Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization

Khatir, Samir; Dekemele, Kevin; Loccufier, Mia; Khatir, Tawfiq; Wahab, Magd Abdel

doi:10.1016/j.crme.2017.11.008

Cited by 115 publications

(38 citation statements)

References 27 publications

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“…The regression equations for the selected responses, as given by Eqs. (2)(3), are further used as fitness function for process optimization using GA.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The crack length and crack propagation data have been linked with statistical moments, frequency spectra, and wavelet coefficient data. Khatir et al [2] used experimentally determined natural frequencies along with the particle swarm optimization (PSO) technique to detect an open crack on a cantilever beam made up of steel. Genetic algorithm (GA) is an optimization method which is bio-inspired, and it supports the ideas of natural genetics and selection theories as proposed by Charles Darwin.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parametric Optimization of Cracked Cantilever Beam Using Genetic Algorithm

Sutar

Pattnaik

Modi

2020

Lecture Notes in Mechanical Engineering

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This paper presents a smart crack detection system using genetic algorithm (GA) for a cantilever beam. The accuracy of prediction is optimized by performing parametric optimization of the first three natural frequencies. The three modes of natural frequencies are obtained from the experimental procedures and are optimized to reduce the percentage of error in predicting the relative crack position and crack depth from a fixed position. Comparison of analytical method is in agreement with the experimental values as the error percent between the experimental and predicted values is below 10%.

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“…The regression equations for the selected responses, as given by Eqs. (2)(3), are further used as fitness function for process optimization using GA.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parametric Optimization of Cracked Cantilever Beam Using Genetic Algorithm

Sutar

Pattnaik

Modi

2020

Lecture Notes in Mechanical Engineering

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show abstract

“…[ 15 ] Khatir et al performed an experimental and numerical study to FEM updating, identify crack locations and their depth using PSO and natural frequency differences. [ 16 ] Chen and Yu have applied the hybrid PSO and Monte Carlo simulations to damage detection and severity identification of damaged elements in beam‐like structures. The above‐described results have been validated numerically and experimentally.…”

Section: Introductionmentioning

confidence: 99%

Multiverse optimizer for structural damage detection: Numerical study and experimental validation

Ghannadi

Kourehli

2020

Structural Design Tall Build

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New optimization algorithms have been increasingly developed in the course of the recent years. The accuracy of identified results in optimization-based damage detection methods depends on the objective function and optimization algorithm. This paper employs multiverse Optimizer (MVO) to solve the optimization-based damage identification problem. Statistical results obtained by MVO are compared with those of sine cosine algorithm (SCA) and Harris hawks optimization (HHO) in order to perform a comparative study. Two objective functions are used in this optimization problem. The first one is based on the modal assurance criterion (MAC) and the second one on modified total modal assurance criterion (MTMAC). Numerical and experimental examples indicate that the combination of objective function based on MTMAC and MVO algorithm can provide accurate and reliable results in structural damage detection process. MAC is also rejected in competition with MTMAC. While using SCA-MTMAC, the accuracy of the results is acceptable in some cases. However, HHO-MTMAC gives weak results in most cases. MVO and SSA are the lowest, and HHO spends significantly more time in terms of computational cost.

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“…They solve an inverse problem by building a physics-based model and then updating its parameters until the response of the model matches that which is measured in the real structure. Although this approach is currently under exhaustive research [19][20][21][22][23][24][25], it still presents some drawbacks in the assessment of real systems. Such drawbacks include the need for high-quality data and the impossibility to provide real-time insight due to the computational effort required to solve the updating problem [26].…”

Section: Introductionmentioning

confidence: 99%

Bearing assessment tool for longitudinal bridge performance

Fernández-Navamuel

Zamora-Sánchez

Alvear

et al. 2020

J Civil Struct Health Monit

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This work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of principal component analysis. We validate the method and show its efficiency against false positives and negatives using several metrics: accuracy, precision, recall, and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management.

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Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization

Cited by 115 publications

References 27 publications

Parametric Optimization of Cracked Cantilever Beam Using Genetic Algorithm

Parametric Optimization of Cracked Cantilever Beam Using Genetic Algorithm

Multiverse optimizer for structural damage detection: Numerical study and experimental validation

Bearing assessment tool for longitudinal bridge performance

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