Hybrid Particle Swarm Optimization and K-Means Analysis for Bridge Clustering Based on National Bridge Inventory Data

Abstract

Because of budget constraints for maintaining highway bridges in the United States, it is necessary to accurately monitor bridge structures so that repair and rehabilitation strategies can be performed. In this paper, an optimization approach based on the hybrid of the metaheuristic particle swarm optimization and the k-means method (KPSO) in data clustering is presented. The goal is to group bridges by similar structural deficiency attributes by minimizing the sum of squares error associated with assigning data points to each cluster and the determination of the most suitable number of clusters. The presented approach was compared to the basic version of particle swarm optimization (PSO) and the traditional clustering method k-means. The algorithms were tested using the National Bridge Inventory (NBI) database. The results show that KPSO provides better results in terms of the objective function as well as showing an opportunity to implement optimization techniques for data analysis in civil infrastructure systems.