Model-Order-Reduction Approach for Structural Health Monitoring of Large Deployed Structures with Localized Operational Excitations

TL;DR

This paper introduces a simulation-based classification method using a reduced-order model for structural health monitoring of large structures under localized excitations, achieving high accuracy in crack detection.

Contribution

It extends the PR-RBC technique for faster hyperbolic PDE solutions and develops correlation-based features for damage classification under operational conditions.

Findings

Test classification errors below 0.1% with large synthetic datasets

Effective damage detection on a bridge with moving vehicle simulation

Enhanced classification accuracy with reduced computational effort

Abstract

We present a simulation-based classification approach for large deployed structures with localized operational excitations. The method extends the two-level Port-Reduced Reduced-Basis Component (PR-RBC) technique to provide faster solution estimation to the hyperbolic partial differential equation of time-domain elastodynamics with a moving load. Time-domain correlation function-based features are built in order to train classifiers such as artificial neural networks and perform damage detection. The method is tested on a bridge example with a moving vehicle (playing the role of a digital twin) in order to detect cracks' existence. Such problem has $45$ parameters and shows the merits of the two-level PR-RBC approach and of the correlation function-based features in the context of operational excitations, other nuisance parameters and added noise. The quality of the classification task is enhanced by the sufficiently large synthetic training dataset and the accuracy of the numerical solutions, reaching test classification errors below $0.1\%$ for disjoint training set of size $7\times10^3$ and test set of size $3\times10^3$.