Adaboost集成学习优化的巷道围岩松动圈预测研究

doi:10.11872/j.issn.1005-2518.2023.03.122

Abstract

Abstract:

In order to improve the prediction accuracy of loose zone of excavation damaged zone around roadways and provide more scientific guidance for surrounding rock support and ground pressure management，a new prediction method was proposed.The improved Adaboost regression algorithm was used to integrate and optimize three machine learning algorithms，the optimal value of the error rate threshold was found to achieve the global optimal integration of Adaboost.The grid search was used to optimize the hyperparameters of BP，SVM and RF，and the regression prediction models of BP-Adaboost，SVM-Adaboost and RF-Adaboost were established.The results show that the prediction performance of BP-Adaboost is the best，it had the lowest error rate at 7.65 percent.The verification analysis was carried out based on the test example of excavation damaged zone around roadway，the results show that the mean relative error is 4.15%.Therefore，the model proposed in this paper can provide reference for the excavation damaged zone around roadway and meet the needs of engineering applications.

Key words: loosening ring of surrounding rock, grid search, Adaboost algorithm, Back Propagation Neural Network（BPNN）, Support Vector Machine（SVM）, Random Forest（RF）

CLC Number:

TD322

Boyang FANG,Guoyan ZHAO,Ju MA,Liqiang CHEN,Zheng JIAN. Prediction Study on Loosening Ring of Surrounding Rock Around Roadways Using the Optimized Ensemble Learning Algorithms Based on Adaboost[J].Gold Science and Technology, 2023, 31(3): 497-506.

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样本编号	巷道埋深/m	巷道跨度/m	掘进断面面积/m²	单轴抗压强度/MPa	节理发育程度	厚度/m	样本编号	巷道埋深/m	巷道跨度/m	掘进断面面积/m²	单轴抗压强度/MPa	节理发育程度	厚度/m
1	362	2.6	6.8	62.4	2	0.6	34	689	3.0	7.6	15.1	4	1.8
2	660	4.4	14.6	12.5	5	2.2	35	450	3.0	7.6	11.2	3	1.2
3	384	3.5	11.5	8.5	3	1.2	36	410	3.6	11.7	13.3	4	1.4
4	150	3.6	11.7	14.6	2	0.6	37	348	3.2	9.2	7.5	3	1.2
5	178	2.6	6.4	23.8	3	1.2	38	357	3.2	8.5	10.5	3	1.1
6	510	3.2	7.3	12.6	4	1.6	39	273	2.6	6.6	15.9	2	0.8
7	420	3.6	10.3	14.3	3	1.2	40	280	2.8	7.1	12.7	2	0.8
8	450	3.4	4.8	9.1	5	2.0	41	321	2.6	6.6	15.9	2	0.8
9	236	3.0	7.5	14.3	3	1.2	42	665	4.4	14.6	10.9	4	1.7
10	470	4.0	12.6	10.1	5	2.2	43	350	3.2	8.5	10.5	3	1.2
11	467	3.4	8.2	11.2	3	1.0	44	321	2.6	6.6	9.2	3	1.2
12	490	3.7	8.9	12.5	4	1.8	45	340	3.0	7.6	73.6	2	0.8
13	450	3.6	10.8	13.3	4	1.6	46	470	3.6	11.2	9.1	5	2.1
14	224	3.4	8.2	11.2	3	1.0	47	231	3.0	7.5	18.3	2	0.7
15	460	3.2	9.7	101.6	1	0.4	48	125	3.4	9.8	13.3	3	1.0
16	373	2.5	6.3	14.6	2	0.9	49	296	3.4	7.8	22.4	4	1.4
17	310	2.8	7.1	13.8	3	1.2	50	436	2.8	7.2	15.2	3	1.2
18	125	2.8	7.1	13.3	2	0.7	51	343	3.2	9.6	32.2	2	0.7
19	392	2.8	6.9	14.5	2	0.8	52	525	3.2	7.3	15.8	4	1.6
20	249	3.4	8.2	16.8	3	1.0	53	264	3.2	9.2	11.2	3	1.1
21	140	3.6	10.3	13.4	2	0.5	54	292	3.4	7.8	12.5	4	1.4
22	345	3.0	7.6	65.0	2	0.7	55	362	2.6	6.8	58.0	2	0.8
23	315	2.8	7.1	11.2	3	1.1	56	180	2.8	7.1	110.2	1	0.3
24	550	3.4	9.4	12.5	5	2.1	57	362	2.6	6.8	62.4	2	0.6
25	410	3.2	7.2	13.3	3	1.1	58	340	3.2	9.6	32.2	2	0.7
26	420	3.2	9.2	9.1	4	1.7	59	467	3.4	9.6	10.1	4	1.8
27	340	3.2	9.2	19.8	3	1.3	60	268	3.0	7.5	12.0	3	1.4
28	340	3.2	9.6	32.2	2	0.7	61	236	3.0	7.5	14.3	3	1.2
29	420	3.7	8.9	9.1	4	1.4	62	321	2.6	6.6	13.3	3	1.1
30	370	3.5	8.3	10.5	3	1.0	63	97	3.2	8.8	11.2	4	1.2
31	428	3.6	11.7	16.5	3	1.2	64	322	3.4	7.7	14.3	4	1.5
32	465	4.0	12.6	9.5	4	1.6	65	293	3.5	8.3	11.9	3	1.1
33	403	2.9	7.2	12.6	3	1.3	66	450	3.4	7.8	9.1	5	2.0

模型	误差	MSE	R²	MAE/%
BPNN	-	0.01527	0.923	9.83
BP_Ada	0.50	0. 01376	0.931	8.05
BP_Ada	0.60	0.01273	0.936	7.65
SVM	-	0.01412	0.928	10.81
SVM_Ada	0.50	0.01344	0.932	10.29
SVM_Ada	0.59	0.01318	0.933	9.58
RF	-	0.02325	0.882	15.36
RF_Ada	0.33	0.02473	0.877	15.13
RF_Ada	0.50	0.02473	0.877	15.13

Prediction Study on Loosening Ring of Surrounding Rock Around Roadways Using the Optimized Ensemble Learning Algorithms Based on Adaboost

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