Optimizing the inspection schedule for bridge networks

Optimizing the inspection route for a bridge network is a paramount factor in reducing inspection duration and cost, particularly for bridges located in a large geographical area. In this respect, this study presents a bridge network inspection planning model. The model aims to minimize the total inspection cost by reducing traveling distance, accommodation cost, wasted time, and inspection crew cost. The model was developed based on themain concept of themultiple traveling salesman problem. In this model, a discrete event simulation engine was built to estimate the inspection duration for each bridge in the network, whereas the genetic algorithm approach was used to optimize the inspection route. Python was used in coding the model steps. Web scraping technique was utilized to develop a geospatial information algorithm dedicated to extracting the actual driving distance between any two points in the inspection route. To tackle the limitations of previous models, the developed model considered several parameters, such as bridge inspection durations that are either shorter or longer than a day shift, variations in the accommodation fees, actual driving distance, minimum workload to assign a new inspection crew, and minimum time to allow starting inspection activities in a new bridge. Considering these parameters make the developed model comprehensive and more accurate. The model was tested against a real bridge network in the IL, USA, and it proved its effectiveness in optimizing the inspection route. The model provides strong guidance for consultants and authorities in charge of bridges in planning the upcoming inspection activities.