基于WOA-SVM的引水隧洞岩爆烈度评估模型

doi:10.16265/j.cnki.issn1003-3033.2023.09.1143

摘要/Abstract

摘要：

为减少引水隧洞施工过程中岩爆事故的发生,在施工前做好岩爆烈度评估,选取4项评价指标作为岩爆的评价指标,分别为:岩石单轴饱和抗压强度R_c、岩石单轴抗拉强度R_t、围岩最大切向应力σ_θ和岩石弹性能量指数W_et;基于前人研究成果,选取120组岩爆实例作为机器学习样本数据,构建基于鲸鱼优化算法(WOA)优化支持向量机(SVM)的评估模型;并以滇中引水工程香炉山深埋长引水隧洞为例进行岩爆烈度评估的验证。结果表明:机器学习可以较好避开人为因素,完全由数据驱动,WOA-SVM评估精度达到97.22%;经过对比,所构建的模型比PSO-SVM、GA-SVM和WOA-BP神经网络模型在评估精度、泛化程度上均更优;同时,WOA-SVM模型在处理岩爆问题上可以更好地捕捉岩爆等级与指标之间的联系。

关键词: 鲸鱼优化算法(WOA), 支持向量机(SVM), 引水隧洞, 岩爆烈度, 机器学习

Abstract:

This study aims to reduce the occurrence of rockburst accidents in the construction of diversion tunnels. Before construction, rockburst intensity assessment and prevention measures were put forward. Four evaluation indexes were selected as rock burst evaluation indexes in the study, namely, rock uniaxial saturated compressive strength R_c, rock uniaxial tensile strength R_t, maximum tangential stress σ_θ of surrounding rock and rock elastic energy index W_et. According to the research results, 120 groups of rockburst instances were selected as the machine learning sample data, and the WOA-SVM assessment model was proposed. Taking a section of a deep-buried and long diversion tunnel in Xianglushan of the Central Yunnan Water Diversion Project as an example, the intensity assessment results of rockburst intensity were verified. The results show that machine learning can better avoid human factors, and it is completely data-driven with an assessment accuracy of 97.22% for WOA-SVM. By comparison, its assessment accuracy and generalization are better than those of PSO-SVM, GA-SVM and WOA-BP neural network models. The results show that the WOA-SVM model can better capture the link between rockburst levels and indicators for rockburst problems.

Key words: whale optimization algorithm (WOA), support vector machine (SVM), water intake tunnel, rockburst intensity, machine learning

靳春玲, 姬照泰, 贡力, 安祥, 周一. 基于WOA-SVM的引水隧洞岩爆烈度评估模型[J]. 中国安全科学学报, 2023, 33(9): 41-48.

JIN Chunling, JI Zhaotai, GONG Li, AN Xiang, ZHOU Yi. Evaluation model of rockburst intensity of diversion tunnel based on WOA-SVM[J]. China Safety Science Journal, 2023, 33(9): 41-48.

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岩爆等级	类别	分级依据
岩爆等级	类别	破坏方式	破坏过程	破坏程度
Ⅰ级	无岩爆	—	—	—
II级	轻微岩爆	劈裂成板,剪断脱离母体, 产生射落	洞壁表面局部轻微破坏,噼啪声响	不损坏机械设备, 威胁不大
III级	中级岩爆	劈裂—剪断—弹射向洞壁内发展,形成V形坑。洞壁大范围破坏	类似子弹射击声响,个别情况下损坏设备	生产中断
IV级	强烈岩爆	劈裂-剪断-弹射急速发生,并急剧向洞壁深处扩展,持续时间长	有似闷雷巨响,几乎全断面破坏	人材损失严重, 生产停顿

序号	R_c/MPa	R_t/MPa	σ_θ/MPa	W_et	等级
1	118.46	3.509 9	26.061 2	2.89	Ⅱ
2	52	3.7	7.5	1.3	Ⅰ
3	306.58	13.9	105	6.38	Ⅳ
4	175	7.25	62.5	5	Ⅲ
5	120.05	7.41	90	7.3	Ⅱ
6	180	8.3	48.75	5	Ⅲ
︙	︙	︙	︙	︙	︙
115	225.6	17.2	91.3	7.3	Ⅳ
116	180	8.3	48.75	5	Ⅲ
117	196	8.3	89	5	Ⅲ
118	54.2	12.1	34.15	3.17	Ⅰ
119	52	3.7	7.05	1.3	Ⅰ
120	69.7	4.8	9.57	3.8	Ⅰ

算法	基本参数		待优化参数	参数范围
WOA	最大迭代次数	100	惩罚因子c 核函数参数g	(0.001,100) (0.001,100)
	初始化种群数	10
	训练目标最小误差	0.000 01
PSO	最大迭代次数	100	惩罚因子c	(0.001,100)
	种群数量	20	惩罚因子c	(0.001,100)
	局部学习因子c₁	2	核函数参数g	(0.001,100)
	全局学习因子c₂	2	核函数参数g	(0.001,100)
GA	最大迭代次数	100	惩罚因子c	(0.001,100)
	种群数量	20	惩罚因子c	(0.001,100)
	交叉概率	0.5	核函数参数g	(0.001,100)
	变异概率	0.01	核函数参数g	(0.001,100)

算法	c_b	g_b	最佳适应度/%	准确率/%	训练时长/s
WOA-SVM	0.163 8	0.607 4	99.225 7	97.22	32.762
PSO-SVM	0.241 6	2.136 8	98.347 1	88.88	47.165
GA-SVM	1.263 5	5.384	97.819 8	91.67	63.468

序号	R_c/MPa	R_t/MPa	σ_θ/MPa	W_et	等级
1	82.3	5.6	38.5	2.6	Ⅱ
2	119.472	7.8	28.4	6.71	Ⅲ
3	63.4	2.21	17.24	1.6	Ⅰ
4	90.5	9.7	117.52	4.9	Ⅲ
5	90.16	5.4	58.4	3.4	Ⅱ
6	156.4	8.7	106.4	5.12	Ⅲ