An Investigation into the Capabilities of Artificial Neural Networks(ANN) in Predicting the Location and Depth of Crack on Beam
Abstract
This paper investigates the capabilities of Artificial Neural Networks (ANN) in predicting the crack location and depth on I-section beam under the clamp boundary condition. Several finite element analyzes have been carried out for the training and testing of the ANN model using MODAL analysis in ANSYS software. The I-section beam is modelled in solid structural beam. The natural frequencies were found through analyzes made using finite element analysis (FEA) software. The ANN model was developed using the algorithm Cascade- forward Back Propagation (CFBP). The validity of the method designed is verified by the determination coefficient (R). It was found that the R 2 (R: coefficient of determination) values are 0.998 for train and test data,
respectively. The result showed that the backpropagation training algorithm was capable of predicting the crack depth and location of solid I-section beam. To evaluate the capability and efficiency of the developed ANN model, the results predicted by ANN are in excellent agreement with the results of the finite element analysis (FEA).This study is useful and contributes significance knowledge to understand the prediction of crack location and depth of I-beam using the ANN model.
References
Network”, International Journal of Advanced Research Trends in Engineering and
Technology (IJARTET),Vol. 4, Special Issue 15, March 2017.
Abu-Mahfouz, I., & Banerjee, A. (2017). Crack Detection and Identification Using Vibration
Signals and Fuzzy Clustering. Procedia Computer Science, 114, 266– 274
Banerjee, A., Pohit, G., & Panigrahi, B. (2017). Vibration Analysis and Prediction Natural
Frequencies of Cracked Timoshenko Beam by Two Optimization Techniques - Cascade
ANN and ANFIS. Materials Today: Proceedings, 4(9), 9909–9913.
Chuang, K.-C., Wang, D.-F., Fang, X., Wang, Y.-H., & Huang, Z.-L. (2020). Applying
bandgap defect modes to crack detection in beams using periodic concentrated masses.
Journal of Sound and Vibration, 115308. doi:10.1016/j.jsv.2020.115308.
P. Behera.(2015).Vibration analysis of a beam using neural network technique .Department
of Mechanical Engineering. Mechanical Engineering.,Vol 1, pp. 1-50.
M.S. Mhaske & S.N. Shelke (2015). Detection of Depth and Location of Crack in a Beam by
Vibration Measurement and its Comparative Validation in ANN and GA. International
Engineering Research Journal (IERJ) Special Issue 2 Page 488-493, 2015, ISSN 2395-
1621.
Nigam, R., & Singh, S. K. (2020). Crack detection in a beam using wavelet transform and
photographic measurements. Structures, 25, 436–447. doi:10.1016/j.istruc.2020.03.010.
Ramesh, L., & Srinivasa Rao, P. (2018). Damage Detection in Structural Beams Using Model
Strain Energy Method and Wavelet Transform Approach. Materials Today:
Proceedings, 5(9), 19565–19575. doi:10.1016/j.matpr.2018.06.318.
S.Li & X.Zhao (2019).Image-Based Concrete Crack Detection Using Convolutional Neural
Network and Exhaustive Search Technique. Advances in Civil Engineering, Vol.2019,
Article ID 6520620, pp 1-12,2019.
Zhu, L.-F., Ke, L.-L., Zhu, X.-Q., Xiang, Y., & Wang, Y.-S. (2018). Crack identification of
functionally graded beams using continuous wavelet transform. Composite Structures
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