Construction Safety Risk Prediction Using SAE-SMOTE Data Augmentation and Adaptive Weighted Naive Bayes

Safety risk prediction in construction is a fundamental basis for accident prevention, and its scientific rigor directly affects the effectiveness of safety management decision-making. However, traditional approaches rely heavily on subjective judgment in weight determination, while accident datasets are often constrained by limited sample sizes and imbalanced class distributions, which hinder model training performance. To address these limitations, this study proposes an improved model based on data augmentation, termed SAE-SMOTE-AWBN (SSA). The proposed approach employs SMOTE to alleviate data imbalance, integrates a stacked autoencoder (SAE) to enhance feature representation, and utilizes AWBN to perform probabilistic inference of risk factors alongside adaptive weight adjustment. Results obtained from five-fold cross-validation indicate that, compared with the 58.6% prediction accuracy achieved by traditional methods, the SSA model improves accident prediction accuracy to 78.3 ± 2.4%, thereby demonstrating the effectiveness and applicability of the proposed approach.