On risk-based active learning for structural health monitoring

Abstract

A primary motivation for the development and implementation of structural health monitoring systems, is the prospect of gaining the ability to make informed decisions regarding the operation and maintenance of structures and infrastructure. Unfortunately, descriptive labels for measured data corresponding to health-state information for the structure of interest are seldom available prior to the implementation of a monitoring system. This issue limits the applicability of the traditional supervised and unsupervised approaches to machine learning in the development of statistical classifiers for decision-supporting SHM systems.The current paper presents a risk-based formulation of active learning, in which the querying of class-label information is guided by the expected value of said information for each incipient data point. When applied to structural health monitoring, the querying of class labels can be mapped onto the inspection of a structure of interest in order to determine its health state. In the current paper, the risk-based active learning process is explained and visualised via a representative numerical example and subsequently applied to the Z24 Bridge benchmark. The results of the case studies indicate that a decision-maker's performance can be improved via the risk-based active learning of a statistical classifier, such that the decision process itself is taken into account.

Domain-adapted Gaussian mixture models for population-based structural health monitoring

“…9b and c show that the key damage and retrofit states have been well identified, with the GMM identifying the onset of the smallest damage extent on the Z24, a lowering of the pier by 20 mm on the 10 th August 1998 (red line). This result is a significant achievement, given that the model has been inferred on a reduced dataset (only the first and third natural frequencies), and provides state-of-the-art performance when compared to existing methods applied to the Z24 in the literature [23][24][25][26][27][28][29][30][31][32]. As found in previous analysis on the Z24 dataset using an infinite Gaussian mixture model [30], the Z24 ambient normal condition is non-Gaussian and can be captured by two additional components; classes Five and Six.…”

“…It is noted that model-updating approaches [23,26,27] have been able to detect when the pier was lowered by between 80-95mm, which occurred on the 17-18 th August. In contrast, several data-based approaches [24,[28][29][30][31][32] have detected the onset of the smallest damage introduced to the Z24 bridge, a lowering of the pier by 20 mm on the 10th August, with several even detecting when the installation equipment was brought onto the bridge (9th August). The KW51 bridge is a steel bowstring railway bridge in Leuven, Belgium.…”

“…The Z24 bridge dataset has been well-studied within the literature, with numerous analyses managing to identify the key events in the dataset [23][24][25][26][27][28][29][30][31][32]. The Z24 bridge, located in the canton Bern near Solothurn, Switzerland, was a concrete highway bridge that was used as an experimental structure for an SHM campaign before its demolition in 1998, as part of the SIMCES project [19].…”

On statistic alignment for domain adaptation in structural health monitoring

“…The Z24 bridge dataset is well-studied, with data-based approaches being able to identify key events during the monitoring campaign [2,[62][63][64][65][66][67][68][69]. The Z24 bridge was a concrete highway bridge in Bern, Switzerland, which, as part of the SMICES project, was used for an SHM campaign before its demolition in 1998 [42].…”

Damage identification for nonlinear fatigue crack of cantilever beam under harmonic excitation

“…Damage identification as an important part of SHM is sensitive to structural stiffness reduction [1]. The development of damage diagnosis and data mining theory further promotes the accuracy of structural damage identification [2][3][4]. However, structural damage usually manifests in the appearance of cracks, and gradually undergoes a process from less to more, from small to large.…”