Multifaceted Optimization for Bridge Inspection Process

Abstract

Regular bridge inspections are essential for ensuring user safety and maintaining the integrity of bridge decks. The effective planning of this process requires careful consideration of various parameters, such as selecting appropriate inspection techniques to accurately identify necessary interventions and implementing effective traffic control strategy to minimize an inspection’s economic, social, and environmental implications, including traffic disruption, fuel consumption, and pollutant emissions. Nevertheless, previous studies only considered part of these parameters and neglected the rest, limiting the efficient application of their methodologies in practice. This paper aims to bridge this gap by proposing a comprehensive multiobjective simulation-based optimization model that integrates all relevant parameters. The model simultaneously optimizes various parameters, including inspection techniques, overtime hours, crew size, and traffic control strategies, to enhance system performance, that is, maximizing accuracy and confidence in test results while minimizing inspection duration, cost, and traffic disruptions. The multiobjective particle swarm optimization approach was employed to build the optimization algorithm. A discrete event simulation engine was integrated into the optimization algorithm to mimic the inspection procedures and estimate inspection duration and cost. The model was tested on a network of ten bridges. The findings revealed that using ground penetrating radar with 0.3048-m (1-ft) test spacing effectively addressed the scope of the inspection. In contrast, using impact echo [0.3048 m (1 ft)] and half-cell potential [0.3048 m (1 ft)] provided highly accurate inspections but longer inspection time/traffic delays and higher costs. The results demonstrated the efficiency of the proposed model in balancing competing objectives. The model not only enhances the efficiency of bridge inspections but also significantly reduces inspection cost and time. This study presents a novel decision support model designed to assist bridge authorities and consultants in optimizing the forthcoming inspection activities. The proposed model aims to simultaneously optimize several inspection parameters, including utilized inspection techniques, overtime hours, inspection crew size, and traffic control strategy. The model can significantly streamline bridge inspection processes, potentially leading to reduced inspection duration and costs while maintaining high-quality inspections. Integrating a traffic perspective into the scope of the model supports efforts to relieve traffic congestion caused by inspection activities, particularly for bridges located in major cities and highways. This integration helps maintain efficiency in the transportation system and reduce fuel consumption, pollutant emissions, and traveling time. The proposed model is supported by an automated tool to facilitate its application and ensure broad adoption. The findings of this study are invaluable for bridge authorities seeking to optimize the operational cost of bridge networks while ensuring public safety.