Accident Modeling in Small-scale Construction Projects Based on Artificial Neural Networks
Message:
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 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.

Article Type:
Research/Original Article
Language:
English
Published:
Journal of Human, Environment and Health Promotion, Volume:5 Issue:3, 2019
Pages:
121 - 126
magiran.com/p2052099  
روش‌های دسترسی به متن این مطلب
اشتراک شخصی
در سایت عضو شوید و هزینه اشتراک یک‌ساله سایت به مبلغ 300,000ريال را پرداخت کنید. همزمان با برقراری دوره اشتراک بسته دانلود 100 مطلب نیز برای شما فعال خواهد شد!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی همه کاربران به متن مطالب خریداری نمایند!