基于改进XGBoost算法的深部巷道松动圈智能预测研究

doi:10.11872/j.issn.1005-2518.2024.01.116

摘要/Abstract

摘要：

深部巷道爆破开挖后由于爆炸冲击和原位应力动态卸载耦合作用，围岩内不可避免地产生松动圈，进而影响结构的稳定性，因此对松动圈厚度进行超前预测显得非常重要。依托多座地下矿山松动圈测试作为研究对象，共获取300组有效数据样本。采用4种主流的超参数优化算法，即遗传算法（GA）、灰狼优化算法（GWO）、粒子群优化算法（PSO）和樽海鞘算法（SSA）对XGBoost算法进行优化，并以此构建4种松动圈预测混合模型。采用R²、RMSE、MAE和MAPE指标对预测模型的性能进行对比分析，并开展松动圈厚度参数的敏感性分析。最后，将最优的PSO-XGBoost模型应用于地下矿山运输巷道进行工程验证。结果表明：在群体规模分别为90、70、60和100时，GA-XGBoost、GWO-XGBoost、PSO-XGBoost和SSA-XGBoost模型取得了最佳的预测表现。其中，PSO-XGBoost模型在训练集和测试集中的相关系数分别为0.9244和0.8787，具有最佳的预测性能。相比基准模型（XGBoost、RF、SVM和LightGBM），优化后模型松动圈的预测精度和性能均得到显著提升。巷道当量直径（TD）和围岩地质强度指标（GSI）对松动圈厚度的影响最为显著，垂直主应力也具有明显的影响。优化后的XGBoost模型在实际工程中的应用结果显示实测值与预测值误差在10%以内，PO-XGBoost具有工程应用价值。

关键词: 松动圈, 深部巷道, 机器学习, 人工智能, 地应力, 优化XGBoost算法

Abstract:

During deep tunnelling using drill-and-blast method，excavation damaged zone （EDZ） is inevitably induced in surrounding rocks due to the coupled impacts of blast loading and dynamic initial stress unloading and thus affect the structure stability.Therefore，it is very important to predict EDZ depth before roadways excavation.Relying on the field measurements of EDZ in several underground mines as the research object，300 data samples were collected.Four mainstream hyperparametric optimization algorithms，i.e.，genetic algorithm （GA），gray wolf optimization algorithm（GWO），particle swarm optimization algorithm（PSO），and salp swarm algorithm （SSA），were used to optimize the XGBoost algorithm and to construct four hybrid models for EDZ prediction.Comparative analysis of predictive model performance was conducted in terms of R²，RMSE，MAE and MAPE，along with a sensitivity analysis of the influencing parameters.Finally，the optimal PSO-XGBoost model was applied to a transportation roadway in an underground mine for engineering validation.The results show that the GA-XGBoost，GWO-XGBoost，PSO-XGBoost，and SSA-XGBoost models achieve the best predictive performance with swarm sizes of 90，70，60 and 100，respectively.Among them，the PSO-XGBoost model demonstrates the best predictive performance with correlation coefficients of 0.9244 and 0.8787 in the training and testing sets，respectively.Moreover，compared to bench models（XGBoost，RF，SVM and LightGBM），both the prediction accuracy and stability of the optimized models are improved.The tunnel diameter（TD） and rock mass geological strength index（GSI） have the most significant influence on the loosened zone thickness，along with a noticeable impact from the vertical principal stress.The application results of the optimized XGBoost model in practical engineering show that the error between the measured value and the predicted value is within 10%，indicating that the PO-XGBoost is of significance for engineering application.

Key words: excavation damaged zone（EDZ）, deep tunnels, machine learning, artificial intelligence, in-situ stress, optimized XGBoost algorithm

中图分类号:

TD353

凡兴禹, 王雪林. 基于改进XGBoost算法的深部巷道松动圈智能预测研究[J]. 黄金科学技术, 2024, 32(1): 109-122.

Xingyu FAN, Xuelin WANG. Research on Intelligent Prediction of EDZ Around Deep Tunnels Based on Improved XGBoost Algorithm[J]. Gold Science and Technology, 2024, 32(1): 109-122.

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参数类型	参数名称	符号	单位	取值范围	均值	标准偏差	P值
输入参数	地质强度指标	GSI	-	26.43~77.69	53.66	13.9	0.049
	单轴抗压强度	UCS	MPa	64.91~155.89	101.17	23.63	0.000
	垂直主应力	σ_v	MPa	3.75~40.08	19.35	10.04	0.000
	侧压力系数	λ	-	0.42~2.31	1.50	0.28	0.000
	巷道直径	TD	m	2.80~8.35	4.73	1.01	0.015
	装药系数	PF	kg/m³	1.12~2.00	1.58	0.18	0.000
	抵抗线	B	m	0.50~1.05	0.76	0.10	0.031
	孔间距	HS	m	0.66~1.05	0.86	0.09	0.001
输出参数	松动圈厚度	EDZ	cm	6.7~65.2	41.4	13.2	0.802

粒子数	训练集				测试集
粒子数	MAE	MAPE	RMSE	R²	MAE	MAPE	RMSE	R²
60	7.031	7.321	10.143	0.9014	7.593	7.907	10.954	0.8560
70	7.743	8.331	11.043	0.8802	8.362	8.997	11.926	0.8361
80	7.202	7.954	10.631	0.8892	7.778	8.590	11.481	0.8453
90	6.812	6.943	9.522	0.9073	7.357	7.498	10.284	0.8621
100	8.212	8.994	11.705	0.8723	8.869	9.714	12.641	0.8290

粒子数	训练集				测试集
粒子数	MAE	MAPE	RMSE	R²	MAE	MAPE	RMSE	R²
60	7.572	8.135	12.143	0.8573	8.178	8.786	13.114	0.8233
70	7.483	7.201	10.423	0.8734	8.082	7.777	11.257	0.8384
80	7.384	7.759	11.543	0.8702	7.975	8.380	12.466	0.8357
90	8.312	8.734	12.795	0.8432	8.977	9.433	13.819	0.8098
100	7.943	8.429	12.481	0.8528	8.578	9.103	13.479	0.8191

粒子数	训练集				测试集
粒子数	MAE	MAPE/%	RMSE	R²	MAE	MAPE/%	RMSE	R²
60	5.231	5.513	7.143	0.9244	5.543	5.844	7.572	0.8787
70	5.741	5.734	7.455	0.9212	6.081	6.078	7.902	0.8752
80	5.828	5.945	7.543	0.9189	6.178	6.302	7.996	0.8731
90	6.321	6.712	9.365	0.9023	6.712	7.115	9.927	0.8573
100	5.934	6.294	8.011	0.9123	6.298	6.672	8.492	0.8670

粒子数	训练集				测试集
粒子数	MAE	MAPE	RMSE	R²	MAE	MAPE	RMSE	R²
60	8.213	8.733	12.466	0.8629	8.624	9.170	13.089	0.8208
70	7.426	7.986	10.962	0.8811	7.797	8.385	11.501	0.8374
80	7.132	7.329	10.543	0.8856	7.489	7.695	11.070	0.8416
90	8.012	8.345	11.607	0.8743	8.413	8.762	12.187	0.8313
100	7.087	7.012	9.914	0.8933	7.441	7.363	10.410	0.8496