GNN for Structural Displacement Prediction

2026-05-08

AI Summary

This study addresses the high computational cost of traditional finite element methods (FEM) in structural displacement prediction, which hinders real-time monitoring applications. To overcome this limitation, the authors propose a data-driven approach based on graph neural networks (GNNs), wherein the structural system is represented as a graph—nodes correspond to structural joints and edges to structural members. The work introduces a novel GNN framework that uniquely integrates both geometric and mechanical properties into the graph representation for structural response prediction. Experimental results on a two-story frame structure dataset demonstrate that the proposed model efficiently and accurately predicts displacements and rotations, outperforming conventional neural networks and highlighting its potential as a computationally efficient surrogate for FEM.

Abstract

Accurate prediction of structural displacements under external loading is fundamental to structural health monitoring and seismic safety assessment. Although the finite element method (FEM) remains the prevailing approach because of its high accuracy, its considerable computational cost restricts its suitability for real-time monitoring applications. To address this limitation, this study proposes a data-driven framework based on Graph Neural Networks (GNNs), in which structural systems are represented as graphs with joints modeled as nodes and structural members as edges. By incorporating both geometric and mechanical properties into the graph representation, the proposed model learns the relationship between applied loads and structural responses directly from simulated data. A synthetic dataset was generated from a two-story frame structure using ANSYS, and both a conventional Neural Network (NN) and a GNN were trained for comparison. The results show that the proposed GNN framework predicts displacements and rotations with high accuracy and outperforms the NN model, demonstrating its potential as a fast and efficient alternative to traditional FEM-based analysis.

Problem

• structural displacement prediction
• real-time monitoring
• computational cost
• structural health monitoring
• seismic safety assessment

Innovation

• Graph Neural Networks
• Structural Displacement Prediction
• Data-driven Modeling
• Finite Element Method
• Structural Health Monitoring