基于优化FAHP-TOPSIS法的高压富水花岗岩断层涌水预测
|
摘要
隧道穿越花岗岩断层带施工的最主要灾害为高压富水体的突涌,其危险性极高且破坏力巨大。为有效解决理论计算、模型试验、数值模拟等方法中隧道涌水量预测值与实际值存在较大误差的核心问题,从系统辨识工程地质、水文地质和施工设计3方面共13个独立的花岗岩断层涌水致灾影响因素入手,提出基于优化FAHP-TOPSIS法的隧道断层涌水精细预测方法。该方法的创新在于采用平均优势度进行模糊层次分析法(FAHP)的判断矩阵优化,能解决传统FAHP法排序互斥而导致权重分配失衡的问题;同时,采用逼近理想解排序法(TOPSIS)替代模糊综合评价法,避免模糊综合评价法依赖大量样本数据及模型训练冗余的问题,能极大提高隧道涌水预测精度。并将优化FAHP-TOPSIS法应用于涌水预测实例中,实现6处断层涌水区段涌水等级及涌水量的精细预测。预测结果表明:S_1、S_4、S_5、S_6区段存在中等涌水风险,S_2、S_3区段存在大涌水风险;6处断层涌水区段预测涌水量与实际涌水量的最大相对误差为14.8%,平均相对误差为8.87%,满足工程准确预测精度要求。
The main construction disaster of tunnel crossing granite fault zone is the inrush of high-pressure and rich water, which is extremely dangerous and destructive. In order to effectively solve the core problem that large errors exist between the predicted values and actual values of tunnel water inflow in theoretical calculation, model test and numerical simulation, 13 independent disaster-causing factors for granite fault water inrush in engineering geology, hydrogeology and construction design are systematically identified, and a fine prediction method of tunnel fault water gushing based on optimized FAHP-TOPSIS method is proposed. The innovations of the method mentioned above are using average dominance to optimize the judgment matrix of FAHP, which can solve the problem of unbalanced weight distribution caused by mutual exclusion of traditional FAHP ranking method. Meanwhile, the TOPSIS is used to replace fuzzy comprehensive evaluation method, which avoids the problem that the fuzzy comprehensive evaluation method relies on a large number of sample data and model training redundancy, and greatly improves the prediction accuracy of tunnel water gushing. The optimized FAHP-TOPSIS method is applied to the prediction of water gushing, and the fineprediction of water gushing grade and water inflow in 6 fault water gushing sections is realized. The prediction results show that:(1) There is medium risk of water gushing in S_1, S_4, S_5 and S_6, and large risk of water gushing in S_2 and S_3;(2) The maximum relative error between predicted water inflow and actual water inflow in 6 fault water gushing sections is 14. 8%, and the average relative error is 8. 87%, which meets the requirement of engineering prediction accuracy.
The main construction disaster of tunnel crossing granite fault zone is the inrush of high-pressure and rich water, which is extremely dangerous and destructive. In order to effectively solve the core problem that large errors exist between the predicted values and actual values of tunnel water inflow in theoretical calculation, model test and numerical simulation, 13 independent disaster-causing factors for granite fault water inrush in engineering geology, hydrogeology and construction design are systematically identified, and a fine prediction method of tunnel fault water gushing based on optimized FAHP-TOPSIS method is proposed. The innovations of the method mentioned above are using average dominance to optimize the judgment matrix of FAHP, which can solve the problem of unbalanced weight distribution caused by mutual exclusion of traditional FAHP ranking method. Meanwhile, the TOPSIS is used to replace fuzzy comprehensive evaluation method, which avoids the problem that the fuzzy comprehensive evaluation method relies on a large number of sample data and model training redundancy, and greatly improves the prediction accuracy of tunnel water gushing. The optimized FAHP-TOPSIS method is applied to the prediction of water gushing, and the fineprediction of water gushing grade and water inflow in 6 fault water gushing sections is realized. The prediction results show that:(1) There is medium risk of water gushing in S_1, S_4, S_5 and S_6, and large risk of water gushing in S_2 and S_3;(2) The maximum relative error between predicted water inflow and actual water inflow in 6 fault water gushing sections is 14. 8%, and the average relative error is 8. 87%, which meets the requirement of engineering prediction accuracy.
引文
[1]曹放,蒲超,方兴,等.米仓山隧道涌突水综合预测研究[J].人民珠江,2017,38(3):17.CAO Fang,PU Chao,FANG Xing,et al.Research on the comprehensive prediction of water inrush in Micangshan Tunnel[J].Pearl River,2017,38(3):17.
[2]尹士清.戴云山隧道涌水量的预测和验证分析[J].铁道工程学报,2015,32(12):70.YIN Shiqing.Prediction and verification analysis of water inflow for Daiyunshan Tunnel[J].Journal of Railway Engineering Society,2015,32(12):70.
[3]崔建宏.六沾铁路复线乌蒙山一号隧道涌水量预测[J].路基工程,2015(1):141.CUI Jianhong.Estimation of water-gushing amount of No.1Wumeng Mountain Tunnel of Liupanshui-Zhanyi doubletrack railway[J].Subgrade Engineering,2015(1):141.
[4]王健华,李术才,李利平,等.富水岩层隧道区域涌水量预测方法及工程应用[J].人民长江,2016,47(14):40.WANG Jianhua,LI Shucai,LI Liping,et al.Prediction method of water inrush tunnels in water rich area and its engineering application[J].Yangtze River,2016,47(14):40.
[5]顾博渊,史宝童,黄嫚.山岭隧道涌水量预测方法分类及相关因素分析[J].隧道建设,2015,35(12):1258.GU Boyuan,SHI Baotong,HUANG Man.Classification of water inflow prediction methods for mountain-crossing tunnels and analysis on related factors[J].Tunnel Construction,2015,35(12):1258.
[6]李铮,何川,杨赛舟,等.不考虑开挖扰动影响的隧道涌水量预测模型试验研究[J].岩石力学与工程学报,2016,35(12):2499.LI Zheng,HE Chuan,YANG Saizhou,et al.Experimental study of tunnel inflow without considering the influence of excavation disturbance[J].Chinese Journal of Rock Mechanics and Engineering,2016,35(12):2499.
[7]雷楷,张洪伟,张国珍,等.基于Visual Modflow的隧道涌水量预测研究[J].工程建设,2018,50(8):1.LEI Kai,ZHANG Hongwei,ZHANG Guozhen,et al.Study of prediction of water inflow in tunnel based on Visual Modflow[J].Engineering Construction,2018,50(8):1.
[8]陈东斌,陈益峰,洪佳敏,等.丹巴水电站长引水隧洞涌水量预测[J].武汉大学学报(工学版),2017,50(2):193.CHEN Dongbin,CHEN Yifeng,HONG Jiamin,et al.Water inflow predictions of Danba water diversion tunnel[J].Engineering Journal of Wuhan University,2017,50(2):193.
[9]贺小勇,漆继红,许模,等.复杂地质条件下隧道涌水量预测中集水面积的研究[J].隧道建设,2017,37(1):56.HE Xiaoyong,QI Jihong,XU Mo,et al.Study of catchment area of water inrush volume forecast of tunnel in complex geological conditions[J].Tunnel Construction,2017,37(1):56.
[10]郭锁山.基于层次分析法隧道涌水量预测影响因素分析[J].建筑技术开发,2017,44(14):58.GUO Suoshan.Analysis of influencing factors of tunnel gushing water forecast based on analytic hierarchy process[J].Building Technology Development,2017,44(14):58.
[11]廖志泓.基于BP神经网络模型预测隧道涌水量的探讨[J].铁路工程技术与经济,2017,32(5):5.LIAO Zhihong.Prediction of water inflow in tunnel based on BP neural network model[J].Railway Engineering Technology and Economy,2017,32(5):5.
[12]武明静.基于层次分析法的浅埋大跨城市隧道扁平率优化研究[J].施工技术,2017,46(11):77.WU Mingjing.Study of flat ratio optimization of large-span shallow buried city tunnel based on analytic hierarchy process[J].Construction Technology,2017,46(11):77.
[13]陈诚.基于改进熵权-TOPSIS的隧道塌方风险等级分类[J].水力发电,2016,42(6):30.CHEN Cheng.Risk grade evaluation of tunnel collapse based on entropy weight method-TOPSIS[J].Water Power,2016,42(6):30.
[14]邬晓光,贺攀,苏兴矩,等.基于海明距离-TOPSIS法的山区公路隧道扩建方案直觉模糊优选[J].隧道建设,2017,37(8):926.WU Xiaoguang,HE Pan,SU Xingju,et al.Intuitionistic fuzzy optimization of enlargement scheme of highway tunnel in mountain areas based on hamming distance-TOPSISmethod[J].Tunnel Construction,2017,37(8):926.
[15]陈雪锋,陈文涛,苗永春.基于熵权TOPSIS法的岩爆预测[J].煤炭技术,2018,37(6):38.CHEN Xuefeng,CHEN Wentao,MIAO Yongchun.Rock burst prediction based on entropy weight TOPSIS method[J].Coal Technology,2018,37(6):38.
[16]侯东赛,张霄,王磊.基于综合赋权-TOPSIS法隧道突涌水风险评价及应用[J].隧道建设,2017,37(6):691.HOU Dongsai,ZHANG Xiao,WANG Lei.Risk evaluation of tunnel water inrush based on comprehensive weightingTOPSIS method and its application[J].Tunnel Construction,2017,37(6):691.
[17]关宝树.漫谈矿山法隧道技术第十四讲:隧道涌水及其控制方法[J].隧道建设,2017,37(1):1.GUAN Baoshu.Tunneling by mining method:LectureⅩⅣ:Tunnel water inrush and its countermeasures[J].Tunnel Construction,2017,37(1):1.
[2]尹士清.戴云山隧道涌水量的预测和验证分析[J].铁道工程学报,2015,32(12):70.YIN Shiqing.Prediction and verification analysis of water inflow for Daiyunshan Tunnel[J].Journal of Railway Engineering Society,2015,32(12):70.
[3]崔建宏.六沾铁路复线乌蒙山一号隧道涌水量预测[J].路基工程,2015(1):141.CUI Jianhong.Estimation of water-gushing amount of No.1Wumeng Mountain Tunnel of Liupanshui-Zhanyi doubletrack railway[J].Subgrade Engineering,2015(1):141.
[4]王健华,李术才,李利平,等.富水岩层隧道区域涌水量预测方法及工程应用[J].人民长江,2016,47(14):40.WANG Jianhua,LI Shucai,LI Liping,et al.Prediction method of water inrush tunnels in water rich area and its engineering application[J].Yangtze River,2016,47(14):40.
[5]顾博渊,史宝童,黄嫚.山岭隧道涌水量预测方法分类及相关因素分析[J].隧道建设,2015,35(12):1258.GU Boyuan,SHI Baotong,HUANG Man.Classification of water inflow prediction methods for mountain-crossing tunnels and analysis on related factors[J].Tunnel Construction,2015,35(12):1258.
[6]李铮,何川,杨赛舟,等.不考虑开挖扰动影响的隧道涌水量预测模型试验研究[J].岩石力学与工程学报,2016,35(12):2499.LI Zheng,HE Chuan,YANG Saizhou,et al.Experimental study of tunnel inflow without considering the influence of excavation disturbance[J].Chinese Journal of Rock Mechanics and Engineering,2016,35(12):2499.
[7]雷楷,张洪伟,张国珍,等.基于Visual Modflow的隧道涌水量预测研究[J].工程建设,2018,50(8):1.LEI Kai,ZHANG Hongwei,ZHANG Guozhen,et al.Study of prediction of water inflow in tunnel based on Visual Modflow[J].Engineering Construction,2018,50(8):1.
[8]陈东斌,陈益峰,洪佳敏,等.丹巴水电站长引水隧洞涌水量预测[J].武汉大学学报(工学版),2017,50(2):193.CHEN Dongbin,CHEN Yifeng,HONG Jiamin,et al.Water inflow predictions of Danba water diversion tunnel[J].Engineering Journal of Wuhan University,2017,50(2):193.
[9]贺小勇,漆继红,许模,等.复杂地质条件下隧道涌水量预测中集水面积的研究[J].隧道建设,2017,37(1):56.HE Xiaoyong,QI Jihong,XU Mo,et al.Study of catchment area of water inrush volume forecast of tunnel in complex geological conditions[J].Tunnel Construction,2017,37(1):56.
[10]郭锁山.基于层次分析法隧道涌水量预测影响因素分析[J].建筑技术开发,2017,44(14):58.GUO Suoshan.Analysis of influencing factors of tunnel gushing water forecast based on analytic hierarchy process[J].Building Technology Development,2017,44(14):58.
[11]廖志泓.基于BP神经网络模型预测隧道涌水量的探讨[J].铁路工程技术与经济,2017,32(5):5.LIAO Zhihong.Prediction of water inflow in tunnel based on BP neural network model[J].Railway Engineering Technology and Economy,2017,32(5):5.
[12]武明静.基于层次分析法的浅埋大跨城市隧道扁平率优化研究[J].施工技术,2017,46(11):77.WU Mingjing.Study of flat ratio optimization of large-span shallow buried city tunnel based on analytic hierarchy process[J].Construction Technology,2017,46(11):77.
[13]陈诚.基于改进熵权-TOPSIS的隧道塌方风险等级分类[J].水力发电,2016,42(6):30.CHEN Cheng.Risk grade evaluation of tunnel collapse based on entropy weight method-TOPSIS[J].Water Power,2016,42(6):30.
[14]邬晓光,贺攀,苏兴矩,等.基于海明距离-TOPSIS法的山区公路隧道扩建方案直觉模糊优选[J].隧道建设,2017,37(8):926.WU Xiaoguang,HE Pan,SU Xingju,et al.Intuitionistic fuzzy optimization of enlargement scheme of highway tunnel in mountain areas based on hamming distance-TOPSISmethod[J].Tunnel Construction,2017,37(8):926.
[15]陈雪锋,陈文涛,苗永春.基于熵权TOPSIS法的岩爆预测[J].煤炭技术,2018,37(6):38.CHEN Xuefeng,CHEN Wentao,MIAO Yongchun.Rock burst prediction based on entropy weight TOPSIS method[J].Coal Technology,2018,37(6):38.
[16]侯东赛,张霄,王磊.基于综合赋权-TOPSIS法隧道突涌水风险评价及应用[J].隧道建设,2017,37(6):691.HOU Dongsai,ZHANG Xiao,WANG Lei.Risk evaluation of tunnel water inrush based on comprehensive weightingTOPSIS method and its application[J].Tunnel Construction,2017,37(6):691.
[17]关宝树.漫谈矿山法隧道技术第十四讲:隧道涌水及其控制方法[J].隧道建设,2017,37(1):1.GUAN Baoshu.Tunneling by mining method:LectureⅩⅣ:Tunnel water inrush and its countermeasures[J].Tunnel Construction,2017,37(1):1.