基于CatBoost-NSGA-III算法的盾构姿态预测与优化

doi:10.16265/j.cnki.issn1003-3033.2024.08.1360

摘要/Abstract

摘要：

为解决盾构掘进过程中因盾构前倾变形、蛇形、轴线偏离及纠偏等影响施工安全性与高效性的问题,提出一种将类别型特征梯度提升(CatBoost)与第三代非支配排序遗传算法(NSGA-Ⅲ)相结合的盾构姿态多目标优化方法;以贵阳地铁为例,选取22个影响因素作为输入参数,利用CatBoost算法建立输入参数与盾构姿态之间的非线性映射函数关系,采用随机森林(RF)算法评价输入参数的重要性;以盾构姿态绝对值最小化为目标,构建CatBoost-NSGA-Ⅲ多目标优化模型,并通过案例分析验证所提方法的适用性和有效性。结果表明:采用CatBoost算法训练工程实测数据得到的预测模型具有较高的精度,5个盾构姿态目标的R²范围为0.916~0.943;所研发的CatBoost-NSGA-Ⅲ盾构姿态多目标优化方法,可使盾构姿态得到显著优化,整体改进的平均值为53.34%。

关键词: 类别型特征梯度提升(CatBoost), 第三代非支配排序遗传算法(NSGA-Ⅲ), 盾构姿态, 多目标优化, 重要性排序

Abstract:

To solve the problems such as forward tilt deformation, serpentine shape, axis deviation and correction during shield tunneling, which affected the safety and efficiency of shield construction, a multi-objective optimization method of shield attitude combining CatBoost and NSGA-Ⅲ was proposed. Taking Guiyang Metro as the background, 22 influencing factors were selected as input parameters, and the nonlinear mapping function relationship between input parameters and shield attitude was established by using CatBoost algorithm. The importance of input parameters was evaluated by random forest (RF) algorithm. A CatBoost-NSGA-Ⅲ multi-objective optimization model was established to minimize the absolute value of the shield attitude, and the applicability and effectiveness of the proposed method were verified by a case study. The results show that the prediction model obtained by using CatBoost algorithm to train engineering measured data has high accuracy, and the R² range of 5 shield attitude targets is 0.916-0.943. By using the CatBoost-NSGA-Ⅲ multi-objective optimization method, the attitude of the shield can be optimized significantly, and the average value of the overall improvement is 53.34%.

Key words: categorical boosting (CatBoost), non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ), shield attitude, multi-objective optimization, importance ranking

中图分类号:

X948
U455

吴贤国, 刘俊, 曹源, 雷宇, 李士范, 覃亚伟. 基于CatBoost-NSGA-III算法的盾构姿态预测与优化[J]. 中国安全科学学报, 2024, 34(8): 69-77.

WU Xianguo, LIU Jun, CAO Yuan, LEI Yu, LI Shifan, QIN Yawei. Shield attitude prediction and optimization based on CatBoost-NSGA-III algorithm[J]. China Safety Science Journal, 2024, 34(8): 69-77.

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参考文献 13

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参数类型	变量	数据
参数类型	变量	最小值	最大值	平均值
输入	土仓压力-上 x₁/MPa	0.07	0.21	0.14
	土仓压力-下x₂/MPa	0.04	0.53	0.22
	土仓压力-左 x₃/MPa	0.05	0.29	0.15
	土仓压力-右 x₄/MPa	0.03	0.28	0.15
	螺旋机转速 x₅/ (r·min^-1)	0.91	22.9	12.02
	千斤顶推力-上 x₆/kN	3.69	209.02	77.77
	千斤顶推力-下 x₇/kN	60.19	212.56	123.24
	千斤顶推力-左 x₈/kN	28.84	269.35	132.91
	千斤顶推力-右 x₉/kN	42.20	235.88	109.87
	刀盘转速 x₁₀/(r·min^-1)	1.21	2.04	1.66
	刀盘扭矩 x₁₁/(kN·m)	719.91	2 998.29	2 058.51
	掘进速度 x₁₂/ (mm·min^-1)	7.53	70.29	40.09
	注浆压力 x₁₃/MPa	0.12	0.31	0.25
	总推力 x₁₄/kN	5 441.28	19 086.34	12 249.35
	铰接行程 x₁₅/m	0.87	38.40	13.71
	铰接偏差-水平 x₁₆/mm	1.34	86.68	24.85
	铰接偏差-垂直 x₁₇/mm	0	59.85	21.15
	盾尾间隙-上 x₁₈/mm	29.03	79.03	63.81
	盾尾间隙-下 x₁₉/mm	33.81	83.7	67.81
	盾尾间隙-左 x₂₀/mm	27.03	83.19	67.37
	盾尾间隙-右 x₂₁/mm	11.49	189.87	63.33
	切口行程 x₂₂/m	1.4	1.54	1.5
	泡沫剂 x₂₃/L	25.19	157.79	47.96
输出	切口水平方向位移 f₁/mm	-39.53	32.03	-0.58
	切口垂直方向位移 f₂/mm	-49.47	48.61	-0.32
	尾部水平方向位移 f₃/mm	-52.86	42.36	-25.78
	尾部垂直方向位移 f₄/mm	-46.66	51.94	16.15
	俯仰角 f₅/(°)	-27.11	26.01	-1.45

预测目标	l2_leaf_reg	learning_rate	max_depth
f₁/mm	9	0.026 57	8
f₂/mm	7	0.399 37	8
f₃/mm	4	0.167 00	6
f₄/mm	6	0.034 56	8
f₅/(°)	3	0.065 53	8

折叠序号	f₁/mm			f₂/mm			f₃/mm			f₄/mm			f₅/(°)
折叠序号	R²	RMSE	MAE	R²	RMSE	MAE	R²	RMSE	MAE	R²	RMSE	MAE	R²	RMSE	MAE
1	0.934	3.741	2.922	0.943	3.418	2.705	0.917	3.594	2.827	0.915	4.372	3.429	0.909	0.986	0.833
2	0.943	3.456	2.717	0.949	3.247	2.575	0.923	3.466	2.739	0.920	4.217	3.291	0.920	0.922	0.756
3	0.949	3.292	2.611	0.950	3.216	2.555	0.925	3.431	2.721	0.927	4.037	3.145	0.915	0.953	0.781
4	0.925	3.976	3.198	0.939	3.546	2.851	0.914	3.674	2.876	0.911	4.473	3.555	0.919	0.930	0.766
5	0.922	4.069	3.297	0.933	3.715	3.051	0.912	3.576	2.906	0.912	4.442	3.521	0.920	0.926	0.760
平均值	0.934	3.707	2.950	0.943	3.428	2.748	0.918	3.576	2.814	0.917	4.308	3.388	0.916	0.943	0.779

算法	f₁/mm			f₂/mm			f₃/mm			f₄/mm			f₅/(°)
算法	R²	RMSE	MAE	R²	RMSE	MAE	R²	RMSE	MAE	R²	RMSE	MAE	R²	RMSE	MAE
CatBoost	0.934	3.707	2.950	0.943	3.428	2.748	0.918	3.576	2.814	0.917	4.308	3.388	0.916	0.943	0.779
XGBoost	0.918	4.155	3.321	0.928	3.847	3.257	0.903	3.888	3.514	0.903	4.667	3.757	0.901	1.024	0.820
LightGBM	0.926	3.933	3.047	0.937	3.582	2.872	0.913	3.683	3.092	0.913	4.405	3.530	0.911	0.972	0.794
GRU	0.922	4.047	3.154	0.933	3.699	2.940	0.908	3.787	3.196	0.908	4.546	3.659	0.907	0.994	0.796
LSTM	0.920	4.095	3.228	0.931	3.773	3.094	0.907	3.813	3.263	0.906	4.583	3.697	0.903	1.014	0.813

场景编号	调整参数
1	x₁~x₄,x₆~x₉
2	x₁~x₄,x₆~x₉,x₁₂
3	x₁~x₄,x₆~x₉,x₁₂,x₁₁