Efficiency of Plasma Generation Optimization for Structural Damage Identification of Skeletal Structures Based on a Hybrid Cost Function

Structural Design Tall Build

2022

Summary Optimization‐based methods are increasingly being implemented for structural damage detection problems through the minimization of the objective functions based on vibration data. The adopted optimization algorithm and objective function play an important role in the accurate detection and quantification of damages. Meanwhile, the challenge of long computational time is another aspect of structural damage identification problems, especially upon addressing large‐scale structures. In this paper, recently developed optimization techniques called slime mold algorithm (SMA) and marine predators algorithm (MPA) are applied to damage assessment of large‐scale structures for the first time. The performance of these algorithms is compared with those obtained using other well‐known optimization techniques such as ant lion optimizer (ALO), whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Furthermore, the sensitivity of three objective functions based on modal assurance criterion (MAC), modified total modal assurance criterion (MTMAC), and natural frequency vector assurance criterion (NFVAC) are examined. Two numerical studies, including the 53‐bar planar truss and the Guangzhou New TV tower, and a full‐scale three‐story frame as an experimental investigation are conducted to present a statistical comparison. The overall results show that the combination of SMA and objective function based on MTMAC provides an accurate tool for damage identification. However, improved SMA (ISMA) has been introduced to enhance the capability of standard SMA for damage detection, and especially finite element model updating in the experimental example. Five benchmark functions are also used to evaluate the global optimization capacity of ISMA and SMA. The results show that the ISMA has many benefits in terms of tackling global optimization problems. MPA‐MTMAC can provide promising results compared with ALO‐MTMAC, WOA‐MTMAC, and GOA‐MTMAC. However, excessive computation time is a big drawback for MPA. The overall results confirm the perfection of the objective function based on MTMAC compared with two others based on MAC and NFVAC.

Section: Methodsmentioning

confidence: 99%

Efficiency of the slime mold algorithm for damage detection of large‐scale structures

Structural Design Tall Build

2022

“…Since 2014 to date, new generations of optimization algorithms such as GWO [132], MFO [133], SSA [134], MVO [135], ALO [136], water strider algorithm (WSA) [137], plasma generation optimization (PGO) [138], vibrating particles system (VPS) [139], thermal exchange optimization (TEO) [140] have been introduced. For structural damage identification, successful applications of GWO [38,141], MFO [121,142], SSA [40], MVO [39], ALO [122], WSA [143,144], PGO [145], VPS [146], and TEO [147] have been reported during the past years. These algorithms have some major advantages as follows:…”

Section: Laminate Composite Platementioning

confidence: 99%

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

Frattura Ed Integrità Strutturale

Mirjalili

2022

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.

“…he vital role of structures and infrastructures is undeniable in organizing voluminous human activities in large societies [1]. These structures are exposed to damage due to extreme conditions such as strong earthquakes, winds, or human-induced events [2,3]. Many structures have been constructed and are still in service despite expiration [4].…”

Section: Introductionmentioning

confidence: 99%

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

Frattura Ed Integrità Strutturale

Mirjalili

2023

In recent years, many innovative optimization algorithms have been developed. These algorithms have been employed to solve structural damage detection problems as an inverse solution. However, traditional optimization methods such as particle swarm optimization, simulated annealing (SA), and genetic algorithm are constantly employed to detect damages in the structures. This paper reviews the application of SA in different disciplines of structural health monitoring, such as damage detection, finite element model updating, optimal sensor placement, and system identification. The methodologies, objectives, and results of publications conducted between 1995 and 2021 are analyzed. This paper also provides an in-depth discussion of different open questions and research directions in this area.