The presence of cracks in a beam changes the dynamic characteristics of the beam. Therefore, to assess the condition of the beam, its natural frequencies must be examined. In this study, using a numerical solution based on the Rayleigh method, the natural frequencies of a beam with two cracks are calculated based on the depth and location of the cracks. Next, using the Python programming language, the aforementioned mathematical relationship is entered into this program to solve this relationship sequentially for different inputs by creating iterative loops. The goal of this is to produce a dataset that can be used to train machine learning algorithms such as random forest regression, gradient boosting regression, multilayer perceptron, and decision tree regression to predict the natural frequency. The key innovation in this study is the use of a network search method to determine the optimal amount of data for each algorithm, which increases accuracy and introduces a new criterion for comparison called "required data volume". The study found that increasing the size of the dataset generally increases the prediction accuracy of the algorithms. In addition, algorithms that predict a single output have higher accuracy compared to those that predict multiple outputs. The study demonstrates the effective use of machine learning algorithms for predicting natural frequencies. The gradient boosting regression algorithm with an accuracy of 84.10% and the random forest regression algorithm with an accuracy of 83.73% emerged as the superior methods for predicting beam frequencies.
میر, م. and Razeghian, R. (2025). Frequency analysis of cracked beams using machine learning. Journal of Modeling in Engineering, (), -. doi: 10.22075/jme.2025.36673.2800
MLA
میر, م. , and Razeghian, R. . "Frequency analysis of cracked beams using machine learning", Journal of Modeling in Engineering, , , 2025, -. doi: 10.22075/jme.2025.36673.2800
HARVARD
میر, م., Razeghian, R. (2025). 'Frequency analysis of cracked beams using machine learning', Journal of Modeling in Engineering, (), pp. -. doi: 10.22075/jme.2025.36673.2800
CHICAGO
م. میر and R. Razeghian, "Frequency analysis of cracked beams using machine learning," Journal of Modeling in Engineering, (2025): -, doi: 10.22075/jme.2025.36673.2800
VANCOUVER
میر, م., Razeghian, R. Frequency analysis of cracked beams using machine learning. Journal of Modeling in Engineering, 2025; (): -. doi: 10.22075/jme.2025.36673.2800
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