Higher-Order Multivariate Environmental Influences in Structural Health Monitoring

TL;DR

This paper investigates how environmental conditions influence not only the mean but also the higher-order statistical moments of structural health monitoring data, proposing and comparing two methods to identify and quantify these effects.

Contribution

It introduces two approaches, a random forest and a kernel-based method, for detecting multivariate environmental influences on SHM data covariances and correlations, with a comparative analysis.

Findings

Kernel-based approach achieves higher accuracy.

Random forest offers more robustness and interpretability.

Environmental effects extend beyond mean values to higher-order moments.

Abstract

System outputs such as eigenfrequencies or strain data, often used in structural health monitoring (SHM), not only react to damage but also depend on environmental conditions. When trying to correct for these confounding effects, it is often (at least implicitly) assumed that only the expected, i.e., mean, output values are affected by environmental conditions. However, the evaluation of real-world SHM data indicates that environmental conditions may influence not only the mean output but also higher-order statistical moments, particularly the variances of and the covariances and correlations between the output quantities, such as eigenfrequencies of different modes or strain sensors at different locations. To address these issues, we discuss two approaches for identifying and quantifying multivariate confounding effects on output covariances and correlations: a random forest and a nonparametric, kernel-based approach. We compare the two competing methods on both artificial and real-world SHM data, finding that the kernel-based approach achieves higher accuracy, but the random forest produces estimates that are more robust and sometimes easier to interpret.