TBM掘进速率区间预测Bootstrap-IHHO-BiLSTM模型  

Interval prediction Bootstrap-IHHO-BiLSTM model for TBM advance rate

作　　者：王晓玲[1] 韩国玺 余佳[1] 王佳俊[1] 徐国鑫 肖尧[1] WANG Xiaoling;HAN Guoxi;YU Jia;WANG Jiajun;XU Guoxin;XIAO Yao(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350,China;Shaanxi Province Yinhan Jiwei Engineering Construction Limited Liability Company,Xi’an 710302,China)

机构地区：[1]天津大学水利工程仿真与安全国家重点实验室,天津300350 [2]陕西省引汉济渭工程建设有限公司,西安710302

出　　处：《水力发电学报》2023年第12期159-171,共13页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金项目(52279137,52009090)。

摘　　要：针对现有隧道掘进机(TBM)掘进速率预测模型多采用点预测模型,缺乏考虑因模型结构主观选择、模型参数随机设置和数据随机噪声等导致的不确定性问题,本文提出基于Bootstrap方法和改进哈里斯鹰优化双向长短时记忆网络(BiLSTM)的TBM掘进速率区间预测模型。首先,建立基于改进哈里斯鹰(IHHO)优化BiLSTM网络的TBM掘进速率点预测模型,揭示稳定段掘进速率与上升段刀盘推力、扭矩、转速等掘进参数之间的相关性和时间依赖性;其中,采用基于混沌映射、参数非线性化和混沌搜索策略改进的哈里斯鹰算法对BiLSTM网络超参数进行优化,提高建模效率和精度。进一步地,采用Bootstrap方法对模型不确定性和数据中的随机不确定性进行量化,获得清晰可靠的预测区间。将所提模型应用于引汉济渭秦岭隧洞工程中,开展I~III类围岩条件下的TBM掘进速率区间预测,并将结果与BiLSTM-HHO模型、BiLSTM模型、BP神经网络模型对比,证明了本文模型的优越性。Previous prediction models of the Tunnel Boring Machine(TBM)advance rate mostly adopted the point prediction method and lacked consideration of the uncertainties caused by the subjective selection of model structure,random parameter setting,and random data noise.This paper develops an interval prediction model of the TBM boring rate based on the Bootstrap method and the improved Harris Eagle optimized bi-directional long short-term memory network(BiLSTM).First,we construct a prediction model based on the Improved Harris Hawks Optimization(IHHO)optimized BiLSTM network,and reveal the correlation and time dependency of the boring rate for the stable section operation on the thrust,torque,speed and other boring parameters of the cutterhead for the rising section operation.This model uses the Harris Eagle algorithm based on chaotic mapping,parameter nonlinearization and chaos search strategy to optimize the hyper-parameters of its BiLSTM network for better modeling efficiency and accuracy.Then,the Bootstrap method is used to quantify its model uncertainty and random uncertainty and to obtain clear and reliable prediction intervals.It has been applied to the Qinling Mountain tunnel project under the conditions of surrounding rock class I-III.The results are compared with those of the BILSTM-HHO model,BiLSTM model and BP neural network model,proving the superiority of our new model.

关 键 词：隧道掘进机(TBM) 掘进速率 区间预测 双向长短时记忆网络 哈里斯鹰优化算法 BOOTSTRAP方法 

分 类 号：TV554[水利工程—水利水电工程]