Abstract Aiming at the limitations of traditional methods of classifying surrounding rocks, a Gaussian process-based model for classification of surrounding rocks is proposed. The nonlinear mapping relationship between the classification of surrounding rocks and influencing factors is easily established using the Gaussian process model for machine learning, which possesses excellent classification performance based on making use of the historical knowledge of real engineering projects. The model has been applied to the classification of the surrounding rocks of the Erlangshan Tunnel on the Sichuan-Tibet Highway. The results of this case study show that the model is feasible and that reasonable, reliable and probabilistic results for classification of surrounding rocks can be obtained quickly by using the proposed model. Compared with other machine learning technologies, such as artificial neural networks and support vector machines, the proposed model has the beneficial characteristics of self-adaptive parameter determination and an uncertainty evaluation of predicted results.
Abstract：
Aiming at the limitations of traditional methods of classifying surrounding rocks, a Gaussian process-based model for classification of surrounding rocks is proposed. The nonlinear mapping relationship between the classification of surrounding rocks and influencing factors is easily established using the Gaussian process model for machine learning, which possesses excellent classification performance based on making use of the historical knowledge of real engineering projects. The model has been applied to the classification of the surrounding rocks of the Erlangshan Tunnel on the Sichuan-Tibet Highway. The results of this case study show that the model is feasible and that reasonable, reliable and probabilistic results for classification of surrounding rocks can be obtained quickly by using the proposed model. Compared with other machine learning technologies, such as artificial neural networks and support vector machines, the proposed model has the beneficial characteristics of self-adaptive parameter determination and an uncertainty evaluation of predicted results.
ZHANG Yan,
Su-Guo-Shao,
Yan-Liu-Bin
.Classification of Rocks Surrounding Tunnel Based on Gaussian Process for Machine Learning[J] MODERN TUNNELLING TECHNOLOGY, 2011,V48(6): 32-37
2011, Vol. 48 