Abstract: In order to improve the reliability of prediction of large deformation level of layered soft rock tunnels under complex geological conditions, a particle swarm optimization(PSO)based support vector machine(SVM)was proposed. The large deformation prediction method of tunnel solved the problems of complex calculation of multiple evaluation index weights and diverse boundary values in large deformation prediction. In order to fully consider the influence of layered soft rock surrounding rock strength, surrounding rock structure type, in-situ stress and groundwater on the large deformation of the tunnel, the 6 sub-indices of value: uniaxial compressive strength, rock inclination angle, initial in-situ stress state, burial depth, rock mass correction quality correction index [BQ] and groundwater development, were used to predict the large deformation level. This method constructed a large deformation prediction model based on in-situ stress inversion and on-site large deformation monitoring information according to the classification standard of large deformation grades, and used particle swarm optimization algorithm to adjust the penalty parameter C and the kernel function parameter Gamma to improve the model's performance accuracy. The research results showed that the use of particle swarm optimization-support vector machine(PSO-SVM)algorithm could avoid errors caused by traditional prediction methods such as geological comprehensive judgment method and strength-stress ratio method due to a single index and subjective reasons, and the prediction accuracy was high; this method used the large deformation information of tunnels that had occurred, a PSO-SVM large deformation prediction model conforming to the actual law of the target tunnel site was constructed; the PSO-SVM model had an accuracy of 86.36% in the prediction of the sample test set, which was better than the SVM and GS-SVM models. Taking the Baima Tunnel, a typical layered soft rock tunnel of the Jiu-mian Expressway as the research object, the proposed PSO-SVM model was used to conduct large-scale research. The deformation prediction was compared with the field measurement, and it was found that the prediction accuracy reached 80%, which verified the feasibility of the method.
杨文波, 王宗学, 田浩晟, 吴枋胤, 杨自成. 基于PSO-SVM算法的层状软岩隧道大变形预测方法[J]. 隧道与地下工程灾害防治, 2022, 4(1): 29-37.
YANG Wenbo, WANG Zongxue, TIAN Haosheng, WU Fangyin, YANG Zicheng. Large deformation prediction method of layered soft rock tunnel based on PSO-SVM algorithm. Hazard Control in Tunnelling and Underground Engineering, 2022, 4(1): 29-37.
[1] 代聪.高地应力场软岩隧道开挖与支护研究[D].成都:西南交通大学,2018. DAI Cong. Study on excavation and support of soft rock tunnel in high ground stress field[D]. Chengdu: Southwest Jiaotong University, 2018. [2] 石州,罗彦斌,陈建勋,等.木寨岭公路隧道大变形综合评价预测[J].公路交通科技,2020,37(8):90-98. SHI Zhou, LUO Yanbin, CHEN Jianxun, et al. Comprehensive evaluation and prediction of large deformation of Muzhailing Highway Tunnel[J]. Journal of Highway and Transportation Research and Development, 2020, 37(8): 90-98. [3] 徐林生.二郎山公路隧道岩爆特征与防治措施的研究[J].土木工程学报,2004,37(1):61-64. XU Linsheng. Research of rockburst character and prevention measure in Erlang Mountain Highway Tunnel[J]. China Civil Engineering Journal, 2004, 37(1):61-64. [4] HOEK E, MARINOS P. Predicting tunnel squeezing problems in weak heterogeneous rock masses[J]. Tunnels and Tunnelling International, 2000, 32(11):45-51. [5] 李国良,刘志春,朱永全.兰渝铁路高地应力软岩隧道挤压大变形规律及分级标准研究[J].现代隧道技术,2015,52(1):62-68. LI Guoliang, LIU Zhichun, ZHU Yongquan. On the large squeezing deformation law and classification criteria for the Lanzhou-Chongqing Railway tunnels in soft and high geostress rocks[J]. Modern Tunnelling Technology, 2015, 52(1):62-68. [6] 陈子全,何川,吴迪,等.高地应力层状软岩隧道大变形预测分级研究[J].西南交通大学学报,2018,53(6):1237-1244. CHEN Ziquan, HE Chuan, WU Di, et al. Study of large deformation classification criterion for layered soft rock tunnels under high geostress[J]. Journal of Southwest Jiaotong University, 2018, 53(6): 1237-1244. [7] 孙元春,高波,许再良,等.隧道围岩挤压变形预测方法研究[J].铁道工程学报,2012,29(2):50-54. SUN Yuanchun, GAO Bo, XU Zailiang, et al. Research on prediction method for squeezing deformation of surrounding rock of tunnel[J]. Journal of Railway Engineering Society, 2012, 29(2): 50-54. [8] 廖烟开,郭德平,刘志强,等.隧道周边应变与挤压因子法在隧道围岩大变形预测中的应用[J].现代隧道技术,2020,57(4):20-26. LIAO Yankai, GUO Deping, LIU Zhiqiang, et al. Application of peripheral strain and squeezing factor methods in the prediction of large deformation of tunnel surrounding rocks[J]. Modern Tunnelling Technology, 2020, 57(4):20-26. [9] 王开洋,尚彦军,何万通,等.深埋公路隧道围岩大变形预测研究[J].地下空间与工程学报,2015,11(5):1164-1174. WANG Kaiyang, SHANG Yanjun, HE Wantong, et al. Prediction of surrounding rock deformation in deep highway tunnel[J]. Chinese Journal of Underground Space and Engineering, 2015, 11(5): 1164-1174. [10] 刘志春,朱永全,李文江,等.挤压性围岩隧道大变形机理及分级标准研究[J].岩土工程学报,2008,30(5):690-697. LIU Zhichun, ZHU Yongquan, LI Wenjiang, et al. Mechanism and classification criterion for large deformation of squeezing ground tunnels[J]. Chinese Journal of Geotechnical Engineering, 2008, 30(5): 690-697. [11] 孙炀. 软岩隧道挤压大变形的SVM预测及其支护对策研究[D].济南:济南大学,2018. SUN Yang. Prediction of tunnel squeezing by SVM and study on its support measures[D]. Jinan: University of Jinan, 2018. [12] 黄震,廖敏杏,张皓量,等.基于SVM-BP模型非完整数据的隧道围岩挤压变形预测[J].现代隧道技术,2020,57(增刊1):129-138. HUANG Zhen, LIAO Minxing, ZHANG Haoliang, et al. Prediction of tunnel surrounding rock extrusion deformation based on SVM-BP model with incomplete data[J]. Modern Tunnelling Technology, 2020, 57(Suppl.1):129-138. [13] 焦玉勇,欧光照,王浩,等.基于证据理论的隧道挤压变形预测[J].应用基础与工程科学学报,2021,29(5):1156-1170. JIAO Yuyong, OU Guangzhao, WANG Hao, et al. Prediction of tunnel squeezing based on evidence theory[J]. Journal of Basic Science and Engineering, 2021, 29(5): 1156-1170. [14] 易文豪,王明年,童建军,等.基于支持向量机的大断面岩质隧道掌子面围岩非均一性判识方法[J].中国铁道科学,2021,42(5):112-122. YI Wenhao, WANG Mingnian, TONG Jianjun, et al. Inhomogeneity identification method for surrounding rock of large-section rock tunnel face based on support vector machine[J]. China Railway Science, 2021, 42(5): 112-122. [15] 曹跃. 基于支持向量回归的双相不锈钢抗点蚀性能及焊接工艺优化研究[D].重庆:重庆大学,2016. CAO Yue. Prediction on the pitting corrosion resistance of duplex stainless steel and its welding process optimization via SVR[D].Chongqing:Chongqing University, 2016. [16] 刘超,乔圣扬.基于主成分分析与多分类支持向量机的单沟泥石流危险性预测[J].河北地质大学学报,2021,44(4):83-89. LIU Chao, QIAO Shengyang. Hazard prediction of single gully debris flow based on principal component analysis and multi-classification support vector machine[J].Journal of Hebei GEO University, 2021, 44(4):83-89. [17] 李天斌, 孟陆波, 王兰生.高地应力隧道稳定性及岩爆、大变形灾害防治[M].北京:科学出版社,2016. [18] 陈卫忠,田云,王学海,等.基于修正[BQ] 值的软岩隧道挤压变形预测[J].岩土力学,2019,40(8):3125-3134. CHEN Weizhong, TIAN Yun, WANG Xuehai, et al. Squeezing prediction of tunnel in soft rocks based on modified [BQ] [J]. Rock and Soil Mechanics, 2019, 40(8):3125-3134. [19] 招商局重庆交通科研设计院有限公司.公路隧道设计规范:JTG 3370.1—2018[S].北京:中国建筑标准出版社,2018. [20] 徐林生,李永林,程崇国.公路隧道围岩变形破裂类型与等级的判定[J].重庆交通大学学报(自然科学版),2002(2):16-20. XU Linsheng, LI Yongling, CHENG Chongguo. Judging of the deformation-cracking type and grade about surrounding rock of highway tunnel [J].Journal of Chongqing Jiaotong University(Natural Science), 2002(2):16-20. [21] BEWICK R P, KAISER P K, AMANN F. Strength of massive to moderately jointed hard rock masses[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2019, 11(3): 562-575. [22] MAO J Z, HUANG Z W, XIAO L P, et al. Prediction of mechanical parameters of the surrounding rock based on Hoek-Brown criterion[J].IOP Conference Series: Earth and Environmental Science, 2020, 570(5):052071.