GNN for Structural Displacement Prediction

TL;DR

This paper introduces a Graph Neural Network framework for predicting structural displacements, offering a faster alternative to FEM with high accuracy for real-time structural health monitoring.

Contribution

The study develops a GNN-based approach that incorporates geometric and mechanical properties for direct load-response prediction, outperforming traditional neural networks.

Findings

GNN predicts displacements with high accuracy.

GNN outperforms conventional neural networks.

Framework enables real-time structural response estimation.

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.