Application of BN and genetic algorithm in building construction safety evaluation

Abstract

Developing a safety evaluation model for construction is of utmost importance due to the increasing prevalence of safety issues on construction sites in a rapidly growing sector. Consequently, this research integrates Clonal Genetic Algorithm (CGA) and Bayesian Network (BN) into the current BIM technology for building construction to establish a comprehensive safety evaluation model for building construction. To develop a framework for assessing building safety, the study initially filters the factors impacting building safety through an advanced evolutionary algorithm. Subsequently, a Bayesian network is employed to understand the structure and parameters of the model. When compared to both the backpropagation neural network (BPNN) model and the genetic algorithm (GA) optimized neural network model, the CGA-BPNN model showed a network training error of approximately 0.09%. Additionally, the target error value was observed to be around 0.02%, and the genetic crossover probability of the CGA-BPPN model amounted to 0.6629. These results indicate small algorithm error and appropriate training time of the model, as well as higher accuracy. The CGA-BPNN model filters the evaluation indexes in the Bayesian network and assigns appropriate weights to accurately assess the safety status of the construction project.