Inverse method to identify crack parameters in multi-span beam using genetic algorithm

Mungla, Mitesh J.; Sharma, Dharmendra S.; Trivedi, Reena

doi:10.1080/10589759.2016.1226302

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…Conventional optimisation techniques are gradient-based and they fall into a local minimum. Frequency-based crack detection method coupled with a genetic algorithm (GA) is used to detect the crack location and its severity in a cracked multi-span beam (Mungla et al, 2017). GA and frequency-based methods were used to detect a crack in the clamped-clamped beam.…”

Section: Introductionmentioning

confidence: 99%

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Reddy,

Senthilkumar,

Sreekanth

2023

IJPSQ

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The prediction of crack location and crack depth in a cantilever beam is considered as an optimisation problem. In this paper, the location and depth of the crack are predicted by minimising the cost function. The cost or objective function is formulated based on the difference between the first three experimental and calculated natural frequencies. The natural frequencies are calculated from the equations which are developed based on the numerical analysis. Rao-1 algorithm is used for predicting the crack location and depth. The Rao-1 algorithm does not have any algorithm-specific parameters to tune them to get optimised performance. Results obtained from the Rao-1 algorithm are compared with those obtained by particle swarm optimisation (PSO). PSO algorithm detected the crack with an average error of 4.94% and the crack depth with 5.96% whereas the Rao-1 algorithm has predicted the location and depth of the crack with an average error of 4.85% and 6.08%. Rao-1 algorithm is also robust and can be used in damage detection to avoid tuning algorithmspecific parameters for better prediction.

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Section: Introductionmentioning

confidence: 99%

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Reddy,

Senthilkumar,

Sreekanth

2023

IJPSQ

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“…Nevertheless, first attempts have been recently accomplished in [11][12][13][14] by using the conventional method to compute natural frequencies and mode shapes of cracked multispan beams. Namely, Liu et al [11] and Lien and Hao [12] have demonstrated that change in mode shapes of multispan beam due to cracks could be numerically examined and Sharma et al [13,14] have shown that natural frequencies are promising to use for crack detection in the multispan beams. Recently, Tan et al [15] studied both the forward and inverse problems for multispan continuous beam with arbitrary number of cracks and spring-mass systems.…”

Section: Introductionmentioning

confidence: 99%

Effect of intermediate supports location on natural frequencies of multiple cracked continuous beams

et al. 2018

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The present paper deals with free vibration of multiple cracked continuous beams with intermediate rigid supports. A simplified method is proposed to obtain general solution of free vibration in cracked beam with intermediate supports that is then used for natural frequency analysis of the beam in dependence upon cracks and support locations. Numerical results show that the support location or ratio of span lengths in combination with cracks makes a significant effect on eigenfrequency spectrum of beam. The discovered effects of support locations on eigenfrequency spectrum of cracked continuous beam are useful for detecting not only cracks but also positions of vanishing deflection on the beam.

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“…In structural engineering, experiments are often designed with the aim to characterise a problem e.g. material characterisation (Beilina (2015) or deterioration estimation (Yap (2013) and Mungla, Sharma, and Trivedi (2016)).…”

Section: Introductionmentioning

confidence: 99%

Heuristic linear algebraic rank-variance formulation and solution approach for efficient sensor placement

Chae

Wilke

2017

Engineering Structures

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The digital age has significantly impacted our ability to sense our environment and infer the state or status of equipment in our environment from the sensed information. Consequently inferring from a set of observations the causal factors that produced them is known as an inverse problem. In this study the sensed information, a.k.a. sensor measurement variables, is measurable while the inferred information, a.k.a. target variables, is not measurable. The ability to solve an inverse problem depends on the quality of the optimisation approach and the relevance of information used to solve the inverse problem. In this study, we aim to improve the information available to solve an inverse problem by considering the optimal selection of m sensors from k options. This study introduces a heuristic approach to solve the sensor placement optimisation problem which is not to be confused with the required optimisation strategy to solve the inverse problem. The proposed heuristic optimisation approach relies on the rank of the cross-covariance matrix between the observations of the target variables and the observations of the sensor measurement variables obtained from simulations using the computational model of an experiment. In addition, the variance between observations of the sensor measurements is considered. A new formulation, namely the tolerance rank-variance formulation (TRVF) is introduced and investigated numerically on a full field deterioration problem. The full field deterioration is estimated for a plate by resolving a parametrisation of the deterioration field for four scenarios. We demonstrate that the optimal sen

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Inverse method to identify crack parameters in multi-span beam using genetic algorithm

Cited by 8 publications

References 23 publications

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Vibration-based crack detection in cantilever beams using Rao-1 algorithm

Effect of intermediate supports location on natural frequencies of multiple cracked continuous beams

Heuristic linear algebraic rank-variance formulation and solution approach for efficient sensor placement

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