Unsupervised learning-based damage assessment of full-scale civil structures under long-term and short-term monitoring. (1st April 2022)

• Record Type: Journal Article
• Authors: Hassan Daneshvar, Mohammad; Sarmadi, Hassan
• Is Part Of: Engineering structures. Volume 256(2022)
• Publication Date: 2022-04-01
• Subjects: Structural health monitoring -- Machine learning -- Unsupervised novelty detection -- Nearest neighbour -- Local density -- Anomaly score -- Threshold -- Semi-parametric extreme value theory; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114059
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A novel unsupervised anomaly detector along with a new anomaly index is proposed to detect damage in full-scale structures. A non-parametric approach is developed to select adequate nearest neighbors through a criterion called cumulative distance participation factor. A new probabilistic method is proposed to estimate an alarming threshold based on the semi-parametric extreme value theory. Major advantages of the proposed methods include high damage detectability, non-parametric characteristics, and accuracy in decision-making. Applicability to other fields of science and engineering for anomaly detection. Abstract: Machine learning has become an influential and useful tool for many civil engineering applications, particularly structural health monitoring (SHM). For this reason, this article aims to propose a novel machine learning method in terms of unsupervised information-based anomaly detection for SHM under long-term and short-term monitoring. The crux of this method is to define a new anomaly score or information content by using the concepts of local density, unsupervised feature selection via a one-class nearest neighbor rule, local cutoff distance, and minimum distance value. A non-parametric approach is then proposed to choose adequate nearest neighbors of each feature and determine its local cutoff distance and local density. To detect damage through the concept of anomaly detection, it is necessary to compare the anomaly scores with an alarming threshold. Accordingly, a new probabilistic method under semi-parametric extreme value (SEV) theory is proposed to estimate a quantile from some extreme samples and use this quantile value as a threshold limit. Due to the importance of selecting adequate extreme samples, this process is carried out by a two-stage iterative approach. The major contributions of this article include: (i) proposing a novel unsupervised learning method in a non-parametric fashion, (ii) defining a new anomaly score for SHM applications, (iii) advancing an unsupervised nearest neighbor selection, and (iv) developing a new probabilistic threshold estimation based on the SEV theory without any model selection, modeling procedure, and parameter estimation. Dynamic and statistical features extracted from vibration data of two full-scale bridges are applied to validate the proposed methods along with several comparative studies. Results demonstrate that the methods presented here are effective and reliable tools for accurate SHM via high- and low-dimensional features with superiority over some well-known techniques.