Centre for Infrastructure Engineering and Safety, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Sensors (Basel). 2023 Mar 20;23(6):3293. doi: 10.3390/s23063293.
In recent decades, structural health monitoring (SHM) has gained increased importance for ensuring the sustainability and serviceability of large and complex structures. To design an SHM system that delivers optimal monitoring outcomes, engineers must make decisions on numerous system specifications, including the sensor types, numbers, and placements, as well as data transfer, storage, and data analysis techniques. Optimization algorithms are employed to optimize the system settings, such as the sensor configuration, that significantly impact the quality and information density of the captured data and, hence, the system performance. Optimal sensor placement (OSP) is defined as the placement of sensors that results in the least amount of monitoring cost while meeting predefined performance requirements. An optimization algorithm generally finds the "best available" values of an objective function, given a specific input (or domain). Various optimization algorithms, from random search to heuristic algorithms, have been developed by researchers for different SHM purposes, including OSP. This paper comprehensively reviews the most recent optimization algorithms for SHM and OSP. The article focuses on the following: (I) the definition of SHM and all its components, including sensor systems and damage detection methods, (II) the problem formulation of OSP and all current methods, (III) the introduction of optimization algorithms and their types, and (IV) how various existing optimization methodologies can be applied to SHM systems and OSP methods. Our comprehensive comparative review revealed that applying optimization algorithms in SHM systems, including their use for OSP, to derive an optimal solution, has become increasingly common and has resulted in the development of sophisticated methods tailored to SHM. This article also demonstrates that these sophisticated methods, using artificial intelligence (AI), are highly accurate and fast at solving complex problems.
近几十年来，结构健康监测 (SHM) 对于确保大型复杂结构的可持续性和适用性变得越来越重要。为了设计能够提供最佳监测结果的 SHM 系统，工程师必须对众多系统规格做出决策，包括传感器类型、数量和位置，以及数据传输、存储和数据分析技术。优化算法用于优化系统设置，例如传感器配置，这会显著影响所捕获数据的质量和信息密度，从而影响系统性能。最优传感器布置 (OSP) 被定义为在满足预定义性能要求的情况下，传感器布置的监测成本最低。优化算法通常会在特定输入（或域）下找到目标函数的“最佳可用”值。为了不同的 SHM 目的，包括 OSP，研究人员已经开发了各种优化算法，从随机搜索到启发式算法。本文全面回顾了用于 SHM 和 OSP 的最新优化算法。本文重点介绍以下内容：(I) SHM 的定义及其所有组成部分，包括传感器系统和损伤检测方法，(II) OSP 的问题公式化及其所有当前方法，(III) 优化算法及其类型的介绍，以及 (IV) 如何将各种现有的优化方法应用于 SHM 系统和 OSP 方法。我们全面的比较性回顾表明，在 SHM 系统中应用优化算法，包括将其用于 OSP 以得出最佳解决方案，已经变得越来越普遍，并导致了针对 SHM 定制的复杂方法的发展。本文还表明，这些使用人工智能 (AI) 的复杂方法在解决复杂问题时非常准确和快速。
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