Neural operator for structural simulation and bridge health monitoring

TL;DR

This paper introduces VINO, a neural operator based on Fourier Neural Operator, that acts as a digital twin for bridges, enabling accurate structural simulation and damage detection through learned mappings from response to damage fields.

Contribution

The study develops VINO, a novel neural operator model that combines simulation and experimental data for effective bridge health monitoring and damage localization.

Findings

VINO outperforms finite element models in predicting structural responses.

VINO accurately localizes and quantifies damages across multiple scenarios.

The approach demonstrates the practicality of data-driven structural health monitoring.

Abstract

Infusing deep learning with structural engineering has received widespread attention for both forward problems (structural simulation) and inverse problems (structural health monitoring). Based on Fourier Neural Operator, this study proposes VINO (Vehicle-bridge Interaction Neural Operator) to serve as the digital twin of bridge structures. VINO learns mappings between structural response fields and damage fields. In this study, VBI-FE dataset was established by running parametric finite element (FE) simulations considering a random distribution of structural initial damage field. Subsequently, VBI-EXP dataset was produced by conducting an experimental study under four damage scenarios. After VINO was pre-trained by VBI-FE and fine-tuned by VBI-EXP from the bridge at the healthy state, the model achieved the following two improvements. First, forward VINO can predict structural responses from damage field inputs more accurately than the FE model. Second, inverse VINO can determine, localize, and quantify damages in all scenarios, suggesting the practicality of data-driven approaches.