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

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ALIZADEH SAVAREH BEHROUZ; MAHDINIA MOHSEN; GHIYASI SAMIRA; Rahimi Jmshid; SOLTANZADEH AHMAD

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Journal Paper

Paper Information

Journal: Journal of Human Environment and Health Promotion Year:2019 | Volume:5 | Issue:3 Page(s): 121-126

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Title

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

Author(s)

Keywords

Construction projects

Accident

Modeling

Abstract

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 χ 2 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 decisionmaking 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.

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