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| TBM tunneling parameters prediction method based on clustering classification of rock mass |
| LI Jianbin1，ZHENG Yinghao2，JING Liujie2，3，CHEN Shuai2，JIAN Peng2，YU Taizhang2，ZHAO Yanzhen2 |
| (1. China Railway Hi-Tech Industry Co.，Ltd.，Beijing 100071，China；2. China Railway Engineering Equipment Group Co.，Ltd.，Zhengzhou，Henan 450016，China；3. State Key Laboratory for Geomechanics and Deep Underground Engineering，
China University of Mining and Technology，Xuzhou，Jiangsu 221006，China)
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Abstract Accurate prediction of TBM tunneling parameters under complex geological conditions can optimize and adjust the tunneling parameters in time to effectively guide equipment construction. According to on-site tunneling data from the 3rd TBM section of Songhua River water supply project in Jilin province，this paper first uses least square method to perform regression analysis on TBM tunneling parameters and on-site tracked rock mechanics parameters，which realizes the transformation from mechanical parameters to rock mass information. Then k-means method is used to classify the estimated rock mechanics parameters to establish a database including rock mass properties and machine parameters under different surrounding rock grades. Finally，the TBM rock mass parameters and machine parameters corresponding to surrounding rock grades is used as the model input，operating or control parameters as the model output target，and ELM-based machine learning algorithm is utilized to construct predictive models matching the surrounding rock grade. The predictive value fits the change trend of the measured data well，and the average error is less than 12%. The results show that TBM tunneling parameters prediction method based on clustering classification of rock mass can significantly improve the problems of low prediction accuracy and poor robustness of TBM tunneling parameters under the dynamic change of rock mass.
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2020, Vol. 39 
