Rockburst prediction considering data preprocessing and hole diameter index

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训练轮次	预测准确率/%
训练轮次	SVM	RF	GBDT	Adaboost	XGBoost
1	57.1	57.1	57.1	57.1	53.6
2	67.9	67.9	74.9	67.9	67.9
3	42.9	50.0	50.0	50.0	50.0
4	67.9	71.4	71.4	71.4	71.4
5	71.4	71.4	67.9	71.4	67.9
6	67.9	78.6	75.0	71.4	71.4
7	60.7	67.9	50.0	53.6	53.6
8	75.0	78.6	75.0	78.6	78.6
9	82.1	85.7	82.1	89.3	85.7
10	64.3	67.9	71.4	67.9	67.9
平均值	65.7	69.7	67.5	67.9	66.8

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Rockburst prediction considering data preprocessing and hole diameter index

2023, 45(10): 1987-1994.

Prediction of tunnel rockbursts based on data preprocessing technology considering influences of stress gradient of surrounding rock .

Figures of the Article

Tables of the Article

样本编号	D₀/ m	$ {\sigma _{\theta {\text{max}}}} $/ MPa	$ {\sigma _{\text{c}}} $/ MPa	$ {\sigma _{\text{t}}} $/ MPa	W_et	岩爆烈度
1	9.58	30	88.7	3.7	6.6	3
2	10.83	30	88.7	3.7	6.6	3
3	5.00	90	170.0	11.3	9.0	4
…	…	…	…	…	…	…
304	4.72^*	60	86.0	7.14	2.85	2
305	4.72^*	60	145.2	9.3	3.5	2
306	4.72^*	60	136.8	10.4	2.12	2
注：“*”表示D₀为换算值；第七列中“1”为无岩爆，“2”为弱岩爆，“3”为中等岩爆，“4”为强岩爆。

Dataset of tunnel rockburst

断面
形状椭圆拱形正方形正梯形长方形单边
斜梯

换算系数 1.05 1.1 1.15 1.2 1.2 1.25

Conversion factors
项目估计值估计值95%
置信上限估计值95%
置信下限评价
标准

R² 0.52 0.60 0.42 相关性较强

 R²-tests for imputation dataset
样本
编号 1 2 3 ... 304 305 306

分数 -0.51 -0.55 0.97 ... 0.12 0.08 -0.08

识别 0 0 1 ... 0 0 0

注：“0”表示正常样本，“1”表示离群样本。

Outlier scores for rockburst samples

训练轮次	预测准确率/%
训练轮次	SVM	RF	GBDT	Adaboost	XGBoost
1	57.1	57.1	57.1	57.1	53.6
2	67.9	67.9	74.9	67.9	67.9
3	42.9	50.0	50.0	50.0	50.0
4	67.9	71.4	71.4	71.4	71.4
5	71.4	71.4	67.9	71.4	67.9
6	67.9	78.6	75.0	71.4	71.4
7	60.7	67.9	50.0	53.6	53.6
8	75.0	78.6	75.0	78.6	78.6
9	82.1	85.7	82.1	89.3	85.7
10	64.3	67.9	71.4	67.9	67.9
平均值	65.7	69.7	67.5	67.9	66.8

Rockburst prediction considering data preprocessing and hole diameter index

训练轮次	预测准确率/%
训练轮次	SVM	RF	GBDT	Adaboost	XGBoost
1	72	80	72	72	68
2	52	48	52	60	56
3	56	72	76	72	72
4	48	48	56	48	44
5	52	64	56	56	52
6	60	64	64	64	64
7	68	52	60	68	68
8	64	64	60	52	64
9	72	68	72	68	64
10	60	72	76	60	72
平均值	60.4	63.2	64.4	62.0	62.4

Rockburst prediction considering hole diameter index without data preprocessing

训练轮次	预测准确率/%
训练轮次	SVM	RF	GBDT	Adaboost	XGBoost
1	72	72	72	72	68
2	60	56	60	56	56
3	64	64	56	68	60
4	40	40	48	32	44
5	48	52	52	48	48
6	52	52	52	56	56
7	56	56	64	68	56
8	56	68	64	52	60
9	52	64	68	64	64
10	60	72	64	72	64
平均值	56.0	59.6	60.0	58.8	57.6

Rockburst prediction without data preprocessing and hole diameter index

条件	平均预测准确率/%
条件	SVM	RF	GBDT	Adaboost	XGBoost
数据预处理考虑洞径	65.7	69.7	67.5	67.9	66.8
原始数据集考虑洞径	60.4	63.2	64.4	62.0	62.4
差值	+5.3	+6.5	+3.1	+5.9	+4.4

Comparison of model prediction accuracy with and without data preprocessing

条件	平均预测准确率/%
条件	SVM	RF	GBDT	Adaboost	XGBoost
原始数据集考虑洞径	60.4	63.2	64.4	62.0	62.4
原始数据集不考虑洞径	56.0	59.6	60.0	58.8	57.6
差值	+4.4	+3.6	+4.4	+3.2	+4.8

Comparison of model prediction accuracy with and without hole diameter index

开挖段	主厂房岩爆特征参数
开挖段	D₀/ m	$ {\sigma _{\theta \max }} $/ MPa	σ_c/ MPa	σ_t/ MPa	W_et	实际烈度
中导洞Ⅰ区	11.2^*	90	97.3	6.03	6.6^#	3~4
扩挖Ⅱ区	14.8^*	90	97.3	6.03	5.5^#	3~4
扩挖Ⅲ区	17.7^*	90	97.3	6.03	6.6^#	3~4
注：“*”表示D₀为换算值；“#”表示W_et为插补值。

Rockburst characteristics of underground caverns

开挖段主厂房岩爆倾向性预测

SVM RF GBDT Adaboost XGBoost

中导洞Ⅰ区 3 4 4 4 4

扩挖Ⅱ区 3 3 3 4 3

扩挖Ⅲ区 3 3 4 4 4

Prediction of rockburst for underground caverns

区段	D₀/ m	$ {\sigma _{\theta \max }} $/ MPa	σ_c/ MPa	σ_t/ MPa	W_et	实际烈度
1	11.6^*	32.8	160	6.6	4.6	2
2	11.6^*	44.8	160	6.8	4.9	2
3	11.6^*	50.9	160	7.5	5.3	3
4	11.6^*	44.8	160	6.7	4.8	2
注：“*”表示D₀为换算值。

Rockburst characteristics of Cangling Tunnel

区段 SVM RF GBDT Adaboost XGBoost

1 2 2 2 2 2

2 2 2 2 2 2

3 4 3 3 3 3

4 2 2 2 2 2

Prediction of rockburst for Cangling Tunnel