富水区大埋深高渗压隧洞涌水预测技术研究
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摘要
锦屏二级水电站引水洞工程是世界上目前规模最大的水工隧洞工程,由于施工受阻及工期紧缺,增设辅引3“施工支洞以增加工作面,解救被强岩爆掩埋的TBM等施工设备并且保障工程如期顺利完成。可见,辅引3“施工支洞的顺利完成对锦屏二级水电站引水洞工程至关重要。然而,辅引3#支洞涌水问题是本工程最大的重难点,具有突发性、大流量、高水压等特点,对其涌水量进行合理预测成为工程建设过程必须得面对与解决的难题之一。
本文以锦屏二级水电站辅引3“施工支洞工程为背景,综合运用理论分析、数值模拟和现场测试等研究方法对隧洞涌水进行预测研究。在详细介绍隧道涌水预测的传统方法的基础上,利用BP神经网络方法建立“富水区大埋深高渗压隧洞涌水预测模型”,并采用模糊数学和MODFLOW数值分析两种方法对涌水量进行计算,其计算结果用来对比验证“富水区大埋深高渗压隧洞涌水预测模型”可靠性。本文的主要研究内容及结论如下：
1、对锦屏二级水电站辅引3#施工支洞的工程地质和水文地质条件进行了分析,揭示了辅引3“支洞涌水的突发性、大流量、高水压是本工程最大的重难点。影响辅引3“支洞涌水的因素主要分为三大类共计11个因素,分别为工程地质条件、水文地质条件和隧洞工况。其中工程地质条件具体包括地质构造、最大埋深、围岩级别、渗透系数和岩溶发育5个影响因素,水文地质条件主要考虑了水头高度、降水量和地表环境对涌水的影响,隧洞工况的具体因素为隧道半径、施工工况和隧道长度的影响。
2、综合分析隧洞涌水的影响因素得出,地质构造、水头高度、渗透系数、降水量和隧道半径对涌水量的影响最为显著。其中将地质构造因素按地质构造等级采用1-100由定性指标转化为定量指标,能够较好地计算出隧洞涌水量。基于这五种因素与隧洞涌水量之间的关系,构建了基于BP神经网络方法的“富水区大埋深高渗压隧洞涌水预测模型”。
3、三种预测涌水的方法对比分析可知,涌水预测的基本前提是充分掌握涌水隧洞工程区地质条件与水理环境等方面的信息,在此基础上,BP神经网络和Mod-flow法相对层次分析与模糊数学法对涌水预测较为客观,层次分析与模糊数学法较为主观,建议优先采用BP神经网络和Mod-flow法,可用层次分析与模糊数学法对其他预测方法进行辅助判断。
4、针对锦屏二级水电站辅引3#施工支洞涌水量预测,分别利用“富水区大埋深高渗压隧洞涌水预测模型”、层次分析与模糊数学和Mod-flow法对锦屏二级水电站辅引3#施工支洞辅(3)0+020～0+050、辅(3)0+170～0+200及辅(3)0+280～0+310三个重点涌水段进行涌水量预测,各种方法的预测值与现场实测涌水量进行了对比分析。研究表明,运用“富水区大埋深高渗压隧洞涌水预测模型”对涌水量的预测值比实际值偏大,误差在10%范围内,能够较为合理地预测隧洞涌水量。
JinPing Ⅱ Hydropower Station is the world's largest hydraulic tunnel engineering now. Because of construction delay and duration shortage, its3auxiliary construction tunnel had been added for more working faces, in order to rescue the TBM and other construction equipments buried by strong rock-burst and ensure project completed on schedule. Obviously, it is essential to the hydraulic tunnel engineering of JinPing Ⅱ Hydropower Station for3#auxiliary construction tunnel had been completed successfully. It is the largest heavy and difficult point of this project that3#auxiliary construction tunnel's water gushing has the characteristics of sudden, big flow, high water pressure, however. Reasonable prediction of its water inrush is one of the largest difficulties in the process of engineering construction.
Take the3#auxiliary construction tunnel of JinPing Ⅱ Hydropower Station as the background, this paper comprehensively use the theoretical analysis, numerical simulation and field test and other methods to study of tunnel water gushing prediction. On the basis of the traditional methods to predict the tunnel gushing in detail,"the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure" is established by BP neural network method. At the same time, the numerical fuzzy mathematics and MODFLOW analysis are used to calculate the water inflow. Take the calculation results to compare to verify reliability of "the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure". The main research contents and conclusions of this paper are as follows:
1. Analyzed the engineering geology and Hydrogeology conditions of the3#auxiliary construction tunnel of JinPing II Hydropower Station, reveals that It is the largest heavy and difficult point of this project that3auxiliary construction tunnel's water gushing has the characteristics of sudden, big flow, high water pressure. The water inrush factors of the3#auxiliary construction tunnel are mainly divided into three major categories of a total of11factors, respectively, the engineering geology conditions, hydrogeology conditions and tunnel conditions. Among them, the engineering geological conditions includ geological structure, the maximum buried depth, the rock level, permeability coefficient and the development of karst a total of5influence factors; Hydrogeology conditions mainly consider the influence of water level, the precipitation and surface water environment; Factors influencing tunnel conditions for tunnel radius, construction conditions and tunnel length.
2. They are the most significant influence for water gushing that Include geological structure, water level, permeability coefficient, the precipitation and tunnel radius, by a synthesis of the influential factors of tunnel water gushing. The geological structure factors, according to the tectonic level using1-100transform from qualitative to quantitative indicators, can be used to calculate the tunnel water inflow. Based on the relationship between these five factors and tunnel water gushing,"the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure" is established by BP neural network method.
3. By comparative analysis of three methods of forecasting water inrush, the basic premise of water inrush prediction is fully mastering inrush geological condition of tunnel engineering and water environment information. On the basis of this, BP neural network and Mod-flow method are more objective for water gushing prediction, and hierarchy analysis and fuzzy mathematics method is more subjective. So, The BP neural network and Mod-flow method are the preferred ways other than hierarchy analysis and fuzzy mathematics method.
4.In the light of the water gushing prediction of the3auxiliary construction tunnel of JinPing II Hydropower Station,"the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure", hierarchy analysis and fuzzy mathematics method and Mod-flow method are taken to predict the water gushing of three key water gushing section of the3auxiliary construction tunnel of JinPing II Hydropower Station, which are the auxiliary (3)0+020～0+050, the auxiliary (3)0+170～0+200, and the auxiliary (3)0+280～0+310. Various methods of value prediction are compared with the measured discharge. The results show that using "the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure" value is larger than the actual value for predicting water inrush, which relative error is in the range of10%. It can reasonably predict the tunnel water inflow.
本文以锦屏二级水电站辅引3“施工支洞工程为背景,综合运用理论分析、数值模拟和现场测试等研究方法对隧洞涌水进行预测研究。在详细介绍隧道涌水预测的传统方法的基础上,利用BP神经网络方法建立“富水区大埋深高渗压隧洞涌水预测模型”,并采用模糊数学和MODFLOW数值分析两种方法对涌水量进行计算,其计算结果用来对比验证“富水区大埋深高渗压隧洞涌水预测模型”可靠性。本文的主要研究内容及结论如下：
1、对锦屏二级水电站辅引3#施工支洞的工程地质和水文地质条件进行了分析,揭示了辅引3“支洞涌水的突发性、大流量、高水压是本工程最大的重难点。影响辅引3“支洞涌水的因素主要分为三大类共计11个因素,分别为工程地质条件、水文地质条件和隧洞工况。其中工程地质条件具体包括地质构造、最大埋深、围岩级别、渗透系数和岩溶发育5个影响因素,水文地质条件主要考虑了水头高度、降水量和地表环境对涌水的影响,隧洞工况的具体因素为隧道半径、施工工况和隧道长度的影响。
2、综合分析隧洞涌水的影响因素得出,地质构造、水头高度、渗透系数、降水量和隧道半径对涌水量的影响最为显著。其中将地质构造因素按地质构造等级采用1-100由定性指标转化为定量指标,能够较好地计算出隧洞涌水量。基于这五种因素与隧洞涌水量之间的关系,构建了基于BP神经网络方法的“富水区大埋深高渗压隧洞涌水预测模型”。
3、三种预测涌水的方法对比分析可知,涌水预测的基本前提是充分掌握涌水隧洞工程区地质条件与水理环境等方面的信息,在此基础上,BP神经网络和Mod-flow法相对层次分析与模糊数学法对涌水预测较为客观,层次分析与模糊数学法较为主观,建议优先采用BP神经网络和Mod-flow法,可用层次分析与模糊数学法对其他预测方法进行辅助判断。
4、针对锦屏二级水电站辅引3#施工支洞涌水量预测,分别利用“富水区大埋深高渗压隧洞涌水预测模型”、层次分析与模糊数学和Mod-flow法对锦屏二级水电站辅引3#施工支洞辅(3)0+020～0+050、辅(3)0+170～0+200及辅(3)0+280～0+310三个重点涌水段进行涌水量预测,各种方法的预测值与现场实测涌水量进行了对比分析。研究表明,运用“富水区大埋深高渗压隧洞涌水预测模型”对涌水量的预测值比实际值偏大,误差在10%范围内,能够较为合理地预测隧洞涌水量。
JinPing Ⅱ Hydropower Station is the world's largest hydraulic tunnel engineering now. Because of construction delay and duration shortage, its3auxiliary construction tunnel had been added for more working faces, in order to rescue the TBM and other construction equipments buried by strong rock-burst and ensure project completed on schedule. Obviously, it is essential to the hydraulic tunnel engineering of JinPing Ⅱ Hydropower Station for3#auxiliary construction tunnel had been completed successfully. It is the largest heavy and difficult point of this project that3#auxiliary construction tunnel's water gushing has the characteristics of sudden, big flow, high water pressure, however. Reasonable prediction of its water inrush is one of the largest difficulties in the process of engineering construction.
Take the3#auxiliary construction tunnel of JinPing Ⅱ Hydropower Station as the background, this paper comprehensively use the theoretical analysis, numerical simulation and field test and other methods to study of tunnel water gushing prediction. On the basis of the traditional methods to predict the tunnel gushing in detail,"the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure" is established by BP neural network method. At the same time, the numerical fuzzy mathematics and MODFLOW analysis are used to calculate the water inflow. Take the calculation results to compare to verify reliability of "the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure". The main research contents and conclusions of this paper are as follows:
1. Analyzed the engineering geology and Hydrogeology conditions of the3#auxiliary construction tunnel of JinPing II Hydropower Station, reveals that It is the largest heavy and difficult point of this project that3auxiliary construction tunnel's water gushing has the characteristics of sudden, big flow, high water pressure. The water inrush factors of the3#auxiliary construction tunnel are mainly divided into three major categories of a total of11factors, respectively, the engineering geology conditions, hydrogeology conditions and tunnel conditions. Among them, the engineering geological conditions includ geological structure, the maximum buried depth, the rock level, permeability coefficient and the development of karst a total of5influence factors; Hydrogeology conditions mainly consider the influence of water level, the precipitation and surface water environment; Factors influencing tunnel conditions for tunnel radius, construction conditions and tunnel length.
2. They are the most significant influence for water gushing that Include geological structure, water level, permeability coefficient, the precipitation and tunnel radius, by a synthesis of the influential factors of tunnel water gushing. The geological structure factors, according to the tectonic level using1-100transform from qualitative to quantitative indicators, can be used to calculate the tunnel water inflow. Based on the relationship between these five factors and tunnel water gushing,"the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure" is established by BP neural network method.
3. By comparative analysis of three methods of forecasting water inrush, the basic premise of water inrush prediction is fully mastering inrush geological condition of tunnel engineering and water environment information. On the basis of this, BP neural network and Mod-flow method are more objective for water gushing prediction, and hierarchy analysis and fuzzy mathematics method is more subjective. So, The BP neural network and Mod-flow method are the preferred ways other than hierarchy analysis and fuzzy mathematics method.
4.In the light of the water gushing prediction of the3auxiliary construction tunnel of JinPing II Hydropower Station,"the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure", hierarchy analysis and fuzzy mathematics method and Mod-flow method are taken to predict the water gushing of three key water gushing section of the3auxiliary construction tunnel of JinPing II Hydropower Station, which are the auxiliary (3)0+020～0+050, the auxiliary (3)0+170～0+200, and the auxiliary (3)0+280～0+310. Various methods of value prediction are compared with the measured discharge. The results show that using "the prediction model of hydraulic tunnel water gushing with water-rich area large buried depth and ultra-high osmotic pressure" value is larger than the actual value for predicting water inrush, which relative error is in the range of10%. It can reasonably predict the tunnel water inflow.
引文
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