A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement
Open accessAbstract
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.
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Swarm intelligent algorithms are widely used for structural optimization and compensating for EOCs.
“Swarm intelligent algorithms are widely used for structural optimization and compensating for EOCs.”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [238]
Section 4.1 describes swarm-based algorithms used for structural health monitoring and optimal sensor placement, supporting their use in structural optimization problems. Section 2.3 lists compensation for EOC changes as a sensor characteristic, but does not connect that compensation to swarm-intelligence algorithms. The claim that those algorithms are used to compensate for EOCs is therefore unsupported by this copy. (LLM, conf=0.84)
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- Authors
- Hassani, S., & Dackermann, U.
- Affiliations
- UNSW Sydney (AU)
- Corresponding author
- Dackermann, Ulrike
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-03-20
- Volume
- 23
- Issue
- 6
- Pages
- 3293
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10052056
- OpenAlex
- W4328111237
- Cited by
- 146
- References
- 323
- Citations by year
- 2026: 43 · 2025: 54 · 2024: 41 · 2023: 8
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Keywords
- structural health monitoring, optimal sensor placement, optimization algorithms, damage detection, sensor systems, artificial intelligence, heuristic algorithms, monitoring costs
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