“…Changes in these characteristics can indicate the presence of damage. Vibration-based damage identification relies on analyzing changes in the dynamic response of a structure to detect and locate damage (Das et al, 2016;Gordan et al, 2017;Ngoc-Nguyen et al, 2022;Rajenderan and Jayaguru, 2022).…”
This paper presents an enhanced version of the Artificial Rabbit Optimization (ARO) algorithm designed for identifying structural damage in bridge structures. The original ARO draws inspiration from survival observed in wild rabbits. However, it demands a substantial investment of computational time. Therefore, in this paper, the Improved ARO (IARO) algorithm incorporating elements of the Grey Wolf Optimizer (GWO) through hybridization, is employed to deal with optimization problems. The central concept of this approach involves infusing predator-hunting characteristics into the prey-rabbit during the hunting process, thereby enabling more effective predator evasion. The proposed method is evaluated through a series of simulations related to two real bridges: a simple supported beam structure and a steel truss bridge. The results show a significant improvement in accuracy and efficiency in determining structural damage while considering factors such as damage location, severity, and computation time. These findings underscore the potential of the proposed approach for real-world applications in structural health monitoring and damage detection.
“…Changes in these characteristics can indicate the presence of damage. Vibration-based damage identification relies on analyzing changes in the dynamic response of a structure to detect and locate damage (Das et al, 2016;Gordan et al, 2017;Ngoc-Nguyen et al, 2022;Rajenderan and Jayaguru, 2022).…”
This paper presents an enhanced version of the Artificial Rabbit Optimization (ARO) algorithm designed for identifying structural damage in bridge structures. The original ARO draws inspiration from survival observed in wild rabbits. However, it demands a substantial investment of computational time. Therefore, in this paper, the Improved ARO (IARO) algorithm incorporating elements of the Grey Wolf Optimizer (GWO) through hybridization, is employed to deal with optimization problems. The central concept of this approach involves infusing predator-hunting characteristics into the prey-rabbit during the hunting process, thereby enabling more effective predator evasion. The proposed method is evaluated through a series of simulations related to two real bridges: a simple supported beam structure and a steel truss bridge. The results show a significant improvement in accuracy and efficiency in determining structural damage while considering factors such as damage location, severity, and computation time. These findings underscore the potential of the proposed approach for real-world applications in structural health monitoring and damage detection.
“…For instance, composite patches [ 31 ] have assessed vibration excitation and separation in laminates. At the same time, concrete beams have been used for damage evaluation in reinforced concrete structures with lap splices of tensional steel bars [ 32 ]. Additionally, the effectiveness of PZT actuators has been examined when combined with viscoelastic bonding layers on elastic structures [ 33 ].…”
“…Case studies of damage detection of the model bridge and real bridge structures employing Digital twin (DT) technology or DL algorithms, with high accuracy of 92 percent, are used to show the viability of the suggested framework [25]. In the latest studies, piezoelectric materials adhesively bonded to structures were also found in some other cases such as in composite patches [26] which is used to evaluate the vibration excitation and separation in laminates and concrete beams which is used for damage evaluation of reinforced concrete structures at lap splices of tensional steel bars [27]. Additionally, the performance of PZT actuators was found in the effects of a viscoelastic bonding layer bonded to an elastic structure [28].…”
In recent studies, piezoelectric actuators have been recognized as a practical and effective material for repairing cracks in thin-walled structures, such as plates that are adhesively bonded with piezoelectric patches due to their electromechanical effects. In this study, we used the finite element method through the ANSYS commercial code to determine the stress intensity factor (SIF) at the crack tip of a cracked plate bonded with a piezoelectric actuator under a plane stress model. By running various simulations, we were able to examine the impact of different aspects that affect this component, such as the size and characteristics of the plate, actuator, and adhesive bond. To optimize performance, we utilized machine learning algorithms to examine how these characteristics affect the repair process. This study represents the first-time machine learning has been used to examine bonded PZT actuators in damaged structures, and we found that it had a significant impact on the current problem. As a result, we were able to determine which of these parameters were most helpful in achieving our goal and which ones should be adjusted to improve the actuator's quality and reduce significant time and costs.
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