Title: Performance assessment of a bio-inspired anomaly detection algorithm for unsupervised SHM: application to a Manueline masonry church

Authors: Alberto Barontini; Maria Giovanna Masciotta; Paulo Amado-Mendes; Luís F. Ramos

Addresses: Department of Civil Engineering, ISISE, University of Minho, Campus de Azurém, Guimarães, 4800-058, Portugal ' Department of Civil Engineering, ISISE, University of Minho, Campus de Azurém, Guimarães, 4800-058, Portugal ' Department of Civil Engineering, ISISE, University of Coimbra, R. Luis Reis dos Santos 290, 3030-790 Coimbra, Portugal ' Department of Civil Engineering, ISISE, University of Minho, Campus de Azurém, Guimarães, 4800-058, Portugal

Abstract: Vibration-based techniques are commonly used in structural health monitoring (SHM) to assess the condition of structural systems and identify the presence of damage. Negative selection algorithms (NSAs) are bio-inspired methods which allow to automatise the damage detection process by classifying the monitored system's features as normal or abnormal. In this paper, an NSA with a non-random strategy for detector generation is tested on the monitoring data of a remarkable masonry church in Portugal. The work aims to make users aware of NSA potential, contributing to a diligent application of the method in terms of best algorithm instance definition. Different setting approaches for the algorithm parameters are discussed and compared, exploiting artificial outliers of the features distribution to assess the NSA performance. Such a strategy allows the optimisation of the algorithm in most of the civil engineering applications where no information about the features belonging to unhealthy scenarios is available.

Keywords: structural health monitoring; SHM; anomaly detection; damage identification; classification problems; negative selection algorithm; NSA.

DOI: 10.1504/IJMRI.2020.111798

International Journal of Masonry Research and Innovation, 2020 Vol.5 No.4, pp.468 - 496

Received: 29 Nov 2018
Accepted: 10 May 2019
Published online: 15 Dec 2020 *

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