Multifaceted Optimization for Bridge Inspection Process

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.