Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method

• Published in: Applied sciences Vol. 14; no. 20; p. 9566
• Main Authors: Zhu, Chuangwei, Guo, Baohua, Zhang, Zhezhe, Zhong, Pengbo, Lu, He, Sigama, Anthony
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Algorithms; BP neural network; Concrete; Datasets; Friction; genetic algorithm; JRC-JCS model; Machine learning; Mechanical properties; Methods; Morphology; Neural networks; peak shear strength; rock joint; Shear strength; Shear tests; Tensile strength; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14209566

The peak shear strength of a rock joint is an important indicator in rock engineering, such as mining and sloping. Therefore, direct shear tests were conducted using an RDS-200 rock direct shear apparatus, and the related data such as normal stress, roughness, size, normal loading rate, basic friction angle, and JCS were collected. A peak shear strength prediction model for rock joints was established, by which a predicted rock joint peak shear strength can be obtained by inputting the influencing factors. Firstly, the study used the correlation analysis method to find out the correlation coefficient between the above factors and rock joint peak shear strength to provide a reference for factor selection of the peak shear strength prediction model. Then, the JRC-JCS model and four established GA-BP neural network models were studied to identify the most valuable rock joint peak shear strength prediction method. The GA-BP neural network models used a genetic algorithm to optimize the BP neural network with different input factors to predict rock joint peak shear strength, after dividing the selected data into 80% training set and 20% test set. The results show that the error of the JRC-JCS model is a little bigger, with a value of 11.2%, while the errors of the established GA-BP neural network models are smaller than 6%, which indicates that the four established GA-BP neural network models can well fit the relationship between the peak shear strength and selected input factors. Additionally, increasing the factor number of the input layer can effectively improve the prediction accuracy of the GA-BP neural network models, and the prediction accuracy of the GA-BP neural network models will be higher if factors that have higher correlation with the output results are used as input factors.