Machine learning-based soil-structure interaction analysis of laterally loaded piles through physics-informed neural networks

This research adopts emerging machine learning techniques to tackle the soil-structure interaction analysis problems of laterally loaded piles through physics-informed neural networks (PINNs), which employs prior physical information in the form of partial differential equations during the model training, eliminating the tremendous data requirement in the traditional data-driven machine learning methods. The formulations to describe the problem are discussed, and the corresponding governing equations are derived. A PINN framework, including neural networks architecture and loss functions, is developed for the machine learning-based solution and elaborated with details. The corresponding model training process is presented, based on which the surrogate model construction and back analysis implementation are introduced to demonstrate the effectiveness and flexibility of the proposed method. This method has been demonstrated for its accuracy via several examples with benchmark solutions from the existing well-developed methods. Finally, a case study of the uncertainty evaluation of a laterally loaded pile is conducted to illustrate its high computational efficiency and advantages in potential engineering applications.