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This study explored the success variables (SVs) in construction partnering and the relationships among the SVs using structural equation modeling (SEM). Research results show that four successful factors (collaborative team culture, long-term quality perspective, consistent objectives, and resource sharing) have a significant influence on the success of construction partnering. Of the four factors, collaborative team culture and consistent objectives have the highest correlation. Collaborative team culture and long-term quality perspective have the lowest correlation. Additionally, good cultural fit has the most influence on characterizing collaborative team culture, commitment to continuous improvement has the highest influence in characterizing long-term quality perspective, clear understanding has the highest influence in characterizing consistent objectives, and availability of resource has the highest influence in characterizing resource sharing. The proposed SEM framework provides information which enables the users to control individual SV by considering their relationships with other SVs.

Falling is the most common one during bridge construction. Current safety management on site mainly relies on checklist assessment. Yet the assessment result is often influenced by the ability and experience of the evaluator, thus is not impossible to achieve consistent and systematic assessment objective. Moreover, most critical factors that can prevent occurrence of accidents cannot be found from existing safety management and assessment method. This paper built a Bayesian Network (BN) model by converting Fault Tree to assess the fall risk of bridge construction projects. We analyse falling factors and their relationships in Bayesian Network, and collect prior probability event and calculate the probability for the entire model. Using the model to analyse and validate with the current bridge projects under construc­tion, the results from Bayesian Network is consistent with that from conventional labour safety performance assessment. Therefore, the ability to manage site safety of the model is proven to be useful.