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ABSTRACTThe engineering design process has fundamentally impacted the life cycle of construction projects and notably, the engineering performance is significantly measured in delivering projects. Previous studies on engineering performance have established the cause–effect relationships between project variables and performance measures. Recently, the building information modeling (BIM) application has reformed how owners execute the engineering, construction, commissioning, and operation in the industry. There has been an increasing focus in finding the benefits of BIM on project performance, however, a minor focus has been given to engineering performance. This paper proposes an artificial neural network (ANN) machine learning multilayer perceptron (MLMP) method and linear regression (LR) that correlates the use of BIM with engineering performance for better construction project assessment. The conclusions reveal a high-level correlation measure between BIM use inputs and engineering performance outputs and further methods for evaluating the engineering performance. Furthermore, we achieved and validated the best prediction by leveraging data from 60 samples using the MLMP and LR models.

ABSTRACTBehavior-Based Safety (BBS) has been considered as a useful tool for safety management. In prior applications, workers’ behaviors have been observed by third-party consultants or senior safety personnel. Under their observations, workers will perform safe behaviors to avoid punishment; but once they are without supervision, they will quickly return to bad habits. Hence, this paper aims to introduce an adjusted BBS observation program in which the observation is made by the front-line workers. By this strategy, observation could be less conspicuous and continually deepen observers’ safety awareness meanwhile. Also, this paper presents regression analyses based on the data from 18 projects which have accumulated a total of 199,296 observations. The results show the higher the Amount of Recorded Unsafe Behaviors (ARUB) on a project, the lower the Total Recordable Case Rate (TRCR) of the project. According to this result, for projects with remarkably low ARUB, the company should conduct random safety audits to verify the implementation of this adjusted BBS observation program to ensure this program works effectively. In conclusion, the authors propose that the adjusted BBS observation program could be an additional leading indicator to reduce occupational construction accidents.