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.
引文
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