AI Approaches for Predictive Maintenance in Road Bridge Infrastructure

Efficient maintenance of transportation networks relies on smart technologies to ensure safety and longevity. Traditional corrective maintenance methods are often insufficient, necessitating advanced predictive approaches driven by AI technologies. However, existing literature lacks a comprehensive evaluation of these AI-driven strategies, with sustainability considerations often overlooked. To address this, we have executed a critical literature survey in which we evaluate all models with the metrics of cost, performance, ease of implementation, etc. Our study examines various predictive maintenance models, including Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), Stochastic Models,Particle Swarm Optimisation and Constrained Programming [1]–[5]. Our findings conclude with the proposition of our proposed model, which integrates ANN and CNN, along with Life Cycle Assessment to incorporate sustainability. By evaluating performance, cost-effectiveness, and practicality, we aim to develop comprehensive AI-driven main-tenance strategies. We focus on the accuracy, complexity, and scalability of each model and assess their ability to address road bridge infrastructure maintenance challenges. Additionally, we evaluate the sustainability implications, providing insights for future research and practice. This study aims to bridge the gap in the literature and contribute to the development of sustainable predictive maintenance strategies leveraging AI technologies, and propose the predictive road maintenance model workflow, which will further lead to build a prototype of AI driven technologies based predictive model.