Prediction Analysis of Settlement of Existing Road Under Shield Tunneling Based on IPSO-SVR

doi:10.12141/j.issn.1000-565X.220553

Abstract

Abstract:

At present, there are relatively few studies on the settlement prediction of shield tunnels crossing existing roads in parallel. To accurately predict the influence of different factors on the settlement of existing parallel roads in the shield process, this study proposed a support vector regression prediction model based on the improved particle swarm optimization (IPSO-SVR), which was applied to the surface road settlement prediction of actual subway tunnel engineering. Based on the Changchun Metro Line 6 under-crossing Feiyue Road section project, combined with the shield boring parameters, formation information and road settlement monitoring during the construction of the shield tunnel, this paper used the grid search method of libsvm to reduce the range of hyperparameters, and improved the change of inertia weight and acceleration factor in particle swarm optimization algorithm by combining the nonlinear decline strategy. Finally, IPSO-SVR prediction model was established to achieve the settlement prediction of subsequent sections in the interval. The results show that, comparing the changes of the objective function (mean square error) in the grid search method and in the conventional particle swarm optimization training, the convergence speed of the improved particle swarm optimization is greatly improved; the convergence effect of the objective function is better, and the minimum value is reduced by nearly 15%. The mean absolute error (MAE) of IPSO-SVR prediction of road settlement proposed in this paper is 0.287, the fitting coefficient R² is 0.884, and the average relative error is only 8.91%, which has better performance than back propagation (BP) neural network, support vector regression (SVR) and particle swarm optimization support vector regression (PSO-SVR) prediction models. It can be seen that IPSO-SVR has high precision for nonlinear prediction of multi-factor coupling under complex conditions, and its prediction method is feasible and generalizable. IPSO-SVR can provide a reliable basis for effective control of road settlement and is of great significance for ensuring the normal operation of roads and the safety of shield construction.

Key words: shield tunnel, settlement prediction, particle swarm optimization, support vector regression, multi-factor coupling

CLC Number:

U455.43

WEI Haibin, WEI Dongsheng, JIANG Boyu, et al. Prediction Analysis of Settlement of Existing Road Under Shield Tunneling Based on IPSO-SVR[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(6): 62-71.

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类别	注浆压力	同步注浆量	盾构推力	土方重	刀盘扭矩	贯入度	土仓压力
一阶影响指数	0.054 3	0.082 7	-0.198 9	0.026 5	-0.468 8	-0.296 3	0.328 3
全局影响指数	0.045 9	0.186 4	0.120 3	0.332 4	0.161 2	0.079 2	0.162 1

地层类别	天然密度/（g·cm^-3）	粘聚力/kPa	内摩擦角/（°）	侧压力系数	泊松比	孔隙比
粉质粘土	1.96	29	20	0.425	0.30	0.738
全风化泥岩	2.03	27	22	0.390	0.25	0.609
强风化泥岩	—	43	23	0.360	—	—
中风化泥岩	2.08	52	25	0.330	0.21	0.713

样本环号	深跨比	地下水埋深/m	粘聚力/kPa	内摩擦角/（°）	侧压力系数	同步注浆量/m³	盾构推力/kN	刀盘扭矩/（kN·m）	土方重/kN	土仓压力/kPa	最大沉降值/mm
250	1.70	3.25	47.12	20.3	0.39	6.4	13 000	3 600	31.83	110	6.76
255	1.70	3.28	47.82	22.5	0.37	7.2	12 600	3 800	33.63	110	6.78
260	1.69	3.28	48.72	22.4	0.33	7.0	12 000	3 100	34.41	110	6.99
265	1.69	3.39	46.90	21.4	0.36	7.0	10 000	3 100	34.04	120	6.84
270	1.68	3.40	44.19	20.5	0.38	6.8	12 000	3 900	33.45	110	6.77
︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙
525	1.91	5.12	55.26	24.4	0.32	7.0	8 000	3 100	33.17	130	3.57
530	1.92	5.36	55.44	25.2	0.37	6.4	9 000	3 000	33.08	120	4.99
535	1.93	5.41	53.69	21.3	0.29	7.0	7 500	3 100	32.55	130	3.23
540	1.94	5.44	56.84	24.2	0.36	6.4	7 200	3 300	32.70	120	3.93
545	1.96	5.50	54.16	21.0	0.32	7.0	7 000	3 100	33.14	130	3.46

模型类别	超参数
BP	l=0.1，h=1，s=10
SVR	C=1，γ=0.1
PSO-SVR	C=2.034 9，γ=0.140 3

预测模型类型	平均相对误差/%	最大相对误差/%
BP	19.42	44.67
SVR	11.14	23.93
PSO-SVR	10.09	17.79
IPSO-SVR	8.91	14.69