Finite Element Analysis of Perforated Prestressed Concrete Frame Enhanced by Artificial Neural Networks
Open Access
Abstract
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.Keywords
Funding Information
- National Natural Science Foundation of China (52178299)
This publication has 31 references indexed in Scilit:
- Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogate modelFrontiers of Structural and Civil Engineering, 2023
- Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concreteFrontiers of Structural and Civil Engineering, 2023
- A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motionComputational Mechanics, 2022
- Bridging finite element and deep learning: High-resolution stress distribution prediction in structural componentsFrontiers of Structural and Civil Engineering, 2022
- Predicting the capacity of perfobond rib shear connector using an ANN model and GSA methodFrontiers of Structural and Civil Engineering, 2022
- Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificial neural networkFrontiers of Structural and Civil Engineering, 2022
- Optimal CNN-based semantic segmentation model of cutting slope imagesFrontiers of Structural and Civil Engineering, 2022
- Digital image correlation-based structural state detection through deep learningFrontiers of Structural and Civil Engineering, 2022
- On design optimization for structural crashworthiness and its state of the artStructural and Multidisciplinary Optimization, 2016
- Predicting piezometric water level in dams via artificial neural networksNeural Computing and Applications, 2013