Crack detection in beam-like structures using genetic algorithms

Vakil-Baghmisheh, Mohammad-Taghi; Peimani, Mansour; Sadeghi, Mostafa; Ettefagh, Mir Mohammad

doi:10.1016/j.asoc.2007.10.003

Cited by 156 publications

(60 citation statements)

References 15 publications

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“…An active branch of vibration-based damage identification is damage pattern recognition based on artificial intelligence methods [11][12][13][14][15]. The most popular artificial intelligence method used…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

et al. 2017

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A sophisticated hierarchical neural network model for intelligent assessment of structural damage is constructed by the synergetic action of auto-associative neural networks (AANNs) and Levenberg-Marquardt neural networks (LMNNs). With the model, AANNs aided by the wavelet packet transform are firstly employed to extract damage features from measured dynamic responses and LMNNs are then utilized to undertake damage pattern recognition. The synergetic functions endow the model with a unique mechanism of intelligent damage identification in structures. The model is applied for the identification of damage in a three-span continuous bridge, with particular emphasis on noise interference. The results show that the AANNs can produce a low-dimensional space of damage features, from which LMNNs can recognize both the location and the severity of structural damage with great accuracy and strong robustness against noise. The proposed model holds promise for developing viable intelligent damage identification technology for actual engineering structures.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

et al. 2017

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“…It is a search technique used to find an approximate solution to optimization and search problems. Baghmisheh [9] proposed a fault diagnosis method based on GA for a cracked structure. An analytical model of a damaged cantilever beam was considered and natural frequencies were calculated.…”

Section: Genetic Algorithm For Crack Detectionmentioning

confidence: 99%

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Thatoi¹

2013

IJET

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Abstract-Detection of cracks using vibration analysis employs different algorithms out of which artificial intelligence (AI) techniques is drawing a lot of attention of the engineers and scientists. This paper presents a comprehensive review of various AI techniques for determining the location, size and depth of cracks in different structures. The techniques mainly consist of fuzzy interference technique, neural network (NN), genetic algorithm (GA), hybrid models and other optimization algorithms.

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“…Baghmisheh et al [64] used genetic algorithm (GA) to monitor the changes in natural frequencies of a cantilever beam having crack. They used an analytical model to formulate the crack beam structure and numerical methods to obtain the natural frequencies.…”

Section: Genetic Algorithm For Fault Detectionmentioning

confidence: 99%

Review of Techniques for Fault Diagnosis in Damaged Structure and Engineering System

Thatoi

Das

Parhi

2012

Advances in Mechanical Engineering

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Focus has been made to give an overview of various methodologies used in fault diagnosis and condition monitoring. A crack in vibrating structures can lead to premature failure if it is not detected in time. Researchers have been working on the dynamics of cracked structures for decades to be able to monitor a structure and diagnose fault at the earliest possible stage. An effort has been made in the current paper to understand different techniques and methodologies for fault diagnosis and condition monitoring of damaged structures subjected to varied dynamic loading. The methods used are classical, wavelet transform, and finite element methods, artificial intelligence methods, and numerical and experimental methods. Using classical methods, engineers are able to predict faults. But using artificial intelligence techniques, it is observed that the forecasting time for fault diagnosis improves a lot in comparison to other methodologies.

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Crack detection in beam-like structures using genetic algorithms

Cited by 156 publications

References 15 publications

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Review of Techniques for Fault Diagnosis in Damaged Structure and Engineering System

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