Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of incompetent rock mass based on characteristic parameters and neural network
TBM is famous for its safety and efficiency of tunnel boring, but TBM is also easy to encounter geological disasters in incompetent rock mass with its poor geological adaptability. Unfortunately, due to the shelter from cutterhead and shield, it’s hard to diagnose and identify the quality of rock mass. Based on this, this paper uses the torque penetration index TPI to predict incompetent rock mass, the main steps can be summarized as follows: firstly, we use linear fitting to calculate TPI, secondly, build the prediction model respectively with II-III grade of surrounding rocks and IV-V grade of surrounding rocks based on neural network, thirdly, predict the current TPI by the TPI of last boring cycles, finally, calculate the predicted torque based on the penetration and predicted TPI, then the incompetent rock mass is quickly identified through the error analysis between actual torque and predicted torque
Prediction of incompetent rock mass based on characteristic parameters and neural network
TBM is famous for its safety and efficiency of tunnel boring, but TBM is also easy to encounter geological disasters in incompetent rock mass with its poor geological adaptability. Unfortunately, due to the shelter from cutterhead and shield, it’s hard to diagnose and identify the quality of rock mass. Based on this, this paper uses the torque penetration index TPI to predict incompetent rock mass, the main steps can be summarized as follows: firstly, we use linear fitting to calculate TPI, secondly, build the prediction model respectively with II-III grade of surrounding rocks and IV-V grade of surrounding rocks based on neural network, thirdly, predict the current TPI by the TPI of last boring cycles, finally, calculate the predicted torque based on the penetration and predicted TPI, then the incompetent rock mass is quickly identified through the error analysis between actual torque and predicted torque
Prediction of incompetent rock mass based on characteristic parameters and neural network
Wen, Fushuan (Herausgeber:in) / Zhao, Chuanjun (Herausgeber:in) / Chen, Yanjiao (Herausgeber:in) / Gong, Xiqiao (Autor:in) / Zhang, Yunpei (Autor:in) / Liu, Lipeng (Autor:in) / Liu, Qing (Autor:in) / Liu, Qi (Autor:in) / Wang, Shuangjing (Autor:in)
Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023) ; 2023 ; Nanjing, China
Proc. SPIE ; 12709
19.10.2023
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
| British Library Online Contents | 1998
Probabilistic machine learning approach to predict incompetent rock masses in TBM construction
| Springer Verlag | 2023