Computational methodologies for optimal sensor placement in structural health monitoring: A review

Abstract

Structural health monitoring plays an increasingly significant role in detecting damages for large and complex structures to ensure their serviceability and sustainability. Optimal sensor placement is critical in the structural health monitoring system as the sensor configuration directly impacts the quality of collected data used for structural health diagnosis. Therefore, this study presents a comprehensive review of computational methodologies for optimal sensor placement in structural health monitoring. The problem formulation of optimal sensor placement is first introduced, including commonly used evaluation criteria for sensor configurations. Then, various existing optimization methodologies for sensor placement are summarized and introduced in detail, especially for the evolutionary algorithms and their improved variants. Finally, the suitability of computational methods for specific structural health monitoring applications is also discussed. The main goal of this study is to deliver a comprehensive reference of computational methodologies for optimal sensor placement in structural health monitoring studies and applications. This article is concluded by highlighting the most widely utilized evaluation criteria and optimization methodologies for sensor configuration determination.

Optimal sensor placements using modified Fisher information matrix and effective information algorithm

“…Papadopoulos and Garcia 20 established a structural sensor placement method using the Gram–Schmidt orthogonalization procedure and principal component analysis. Tan and Zhang 21 reviewed computational methodologies for OSP and formulated evaluation criteria for sensor configurations and optimization methodologies.…”

On the use of acquisition function‐based Bayesian optimization method to efficiently tune SVM hyperparameters for structural damage detection

“…Several metaheuristic optimization approaches (genetic algorithm and particle swarm optimization [PSO]) are recently proposed in the SHM literature to resolve these issues to some extent 19,20 . PSO is one of the most commonly used metaheuristic techniques in SHM literature 20–23 As PSO can be used for black box optimization as well, recently, Gui et al 20 utilized the PSO technique for optimal selection of hyperparameters in SVM for achieving better accuracy of damage detection. Another recent use of PSO in SHM is by Jia et al 21 ; in this work PSO has been utilized to tune the hyperparameters of a support vector regression model for damage localization in pipelines.…”

Impact Damage Detection in Laminate and Honeycomb CFRPs using Lamb Wave Ultrasonic Sensing

“…Due to low attenuation these waves can ACOUSTIC METHODS cover extensive areas, that is extremely important in aircraft industry, where the area of CFRP skins can be very large. Lamb wave ultrasonic methods are widely studied for the detection of impact damages in composites [26,27]; the optimal networks of transducers are investigated [28][29][30]. However, the physical mechanism of Lamb waves propagation is quite complex [31,32] requiring consideration of many factors such as the stability of the adhesive layer and the degradation of its properties [33,34], temperature deviations [35,36], sensor failure [37], etc.…”