A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces

• Main Authors: Fakhradin Ghasemi, Omid Kalatpour, Abbas Moghimbeigi, Iraj Mohammadfam
• Format: Article
• Language: English
• Published: Iranian Journal of Health, Safety and Environment 2017-07-01
• Series: Iranian Journal of Health, Safety and Environment
• Subjects: Safety Behavior, Multilayer Perceptron, Artificial Neural Network, Predictive Model, Safety Attitude, Safety Knowledge.
• Online Access: http://ijhse.ir/index.php/IJHSE/article/view/260

The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73). The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance.