Open Access

ARTICLE

Structural Optimization of a Multi-Story Frame Structure Based on a Pre-Trained Physics-Informed Neural Network (PINN) Surrogate Model

An Xu¹, Zhixiong Liu¹, Hua Rong², Liang Han¹, Wei Shi³, Jun Huang³, Jiyang Fu^1,4,*

1 Research Center for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou, China
2 Central Research Institute of Building and Construction Co., Ltd., MCC Group, Beijing, China
3 China Construction Seventh Engineering Division Corp., Ltd., Zhengzhou, China
4 College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen, China

* Corresponding Author: Jiyang Fu. Email:

Computer Modeling in Engineering & Sciences 2026, 147(1), 9 https://doi.org/10.32604/cmes.2026.079375

Received 20 January 2026; Accepted 23 March 2026; Issue published 27 April 2026

Abstract

In structural optimization, data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost. However, conventional neural networks usually fail to capture key structural characteristics and are limited to predicting global responses (e.g., top displacement), but usually fail to achieve accurate internal force predictions with conventional training data volumes. As a result, most existing studies involving surrogate models did not concern internal force constraints. To address this issue, this study proposes a structural optimization framework based on a pre-trained Physics-Informed Neural Network (PINN) surrogate model. By embedding static equilibrium equation into the loss function, the model achieves higher predictive accuracy, particularly for internal forces, while pre-training accelerates convergence and enhances stability. Combined with an improved multi-swarm particle swarm optimization (MPSO) algorithm, the framework enables efficient optimization of multi-story frame structures under internal force and multiple other constraints. The application to a six-story frame structure validates its effectiveness: compared with a DNN-based model, the PINN-based model improves the coefficient of determination for internal force prediction from 0.8874 to 0.9937. These results demonstrate that the proposed method offers a promising approach for efficient optimization of multi-story frame structures.

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Keywords

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