Assessing and measuring the vulnerability of highway construction projects with BIM using artificial intelligence optimization algorithms

Abstract

Assessing the seismic susceptibility of urban highway and road networks is crucial for strengthening the most vulnerable parts of the road network in advance and effectively responding to and recovering from infrastructure system damage after a disaster. The paper suggests utilizing building information modeling (BIM) and artificial neural network (ANN) techniques to evaluate the seismic vulnerability of an urban road network. This assessment considers the spatial seismic hazard, the components' vulnerability, and the impact of structural damage on the network's functionality. The current article's primary objective was to assess road networks' susceptibility to earthquake hazards using neural networks and building information modeling (BIM) through a comprehensive and systematic approach. To determine the most precise and effective model, a comprehensive evaluation was conducted comparing BIM and ANN with the inclusion of advanced algorithms such as black hole algorithm (BHA), future search algorithm (FSA), particle swarm optimization (PSO), and wind-driven optimization (WDO). The current study's findings regarding implementing machine learning algorithms suggest that the WDO-MLP method achieved an accuracy of 0.9863. Furthermore, while evaluating the model's efficiency using the Area under the curve (AUC), the WDO-MLP algorithm demonstrated an efficiency of 0.98. The BHA-MLP, VS-MLP, and PSO-MLP algorithms demonstrated prediction accuracies of 0.9752, 0.986, and 0.9646, respectively, in assessing the vulnerability of highway construction. Thus, due to its superior accuracy, the WDO-MLP algorithm is the most precise and efficient method for predicting the vulnerability of highway construction during hazardous events. This algorithm can be precious and effective in aiding planners and policymakers in pre-crisis management decision-making.