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The cognitive engineering principle suggests that the unsafe behaviors of construction workers are associated with numerous attributes, and 23 task demands and 12 capability attributes have been proposed in the construction worker’s behavior model (CWBM). Two models utilizing Logistic Regression (LR) and Artificial Neural Networks (ANN) were developed as accident prediction models, and the forecasting efficiencies of these two models were investigated. Robustness of these models was proven by verification. The results provide a basis for designing an in-depth study on the cognitive attributes influencing workers’ behaviors and expanding the choice of analysis techniques. [ABSTRACT FROM AUTHOR]

The use of marginal materials stabilized by cement for base course applications has grown rapidly. Marginal materials consist of marginal crushed rock (MCR), marginal lateritic soil (MLS), and reclaimed asphalt pavement (RAP). However, the use of cement will release high CO2 causing the greenhouse effect. This research investigates the stabilization of marginal crushed rock using cement (C) and fly ash (FA) as a green base course material. FA used as pozzolanic binders was obtained from the Mae Moh power plant in Thailand. The physical and engineering properties of MCR stabilized by OPC-FA were evaluated according to the specification of the Department of Highways in Thailand. The influencing factors studied included binder content of 1%, 3%, and 5% by weight of the crushed rock material, C/FA ratios of 100/0, 90/10, 80/20, 70/30, 60/40, and 50/50, and curing time of 7. The optimum ingredient of MCR stabilized by B was found at a binder content of 3% and C/FA ratios of 60/40. The strength development in MCR mixed with C and FA at all B content and C/FA ratio can be normalized by 28-day compressive strength. The proposed equation is useful for predicting the compressive strength of MCR mixed with C and FA. This research enables FA, which is a residue material, to be used as a green base course material. [ABSTRACT FROM AUTHOR]