Abstract
Scientific classification of rock masses surrounding tunnels has great significance for construction cost and risk in subway systems. Quantifying the surrounding rock simply, quickly, and accurately is always a challenging issue as well as an urgent requirement in construction. Surrounding rock classification considers many complex and variable factors with uncertainty and nonlinear characteristics. Using principal component analysis (PCA) and the ideal point method, a new classification model is built consisting of five key factors, uniaxial compressive strength (UCS), rock mass integrity coefficient (Kv), softening coefficient (η), joint surface coefficient (Jc), and groundwater (ω). In the model, weights of key factors are determined by PCA, then the level of the surrounding rocks is analyzed using ideal point theory. The new model is applied successfully to classify surrounding rock in the Qingdao Metro system. Results provide a reference for classifying surrounding rock quickly and guide the tunnel design and construction.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (grant numbers 51422904, 51379112, and 41772298) and the Shandong Provincial Natural Science Foundation (grant number ZR2013EEQ024, JQ201513, and ZR2017MEE032). The authors would like to express appreciation to the reviewers for their valuable comments and suggestions that helped improve the quality of our paper.
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Xue, Y., Li, Z., Qiu, D. et al. Classification model for surrounding rock based on the PCA-ideal point method: an engineering application. Bull Eng Geol Environ 78, 3627–3635 (2019). https://doi.org/10.1007/s10064-018-1368-5
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DOI: https://doi.org/10.1007/s10064-018-1368-5