Unsafe behavior is a leading factor in accidents, and the working environment significantly affects behaviors. However, few studies have focused on detailed mechanisms for addressing unsafe behaviors resulting from environmental constraints. This study aims to delineate these mechanisms using cognitive work analysis (CWA) for an elevator installation case study. Elevator installation was selected for study because it involves operations at heights: falls from heights remain a major cause of construction worker mortality. This study adopts a mixed research approach based on three research methodology stages. This research deconstructs the details of the working environment, the workers' decision-making processes, the strategies chosen given environmental conditions and the conceptual model for workers' behaviors, which jointly depict environment-behavior mechanisms at length. By applying CWA to the construction industry, environmental constraints can easily be identified, and targeted engineering suggestions can be generated.

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This paper proposed a method for determining the cohesive parameters of fibre-reinforced composite interfaces based on finite element analysis (FEA) and machine learning. 3D FEA models with different boundary conditions and 2D FEA models were created to simulate the process of microdroplet tests, and to compare their maximum reaction forces and the time costs. The proper FEA model that is accurate and efficient was adopted to establish the data set of machine learning. Machine learning based on the FEA data set was divided into two steps: feature selection and kernel ridge regression (KRR) prediction. Feature selection was carried out to confirm the validity of the features, to obtain the optimal parameter of KRR prediction and to quantitatively illustrate the effect of the cohesive parameters on the maximum reaction force. Interfacial shear strength (IFSS) and interfacial fracture toughness (IFFT) of a poly ( p-phenylene benzobisoxazole) (PBO) fibre-reinforced epoxy composite were successfully predicted by the KRR method without extra mechanical theories or assumptions.