Accident Modeling in Small-Scale Construction Projects Based on Artificial Neural Networks

Background: several factors contribute to accidents in small-scale construction projects (sscps). the present study aimed to assess the influential factors in sscp accidents and introduce a model to predict their frequency. methods: in total, 38 sscps were within the scope of this investigation. the safety index of accident frequency rate (afr) causing 452 injury construction accidents during 12 years (2007-2018) was analyzed and modeled. data analysis was performed based on feature selection using pearson's χ² coefficient and spss modeler, as well as the artificial neural networks (anns) in matlab software. results: mean afr was estimated at 26.32 ± 14.83, and the results of both approaches revealed that individual factors, organizational factors, training factors, and risk management-related factors could predict the afr involved in sscps. conclusion: the findings of this research could be reliably applied in the decision-making regarding safety and health construction issues. furthermore, pearson's correlation-coefficient and ann modeling are considered to be reliable tools for accident modeling in sscps.