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Abstract
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The structural health monitoring (SHM) approach plays a key role
not only in structural engineering but also in other various engineering
disciplines by evaluating the safety and performance monitoring of the
structures. The structural damage detection methods could be regarded as the
core of SHM strategies. That is because the early detection of the damages
and measures to be taken to repair and replace the damaged members with
healthy ones could lead to economic advantages and would prevent human
disasters. The optimization-based methods are one of the most popular
techniques for damage detection. Using these methods, an objective function
is minimized by an optimization algorithm during an iterative procedure. The
performance of optimization algorithms has a significant impact on the
accuracy of damage identification methodology. Hence, a wide variety of
algorithms are employed to address optimization-based damage detection
problems. Among different algorithms, the particle swarm optimization
(PSO) approach has been of the most popular ones. PSO was initially
proposed by Kennedy and Eberhart in 1995, and different variants were
developed to improve its performance. This work investigates the objectives,
methodologies, and results obtained by over 50 studies (2005-2020) in the
context of the structural damage detection using PSO and its variants. Then,
several important open research questions are highlighted. The paper also
provides insights on the frequently used methodologies based on PSO, the
computational time, and the accuracy of the existing methodologies.
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