Crack Propagation Rate Prediction Using ML

Sahil Bhatia

Abstract


The key to predicting structure fatigue lifetime is determining the fatigue crack propagation rate. Models such as the nine-parameter fatigue crack propagation rate and the McEvily model are widely used today, but realizing these models is difficult because partial derivatives must be calculated and there is a large deviation between the fitted static parameter and the actual value, and the physical conception is unclear. Because of this, we developed an optimal common machine learning algorithm (LSSVM—least squares support vector machine) for predicting fatigue crack propagation rates based on optimal parameter selection using grid search and cross-validation. It's both complicated and confusing.

References


R Xiao , J. C Wang, Z X Sun, etc. An approach to incremental SVM learning algorithm. Journal of Nanjing University (Natural Sciences) , 2002 ,38 (2) :152-157.

C. D Guo, S. Z Li. Control-based Audio Classification and Retrieval by Support Vector Machines. IEEE Trans. on Neural Network, 2003, 14 (1) , pp.209-115.

Nello Cristianini, S T. John. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge: University Press, 2000.

V N. Vapnik. Statistical Learning Theory. New York: Springer-Verlag, 2000.

Gupta, K., & Jiwani, N. (2021). A systematic Overview of Fundamentals and Methods of Business Intelligence. International Journal of Sustainable Development in Computing Science, 3(3), 31-46.

Tomar, U., Chakroborty, N., Sharma, H., & Whig, P. (2021). AI based Smart Agricuture System. Transactions on Latest Trends in Artificial Intelligence, 2(2).

WHIG, P. (2021). Innovative Smart Blind Guidance System Based on IoT. Transactions on Latest Trends in IoT, 3(3).

WHIG, P. (2019). Application of Machine learning to investigate the mortality risk of viral diseases. Transactions on Latest Trends in IoT, 1(1).

Velu, A. (2019). The spread of big data science throughout the globe. International Journal of Sustainable Development in Computing Science, 1(1), 11-20.

Velu, A. (2019). A Stable Pre-processing Method for the Handwritten Recognition System. International Journal of Machine Learning for Sustainable Development, 1(1), 21-30.

Whig, P. (2019). Exploration of Viral Diseases mortality risk using machine learning. International Journal of Machine Learning for Sustainable Development, 1(1), 11-20.

Whig, P. (2019). A Novel Multi-Center and Threshold Ternary Pattern. International Journal of Machine Learning for Sustainable Development, 1(2), 1-10.

C. W. Hsu, C. J. Lin. A comparison of methods for multi-class support vector machines. IEEE Trans on Neural Networks, 2002, 13(2):415-425.

H.F. Wu, Z.X. Li, J.L. Wu. Intelligent fault diagnosis for diesel engine exhaust valve using support machine, Transactions of CSICE, 2006, 24(5),pp.465-469.

Zheng Shuibo. Application of LSSVMs in automobile dynamical system identification. Journal of Shanghai Jiaotong University, 2005, 3(39): 392-395.


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Impact Factor : 

JCR Impact Factor: 5.9 (2020)

JCR Impact Factor: 6.1 (2021)

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JCR Impact Factor: Under Evaluation (2023)

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