Multi-Objective Optimized GAN-Bayes Model for Predicting Construction Accident Risk - He, Lanfei; Zhou, Li; Huang, Zhenxi; Zhou, Yingbo; Ma, Li; Li, Lvman (Informatica, 2025)
Architectural engineering safety accident risk prediction is critical for proactive risk management. Traditional models often suffer from insufficient prediction accuracy, hindering effective risk prevention. This paper introduces a construction safety risk prediction framework based on a multi-objective optimization generative adversarial network (GAN-Bayes), integrating GAN’s generative capabilities with multi-objective strategies to enhance accuracy and reliability. Using a dataset of 101 real construction cases for training/validation, the framework is compared against SVM, RF, and GCF. Experimental results show significant improvements: the GAN-Bayes framework achieves 92.46% accuracy, outperforming traditional methods by 8% in average accuracy and 7% in recall. Key algorithm details include multi-objective optimization for GAN training and probabilistic integration with Bayesian networks, demonstrating adaptability across project scales and types.