Identification of Beam Cracks by Solution of an Inverse Problem

Teidj, Sara; Khamlichi, Abdellatif; Driouach, Abdellah

doi:10.1016/j.protcy.2016.01.014

Cited by 25 publications

(12 citation statements)

References 15 publications

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“…Zhang et al [6] proposed a new region growth algorithm to detect road cracks, which is not suitable for detecting small and scattered cracks in the road. e extended finite element formula (XFEM) combined with the genetic algorithm (GA) has been proven to be effective in detecting structural defects [7][8][9], but this method also has many limitations. In this article, a novel method [10] is used to improve the convolutional neural network [11,12], so that the convolutional neural network can automatically detect the crack in the image and mark the corresponding position.…”

Section: Introductionmentioning

confidence: 99%

Improved Crack Detection and Recognition Based on Convolutional Neural Network

Chen

Yadav

Khan

et al. 2019

Modelling and Simulation in Engineering

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Concrete cracks are very serious and potentially dangerous. ere are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.

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Section: Introductionmentioning

confidence: 99%

Improved Crack Detection and Recognition Based on Convolutional Neural Network

Chen

Yadav

Khan

et al. 2019

Modelling and Simulation in Engineering

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“…Sutar et al (2015) investigated transverse crack in cantilever beam by proposing a neural network based controller. Crack stiffness to beam elemental stiffness matrix was used to obtain a homogenous linear elastic beam finite element by Teidj et al (2016) and used the measurement of the changes in the beam frequencies and observed their variations to detect the crack defect characteristics. Thatoi et al (2014) described the Cascade Forward Back Propagation (CFBP) network to detect cracks in structural beams with the idea of changes in the natural frequencies and their measurements.…”

Section: Introductionmentioning

confidence: 99%

Detection of crack in structure using dynamic analysis and artificial neural network

Maurya¹,

Sadarang²,

Panigrahi³

2020

10.5267/j.esm

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Cracks are one of the main causes of structural failure and they develop in the structures due to various reasons such as fatigue, temperature variation, excessive load, cyclic load, environmental effects, impact loading etc. Thus, structural health monitoring is necessary to avoid risks, damages and failures. So, in order to avoid an extensive failure or accident, the early prognosis of crack in structures is necessary. Visual inspection and some non-destructive testing (NDT) methods for detection of crack are difficult as it requires time, expenses and are quite inefficient. So the alternative methods are motivated to be developed. In this study, vibration analysis, finite element analysis (FEA) and an alternative way which is artificial neural network (ANN) is used to predict, detect and identify the damages in structures. It is found that the theoretical, experimental, finite element analysis and artificial neural network have good accuracy in predicting the crack characteristics.

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“…Apart from non-destructive methods, vibration-based crack identification methods have been researched widely because they are time saving, inexpensive and non labourintensive. Unlike NDT methods, vibration-based methods can identify both global and local positions of crack [2].…”

Section: Introductionmentioning

confidence: 99%

Inverse method for identification of edge crack using correlation model

Aye

Htike

2019

SN Appl. Sci.

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Crack in a structural member alters local stiffness that affects the dynamic characteristics such as natural frequency and mode shape of the structure. Identification of crack location and size is one key destination of structural health monitoring. In this paper, we propose a correlation-based inverse method for identification of crack location and size in a cantilever beam. First, natural frequencies of healthy and cracked cantilever beams were computed using finite element method and verified with experimental tests. Next, changes in natural frequencies with respect to crack locations and sizes were analyzed and the correlation model between natural frequency and crack parameters was constructed by non-linear curve-fitting. Finally, in the inverse analysis, correlation model was used to identify crack location and size based on the knowledge of the first and second natural frequencies from the measurements. Results indicate that the inverse method with new correlation model can identify crack location and size. The proposed identification method demonstrated for simple cantilever beam can be adapted for crack identification in complex structures. The proposed method is simple and easy to identify crack location and depth; its main advantage is that knowledge of only the first and second natural frequencies are required to predict crack parameters.

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Identification of Beam Cracks by Solution of an Inverse Problem

Cited by 25 publications

References 15 publications

Improved Crack Detection and Recognition Based on Convolutional Neural Network

Improved Crack Detection and Recognition Based on Convolutional Neural Network

Detection of crack in structure using dynamic analysis and artificial neural network

Inverse method for identification of edge crack using correlation model

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