Seismic Action Prediction of Structures Based on Deep Feedforward Neural Networks

In this study, we propose a structural seismic loading prediction method based on deep feedforward neural networks. Considering the urgent need for structural safety in Seismic engineering, we utilized finite element software for simulations and compared these with actual engineering data, generating a large amount of realistic seismic loading data. Through in-depth analysis of these data, we employed a multilayer perceptron model to establish an efficient prediction mechanism. Experimental results show that the proposed model outperforms existing models in prediction accuracy compared to traditional methods. This research provides a new approach for evaluating structural safety under seismic conditions and offers a theoretical basis for future intelligent building design and seismic performance optimization.