Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling.

Alireza Entezami,Hassan Sarmadi,Behshid Behkamal,Stefano Mariani

doi:10.3390/s21051646

Abstract

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

Highlights

Civil structures such as buildings, bridges, towers, subways, tunnels, dams are valuable structural systems that play a crucial role in social life and transportation networks.Damage and deterioration/aging processes are serious hazards to the safety and health of these structures
The comparison between the results relevant to damage detection and those collected in Figures 12 and 14, proves that the proposed classical multidimensional scaling (CMDS)-based method is superior to the principal component analysis (PCA)-based one, because it does not require procedure to set any hyperparameter during the analysis, and in terms of the smaller error rates obtained
A data-driven method based on data partitioning and classical multidimensional scaling has been proposed to efficiently and robustly detect damage in structural systems, in the presence of environmental variability

Summary

Introduction

Civil structures such as buildings, bridges, towers, subways, tunnels, dams are valuable structural systems that play a crucial role in social life and transportation networks. Once damage-sensitive features have been extracted from the dataset, the final step of a data-driven SHM method is to analyze the features themselves for decision-making, providing outcomes in terms of early damage detection, localization, and quantification At this stage, different techniques can be adopted, including statistical distance metrics (e.g., the Mahalanobis distance [23,24] or the Kullback–Leibler divergence [10,21,25]), Bayesian approaches [26,27], artificial neural networks [28,29], principal component analysis [30,31], and clustering [32,33,34].

Classical Multidimensional Scaling

Proposed SHM Data-Driven Method via CMDS

ASCE Structure—Phase II

Z24 Bridge

Findings

Conclusions