The Use Of Machine Learning, Computational Methods, And Robotics In Bridge Engineering: A Review

In this review paper, the applications of machine learning, computational methods, and robotics to bridge design are considered to help improve structure integrity and resilience. It describes a variety of computational methods, including finite element analysis (FEA) and computational fluid dynamics (CFD), that have been used to calculate failure modes and evaluate the dynamic behavior of bridge structures in extreme conditions, such as earthquakes and floods. It also highlights robotics’ potential to streamline inspection techniques, showing new robotic systems for effective bridge monitoring. Additionally, it points out issues related to data shortages and implementation difficulty and presents future research priorities, such as the need for powerful machine learning algorithms and the use of Internet of Things (IoT) solutions for real-time monitoring. In summary, the paper highlights the life-changing impact of these technologies on the safety and reliability of bridge systems.