Structural health monitoring by a novel probabilistic machine learning method based on extreme value theory and mixture quantile modeling

Abstract

This article proposes a novel probabilistic machine learning method based on unsupervised novelty detection for health monitoring of civil structures. The core of this method is based on extreme value theory (EVT) and mixture quantile modeling. Accordingly, a mixture quantile value by combining non-parametric and parametric quantile estimators is proposed as a new decision-making or novelty score. The non-parametric estimator relies on an empirical quantile function, while the parametric estimator stems from modeling a generalized extreme value distribution. Generally, the proposed method is composed of some main steps; that is, calculating distances between feature samples, sorting the distances in ascending order, changing their signs for providing negated quantities, selecting some negated maximum distances or extreme values in an iterative algorithm by using a goodness-of-fit test based on Kullback-Leibler information, and computing a mixture quantile. Furthermore, an EVT-based approach is proposed to estimate an alarming threshold. The major innovation in this article is to develop a novel probabilistic novelty detector with a new score for decision-making. The advantages of the proposed method contain preparing discriminative novelty scores for damage detection, dealing with the major challenge of environmental and/or operational variability, estimating a reliable threshold, and implementing both the procedures of decision-making and threshold estimation within a single framework. Dynamic and statistical features of two full-scale bridge structures are utilized to validate the proposed method along with comparative studies. Results demonstrate that the method is a reliable and influential tool for health monitoring of civil structures under varying various environmental and/or operational conditions.