面向隧道掘进机可掘性评价的多算法融合优化模型及其工程应用

doi:10.13722/j.cnki.jrme.2021.0591

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摘要开展隧道掘进机(TBM)可掘性评价对于科学规划施工要素具有重要意义。在Karaj输水隧道、Zagros输水隧道和西秦岭铁路隧道的219组数据基础上，建立一种基于贝叶斯优化算法和早停策略改进的深度信念网络TBM可掘性评价模型。该模型以岩石单轴抗压强度(UCS)、岩石质量指标(RQD)、优势结构面与隧道轴向的夹角( )和岩体可切削性指数(RMCI)作为输入变量，以现场贯入度指数(FPI)作为输出变量。在数据预处理阶段，分别采用Kriging插值和改进的CRITIC算法补全数据库中的缺失值并实现数据加权。以引松供水工程和辽西北引水工程为例检验模型的工程实用性：对于引松供水工程的37组测试数据，模型的均方根误差(RMSE)、平均绝对百分比误差(MAPE)和决定系数( )分别为2.18，8.25%和0.926 2；对于辽西北引水工程的49组测试数据，模型的RMSE，MAPE和分别为2.83，8.14%和0.981 7。进一步地，通过定量比较数据加权前、后模型的RMSE，MAPE和，发现数据加权可以有效改善模型预测性能。最后，从预测精度和运行速度2个方面开展了本文模型与BP神经网络、支持向量回归、K–近邻和随机森林的对比分析，验证了本文模型的优越性。

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	殷欣1
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	高峰1
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	刘泉声1
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	王心语1
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	黄兴3
	潘玉丛1
	2

关键词 ：岩石力学, 深度信念网络, 贝叶斯优化, 早停策略, 多算法融合, 隧道掘进机, 可掘性评价

Abstract：Carrying out the evaluation of the boreability of tunnel boring machines(TBMs) has an important guiding significance for scientific planning of key construction factors. Based on 219 sets of data of Karaj water conveyance tunnel，Zagros water conveyance tunnel，and West-Qinling railway tunnel，this paper proposed a deep belief network model for the TBM boreability evaluation，improved by Bayes optimization algorithm and early-stopping strategy. This model used rock uniaxial compressive strength(UCS)，rock quality designation(RQD)，the angle between the dominant structural plane and the tunnel axis( )，and rock mass cuttability index(RMCI) as input variables，and used field penetration index(FPI) as the output variable. In the data preprocessing stage，Kriging interpolation and improved CRITIC algorithm were separately used to complement the missing values in the database and implement data weighting. Taking Yinsong water conveyance tunnel and LXB water conveyance tunnel as examples to test the practicality of the model：For 37 sets of test data of Yinsong water conveyance tunnel，the root mean square error (RMSE)，mean absolute percentage error(MAPE)，and coefficient of determination( ) were 2.18，8.25%，and 0.926 2，respectively；for the 49 sets of test data of LXB water conveyance tunnel，the RMSE，MAPE，and were 2.83，8.14%，and 0.981 7，respectively. Furthermore，by quantitatively comparing the RMSE，MAPE and of the model before and after data weighting，data weighting was an effective way to improve the prediction performance of the model. Finally，a comparative analysis of the model in this paper with BP neural network，support vector regression，K-nearest neighbor and random forest was conducted in terms of prediction accuracy and running speed，verifying the superiority of the model in this paper.

Key words： rock mechanics deep belief network bayes optimization early-stopping strategy multi-algorithm fusion tunnel boring machine boreability evaluation

引用本文:

殷欣1，2，高峰1，2，刘泉声1，2，王心语1，2，黄兴3，潘玉丛1，2. 面向隧道掘进机可掘性评价的多算法融合优化模型及其工程应用[J]. 岩石力学与工程学报, 2022, 41(S1): 2757-2771.
YIN Xin1，2，GAO Feng1，2，LIU Quansheng1，2，WANG Xinyu1，2，HUANG Xing3，PAN Yucong1，2. Multi-algorithm fusion-optimization model and its engineering application for boreability evaluation of tunnel boring machine. , 2022, 41(S1): 2757-2771.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2021.0591 或 http://rockmech.whrsm.ac.cn/CN/Y2022/V41/IS1/2757

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