Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.

This paper proposes a novel optimization algorithm called modal force information-based optimization (MFIBO) to identify the location and severity of damage in structures. The main idea behind the MFIBO is to take advantage of information captured from the modal force of structural elements to seek the optimum damage variables. The modal element force, defined as the internal element force caused by the action of mode shapes, allows the MFIBO to recognize promising directions in the search space and assists in accelerating the optimization process. Indeed, unlike meta-heuristic optimization algorithms, which disregard explicit information about the problem and rely only upon time-consuming stochastic search computations, the MFIBO employs an informed search strategy to perform optimization in a rational and directed manner. In order to assess the effectiveness and applicability of the proposed MFIBO algorithm, four benchmark damage identification examples of truss and frame structures are conducted under both noise-free and noisy conditions. In each example, the results of the MFIBO are also compared with those attained by two well-known meta-heuristic algorithms, namely the differential evolution and the teaching–learning-based optimization. The obtained results reveal that the MFIBO is able to accurately and reliably identify structural damage with a significantly lower computational burden compared to the meta-heuristic algorithms.

Beam-column joints are responsible for maintaining the integrity and stability of frame structures, and any damage to these critical components can endanger the overall safety and reliability of the structure. Hence, early detection of structural joint damage is of paramount importance. However, most of the available structural damage identification methods focus on identifying damage in structural members, and relatively fewer methods have been developed so far for assessing damage in structural joints. In view of this, the present study proposes a new two-stage method for joint damage identification of frame structures. In the first stage, an efficient damage indicator, called residual moment-based joint damage index (RMBJDI), is developed and applied to detect the location of potentially damaged joints. This damage indicator can help to reduce the number of involved damage variables by excluding healthy joints from the problem. In the second stage, the reduced dimension damage identification problem is formulated as an optimization problem and is further tackled by employing a robust meta-heuristic algorithm, namely equilibrium optimizer (EO), to determine the damage severity of suspected damaged joints. In order to assess the capability and effectiveness of the presented joint damage identification method, two numerical examples of frame structures are conducted under both noise-free and noisy conditions. The results demonstrate that the proposed two-stage method, which integrates RMBJDI with EO, is a highly accurate and powerful tool for localizing and quantifying the joint damage in frame structures.

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