Bearing Capacity Evaluation of Tunnel-Type Anchorage Based on Artificial Intelligent Algorithm
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摘要: 针对隧道锚承载能力评价合理的解析计算公式缺乏、模型试验测试方法耗时费力、数值模拟可靠性不佳的问题，提出了一种人工智能化隧道锚承载能力预测方法. 从隧道锚受力传力过程出发，分析了影响承载能力的因子，确定了承载能力评价指标体系；基于最小二乘支持向量机（least squares support vector machines，LSSVM）强大的学习预测能力和粒子群优化（particle swarm optimization，PSO）算法良好的优化效果，建立了承载能力非线性映射PSO-LSSVM 模型；将收集到的17个隧道锚工程案例作为输入样本对模型进行了训练，获得了核函数参数和惩罚系数的最优组合为(1,500). 将该模型应用于某大桥隧道锚承载能力的预测，预测结果为10.2P （1P为1倍设计荷载）；通过与现场缩尺模型试验和数值模拟方法综合研究确定的承载能力为11.0P对比，结果表明：预测结果略低，但两者结果非常接近，说明该模型的预测结果合理可靠且偏于保守，预测效果较为理想.Abstract: At present, the tunnel-type anchorage is short of reasonable analytical formula for the evaluation of the bearing capacity, the model test is time-consuming and labor-consuming, and its numerical simulation has poor reliability. To handle the above problems, an artificial intelligence method is presented for predicting the bearing capacity of the tunnel-type anchorage. Starting from its force transmission process, the factors influencing the bearing capacity are analyzed and the evaluation index system of bearing capacity has been determined. Then, given the strong learning prediction ability of least squares support vector machines (LSSVM) and excellent performance of particle swarm optimization (PSO), a PSO-LSSVM model with nonlinear mapping of bearing capacity is established. After training the model with 17 cases of the tunnel-type anchorage as input samples, the optimal combination of kernel parameters and penalty coefficients is determined to be (1,500). Finally, the model is used to predict the bearing capacity of a bridge tunnel-type anchorage and the prediction result is determined as 10.2P. The comparison with the bearing capacity result of 11.0P that is determined by the comprehensive study of the scale model test and numerical simulation method, demonstrate that the predicted result is slightly lower but very close to the result of other method. This also shows that the prediction results of the model are reasonable, reliable and conservative, and the prediction effect is desirable.
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图 2 输入样本中预测承载力与试验承载力对比
Figure 2. Comparison of predicted bearing capacity and test one for input samples
图 3 模型试验锚塞体荷载-变形曲线
Figure 3. Load-deformation curves of anchoring concrete-plug in model test
图 4 数值模拟锚塞体荷载-变形曲线
Figure 4. Load-deformation curves of anchoring concrete-plug by numerical simulation
表 1 各隧道锚工程影响因素指标与承载能力汇总表
Table 1. Summary of influencing factors and bearing capacity of each tunnel-type anchorage project
工程编号 项目名称 A1/m2 A2/m2 L/m $\gamma $/(°) $\theta $/(°) E/GPa F′ C′/MPa H1/m H2/m S Y/MN 1 重庆鹅公岩长江大桥 90.0 152.0 40.0 2.0 26 4.0 0.53 0.45 70 90 2.68 780 2 矮寨悬索桥 72.3 128.5 35.0 3.0 45 20.0 1.00 1.20 40 60 1.46 1400 3 四渡河特大桥 96.5 174.9 40.0 3.0 35 9.0 1.00 1.30 100 66 1.89 1596 4 普立特大桥 80.5 150.8 30.0 3.0 42 15.0 1.10 1.10 57 60 2.00 808 5 浙江官山大桥 85.7 228.5 27.0 7.0 40 10.0 1.00 1.00 50 62 1.59 714 6 水布垭清江特大桥 54.3 80.5 18.0 3.5 40 3.5 0.50 0.30 30 36 1.50 24 7 几江长江大桥 89.3 174.9 60.0 2.0 35 1.0 0.60 0.20 68 78 1.11 756 8 金沙江金东大桥 73.1 174.9 40.0 4.0 35 3.0 0.50 0.23 150 90 1.25 960 9 宜昌伍家岗长江大桥 99.7 277.0 45.0 4.5 40 6.0 0.80 0.60 60 90 1.91 1760 10 泸定大渡河特大桥 195.4 281.7 39.2 2.0 36 2.0 0.75 0.40 180 102 1.53 2083 11 太洪长江大桥（初设） 128.5 307.2 40.0 4.5 40 1.0 0.50 0.10 54 75 1.89 112 12 太洪长江大桥 128.5 307.2 58.0 3.0 40 1.0 0.50 0.10 69 93 1.89 145 13 虎跳峡金沙江大桥 99.4 248.8 30.0 5.5 20 3.0 1.15 0.40 110 60 1.71 1309 14 合川渠江景观大桥 89.3 289.2 40.0 6.0 41 1.0 0.70 0.35 75 65 10.00 712 15 合川渠江景观大桥（比选） 89.3 257.9 35.0 6.0 41 1.0 0.70 0.35 72 60 10.00 552 16 坝凌河大桥 97.3 477.6 40.0 9.0 45 10.0 0.83 0.75 95 71 1.33 6000 17 铁路金沙江大桥 145.7 307.2 45.0 3.5 40 6.4 0.74 1.35 68 85 1.81 2790 
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表 2 基岩物理力学参数
Table 2. Physical and mechanical parameters of surrounding rock
岩石名称 天然块体密度ρ/（g•cm−3） E/GPa 抗拉强
度/MPa岩体 砼/岩体 F′ C′/MPa F F′ C′/MPa F 石英长石砂岩 2.3～2.4 2.0～3.0 0.15～0.25 0.70～0.75 0.35～0.40 0.65～0.70 0.75～0.80 0.40～0.45 0.70～0.75
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