Predicting Injury Severity in Construction Falls: Analyzing OSHA data with ANN and Resampling Techniques

Abstract

The construction industry, essential for economic growth, is notably hazardous, with falls from height (FFH) being the primary cause of injuries and fatalities. To enhance construction safety ANN, which is a Machine Learning (ML) application, can be employed. Typically, there are imbalances in accident data, for instance, more data is available on hospitalization and less data available on fatalities. Such imbalances can result in inaccurate findings from ML models. Past studies have addressed class imbalance in accident data but lacked focus on its impact on ML algorithms. This study considers fall-related accidents in construction by using recent data from OSHA to develop a predictive model for injury severity. After preprocessing of data, critical variables are identified using association analysis and an Artificial Neural Network (ANN) is employed to capture the complex, nonlinear relationships between variables. The study explores the impact of addressing class imbalance on the performance of different architecture of ANN model through three resampling techniques: Random under sampling (RUS), Random over sampling (ROS), and Synthetic Minority oversampling technique (SMOTE). Resampled data is then compared with raw data. Results indicate that worker age, occupation, fall distance, and working surface height significantly impact injury severity, while environmental factors and nature of the task do not significantly associate with accident outcomes. Simpler model architectures and RUS are more effective and provide the best balance between precision and recall in predicting injury severity. These findings are critical for the development of safetyrelated predictive models in future. This study will help relevant stakeholders such as safety managers to take proactive steps by focusing on the critical variables identified in analysis and to help them effectively manage fall accidents.