Finite Element Analysis of Perforated Prestressed Concrete Frame Enhanced by Artificial Neural Networks

With the rapid development of machine learning and data science, computer performance continues to improve. It has become possible to integrate finite element analyses and machine learning technology. In this study, a surrogate-based finite element method enhanced by a deep learning technique is proposed to predict the displacement and stress fields of prestressed concrete beams with openings. Physics-informed neural networks (PINNs) were used to conduct a finite element analysis for the prestressed concrete structures. The displacement and stress of all nodal points were extracted to train the surrogate-based model. Then, the surrogate-based model was used to replace the original finite element model to estimate the displacement and stress fields. The results from the trained neural networks are in good agreement with experimental data obtained in a laboratory. It is demonstrated that the accuracy and efficiency of the proposed PINNs are superior to conventional approaches.