Application of a hybrid of particle swarm and genetic algorithm for structural damage detection

Sandesh, S.; Shankar, K.

doi:10.1080/17415977.2010.500381

Cited by 36 publications

(8 citation statements)

References 29 publications

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“…In order to improve computational efficiency of identification process, GA and SA were often hybridized [134,135,136,137]. Similarly, PSO was hybridized with many other algorithms including, for example, GA [138,139], GA and ACO [140] and other swarm intelligence methods [141]. HS was hybridized with GA [142] and PSO/RO [143].…”

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

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Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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“…However, PSO does not include genetic operations such as crossover and mutation; rather, it uses its own speed to decide the search [141]. From the study of Sandesh and Shankar [142], PSO proved to be fast, whereas GA performed worst in speed and accuracy.…”

Section: Dynamic Responses-driven Hidmentioning

confidence: 99%

Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation

Zhu

Cao

Ostachowicz

et al. 2019

Sensors

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The detection of damage in bridges subjected to moving loads has attracted increasing attention in the field of structural health monitoring. Processing the dynamic responses induced by moving loads to characterize damage is the key to identifying damage in bridges. On this topic, various methods of processing dynamic responses to moving loads have been developed in recent decades, with respective strengths and weaknesses. These methods appear in different applications and literatures and their features have not been comprehensively surveyed to form a profile of this special area. To address this issue, this study presents a comprehensive survey of methods for identifying damage by processing dynamic responses of cracked bridges subjected to moving loads. First, methods utilizing the Fourier transform to process dynamic responses to moving loads for damage detection in bridges are examined. Second, methods using wavelet transform to process the dynamic responses to moving loads for damage characterization are examined. Third, methods of employing the Hilbert-Huang transform to process the dynamic responses to moving loads for damage identification are examined. Fourth, methods of dynamic response-driven heuristic interrogation of damage in bridges subjected to moving loads are examined. Finally, we recommend future research directions for advancing the development of damage identification relying on processing dynamic responses to moving loads. This study provides a profile of the state-of-the-art and state-of-the-use of damage identification in bridges based on dynamic responses to moving loads, with the primary aim of helping researchers find crucial points for further exploration of theories, methods, and technologies for damage detection in bridges subjected to moving loads.

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“…This philosophy of survival of the fittest facilitates to solve numerical optimization problems, where natural evaluation and adaptation to environmental variation are simulated mathematically using GA. This algorithm works based on an iterative procedure consisting of a constant-sized population of individuals, usually encoded as binary strings (chromosomes), representing candidate solutions in a given search space comprising of all the possible solutions to the optimization problem (Beygzadeh et al, 2014;Deb, 2001;Dey et al, 2015b;Sandesh and Shankar, 2010). The initial population of individuals is generated randomly.…”

Section: Identification Of Damagementioning

confidence: 99%

Structural damage identification: A random sampling-high dimensional model representation approach

Mukhopadhyay

Chowdhury

Chakrabarti

2016

Advances in Structural Engineering

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Structural damage identification and quantification of damage using non-destructive methods are important aspects for any civil, mechanical and aerospace engineering structures. In this study, a novel damage identification algorithm has been developed using random sampling-high dimensional model representation approach. A global sensitivity analysis based on random sampling-high dimensional model representation is adopted for important parameter screening purpose. Three different structures (spring mass damper system, simply supported beam and fibre-reinforced polymer composite bridge deck) have been used for various single and multiple damage conditions to validate the proposed algorithm. The performance of this method is found to be quite satisfactory in the realm of damage detection in structures. The random sampling-high dimensional model representation-based approach for meta-model formation is particularly useful in damage identification as it works well when large numbers of input parameters are involved. In this study, two different optimization methods have been used and their relative capability to identify damage has been discussed. Performance of this damage identification algorithm under the influence of noise has also been addressed in this article.

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Application of a hybrid of particle swarm and genetic algorithm for structural damage detection

Cited by 36 publications

References 29 publications

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation

Structural damage identification: A random sampling-high dimensional model representation approach

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