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Back analysis of rock mechanical parameters of underground caverns based on multi-layer perceptron regression algorithm
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.
Back analysis of rock mechanical parameters of underground caverns based on multi-layer perceptron regression algorithm
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.
Back analysis of rock mechanical parameters of underground caverns based on multi-layer perceptron regression algorithm
Jabbar, M. A. (editor) / Lorenz, Pascal (editor) / Cheng, Yongjin (author) / He, Zhanguo (author) / Zeng, Xinghua (author) / Hu, Shuhong (author) / Chu, Weijiang (author)
Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024) ; 2024 ; Kuala Lumpur, Malaysia
Proc. SPIE ; 13184
2024-07-05
Conference paper
Electronic Resource
English
Stress and Seepage Analysis of Underground Rock Caverns
| British Library Conference Proceedings | 2009
| Trans Tech Publications | 2013
| British Library Online Contents | 2015