A neuro-fuzzy risk prediction methodology for falling from scaffold

Fall from height is one of the most significant safety issues in the construction industry, due to the high number of fatal injuries. Scaffolds are a leading cause and have one of the highest injury rates. Therefore, it is crucial to introduce preventive measures and strategies. This study introduces a hybrid approach that merges an Adaptive Neural Network-based Fuzzy Inference System (ANFIS) and a safety inspection checklist to identify risk factors and predict the risk of falling from scaffold on construction sites. Our findings indicate that platform, joints, ladders, personal protective equipment and guardrails are the most important factors. The approach can identify and assess key conditions and situations that have the greatest impact on fall risk. The hybrid ANFIS-checklist model is found to outperform the regression method in predicting fall risk. Experts can use also this approach in other safety areas to identify and predict workplace risk.