Research on the Prediction of Sustainable Safety Production in Building Construction Based on Text Data

Abstract

Given the complexity and variability of modern construction projects, safety risk management has become increasingly challenging, while traditional methods exhibit deficiencies in handling complex dynamic environments, particularly those involving unstructured text data. Consequently, this study proposes a text data-based risk prediction method for building construction safety. Initially, heuristic Chinese automatic word segmentation, which incorporates mutual information, information entropy statistics, and the TF-IDF algorithm, preprocesses text data to extract risk factor keywords and construct accident attribute variables. At the same time, the Spearman correlation coefficient is utilized to eliminate the multicollinearity between feature variables. Next, the XGBoost algorithm is employed to develop a model for predicting the risks associated with safe production. Its performance is optimized through three experimental scenarios. The results indicate that the model achieves satisfactory overall performance after hyperparameter tuning, with the prediction accuracy and F1 score reaching approximately 86%. Finally, the SHAP model interpretation technique identifies critical factors influencing the safety production risk in building construction, highlighting project managers’ attention to safety, government regulation, safety design, and emergency response as critical determinants of accident severity. The main objective of this study is to minimize human intervention in risk assessment and to construct a text data-based risk prediction model for building construction safety production using the rich empirical knowledge embedded in unstructured accident text, with the aim of reducing safety production accidents and promoting the sustainable development of construction safety in the industry. This model not only enables a paradigm shift toward intelligent risk control in safety production but also provides theoretical and practical insights into decision-making and technical support in safety production.