A Nonparametric Unsupervised Learning Approach for Structural Damage Detection

TL;DR

This paper introduces a novel nonparametric unsupervised learning method for structural damage detection in aging infrastructure, leveraging kernel density estimation and Bayesian optimization to improve accuracy.

Contribution

It proposes a new unsupervised SHM approach combining Kernel Density Maximum Entropy with Bayesian hyperparameter tuning and independent components analysis for multivariate data.

Findings

94% accuracy in damage detection on simulated data

Effective extension to multivariate space using ICA

Improved damage detection without needing damage data

Abstract

In a world of aging infrastructure, structural health monitoring (SHM) emerges as a major step towards resilient and sustainable societies. The current advancements in machine learning and sensor technology have made SHM a more promising damage detection method than the traditional non-destructive testing methods. SHM using unsupervised learning methods offers an attractive alternative to the more commonly used supervised learning since it only requires data of the structure in normal conditions for the training process. The density-based novelty detection method provides a statistical element to the damage detection process but it relies heavily on the accuracy of the estimated probability density function (PDF). In this study, a novel unsupervised learning approach for SHM is proposed. It is based on the Kernel Density Maximum Entropy method by leveraging Bayesian optimization for hyperparameter tuning and also by extending the method into the multivariate space by the use of independent components analysis. The proposed approach is evaluated on a numerically simulated three-story reinforced concrete moment frame, where 94% of accuracy is achieved in structural damage detection.