements in terms of time, cost, and quality, various procurement methods are recommended for selection to increase the chance of success for the complex sequence of activities. Design-build (D&B) integrates design and construction to overcome some of the hurdles inherent in the traditional design-bid-build method. It has been extensively used worldwide and its distinctive features are claimed to deal with the problems of the traditional design-bid-build method. An investigation into the determinants of successful D&B projects can therefore help set a benchmark study for industry practitioners to compare their project performance. To better indicate the success level of D&B projects, the writers developed a project success index for D&B projects in the Hong Kong context, which is assessed by the key project performance indicators of time, cost, quality, and functionality. Multiple regression analysis is then applied to show that the project nature, the effective project management action, and the adoption of innovative management approaches are the critical success factors for D&B projects. The identification of success criteria can furnish project participants with indicators to attain success for their D&B projects for benchmarking and control purposes. The predictors for D&B project success were also determined to enrich the knowledge base for the D&B procurement system. [ABSTRACT FROM AUTHOR]

Learning from incidents (LFI) is a process to seek, analyse, and disseminate the severity and causes of incidents, and take corrective measures to prevent the recurrence of similar events. However, the effects of LFI on the learner's safety performance remain unexplored. This study aimed to identify the effects of the major LFI factors on the safety performance of workers. A questionnaire survey was administered among 210 construction workers in China. A factor analysis was conducted to reveal the underlying LFI factors. A stepwise multiple linear regression was performed to analyse the relationship between the underlying LFI factors and safety performance. A Bayesian Network (BN) was further modelled to identify the probabilistic relational network between the underlying LFI factors and safety performance. The results of BN modelling showed that all the underlying factors were important to improve the safety performance of construction workers. Additionally, sensitivity analysis revealed that the two underlying factors-information sharing and utilization and management commitment-had the largest effects on improving workers' safety performance. The proposed BN also helped find out the most efficient strategy to improve workers' safety performance. This research may serve as a useful guide for better implementation of LFI practices in the construction sector.