Back analysis of rock mechanical parameters of underground caverns based on multi-layer perceptron regression algorithm

Yongjin Cheng; Zhanguo He; Xinghua Zeng; Shuhong Hu; Weijiang Chu

doi:10.1117/12.3033109

5 July 2024 Back analysis of rock mechanical parameters of underground caverns based on multi-layer perceptron regression algorithm

Yongjin Cheng, Zhanguo He, Xinghua Zeng, Shuhong Hu, Weijiang Chu

Author Affiliations +

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318439 (2024) https://doi.org/10.1117/12.3033109
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The paper focuses on applying deep learning methods to analyze rock mechanical parameters in the underground caverns of the Kala Hydropower Station, aiming to develop an efficient methodology using the scikit-learn MLP regression algorithm to estimate rock physical parameters based on observed displacements or deformations, with the evaluation of its performance compared to traditional methods contributing to advancing parameter estimation techniques in geotechnical engineering. This novel method underscores the significance of understanding rock mechanical parameters for designing and constructing underground caverns, focusing on rock strength, deformation characteristics, and stress distribution, exploring the use of deep learning techniques, particularly the MLP regressor, to estimate these rock mechanical parameters, aiming to develop a methodology for accurate parameter estimation and back analysis, emphasizing the potential of deep learning in advancing geotechnical engineering practices and solving complex problems in rock mechanics. The comparison between the calculated displacement increments and the measured values from the 3DEC warning prediction model indicates some level of discrepancy between the predicted and actual values. Despite this, the predicted warning values demonstrate satisfactory control standards and carry important guiding implications. The derived rock mass parameters can serve as the fundamental basis for future warning predictions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongjin Cheng, Zhanguo He, Xinghua Zeng, Shuhong Hu, and Weijiang Chu "Back analysis of rock mechanical parameters of underground caverns based on multi-layer perceptron regression algorithm", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318439 (5 July 2024); https://doi.org/10.1117/12.3033109

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KEYWORDS

Deep learning

Data modeling

Analytical research

Deformation

Education and training

Engineering

Statistical modeling

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