基于机器学习的PBM细观参数标定及卸荷岩爆模拟研究

doi:10.16265/j.cnki.issn1003-3033.2026.08.1168

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (8): 260-268.doi: 10.16265/j.cnki.issn1003-3033.2026.08.1168

基于机器学习的PBM细观参数标定及卸荷岩爆模拟研究

喻豪¹(), 王超¹^,²^,³^,^**(), 贺子旺¹, 刘宇¹, 金子浚¹, 亓帅¹

¹ 昆明理工大学国土资源工程学院, 云南昆明 650093
² 自然资源部高原山地地质灾害预报预警与生态保护修复重点实验室, 云南昆明 650093
³ 云南省高原山地地质灾害预报预警与生态保护修复重点实验室, 云南昆明 650093

收稿日期:2026-04-10 修回日期:2026-06-12 出版日期:2026-08-28
通信作者:
**王超(1984—),男,山东济宁人,博士,副教授,主要从事岩石力学及矿山动力灾害防治方面的研究。E-mail:wangchao@kust.edu.cn。
作者简介:
喻豪 (2001—),男,重庆人,硕士研究生,主要研究方向为岩石力学、岩土工程灾害防治。E-mail: yvhao@stu.kust.edu.cn。
基金资助:
国家自然科学基金资助(42367024); 云南省重大科技专项项目(202602AG050013); 云南省专业学位研究生教学案例库建设项目(202416); 昆明理工大学学科交叉研究专项项目(KUST-xk202025003)

Machine learning-based calibration of PBM meso-parameters and numerical simulation of unloading rockburst

Yu Hao¹(), Wang Chao¹^,²^,³^,^**(), He Ziwang¹, Liu Yu¹, Jin Zijun¹, Qi Shuai¹

¹ Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming Yunnan 650093, China
² Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Ministry of Natural Resources of the People's Republic of China, Kunming Yunnan 650093, China
³ Yunnan Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area, Kunming Yunnan 650093, China

Received:2026-04-10 Revised:2026-06-12 Published:2026-08-28

摘要/Abstract

摘要：

为科学准确获取平行黏结模型(PBM)细观参数,首先,采用正交设计、拉丁超立方采样和分层采样构建细观参数样本,结合PFC^2D自动化批量单轴压缩数值试验建立宏-细观参数映射数据库;然后,采用粒子群优化算法(PSO)、麻雀优化算法(SSA)、指数三角优化算法(ETO)分别对随机森林(RF)、反向传播神经网络(BP)、支持向量回归(SVR)和K近邻(KNN)模型开展超参数优化,并构建16种机器学习模型进行比较优选;最后,以锦屏二级水电站大理岩为例,基于优选模型开展细观参数标定及卸荷岩爆数值模拟试验。结果表明:ETO-BP模型预测性能优于其他模型;基于优选模型标定的5种岩样单轴抗压强度、弹性模量和泊松比误差均小于5%;卸荷条件下岩样破坏过程经历平静期、小颗粒弹射期、片状剥离伴颗粒弹射期和全面崩垮期,并呈现颗粒弹射、片状劈裂及块状崩落等破坏形式。

关键词: 平行黏结模型(PBM), 细观参数, 机器学习, 参数标定, 卸荷岩爆

Abstract:

To accurately determine the mesoscopic parameters of the PBM, an integrated parameter calibration framework based on machine learning was developed. First, mesoscopic parameter samples were generated using orthogonal design, Latin hypercube sampling (LHS), and stratified sampling, and a macro-mesoscopic parameter mapping database was established through automated batch uniaxial compression simulations in PFC^2D. Subsequently, particle swarm optimization (PSO), sparrow search algorithm (SSA), and exponential triangle optimization (ETO) algorithms were employed to optimize the hyperparameters of random forest (RF), back propagation(BP) neural Network, support vector regression (SVR), and K-nearest neighbor (KNN) models, and a total of 16 machine learning models were constructed for performance comparison. Finally, the optimal model was applied to calibrate the meso-parameters of marble from the Jinping II Hydropower Station, followed by numerical simulations of unloading rockburst. The results indicate that the ETO-BP model achieves superior predictive performance compared with the other models. The calibration errors of the uniaxial compressive strength, elastic modulus, and Poisson's ratio for five rock specimens are all less than 5%. Under unloading conditions, the simulated rockburst process evolves through four stages, namely the quiet stage, small-particle ejection stage, slab spalling accompanied by particle ejection stage, and overall collapse stage, exhibiting typical failure characteristics including particle ejection, slab splitting, and block collapse.

Key words: parallel bond model (PBM), meso-parameters, machine learning, parameter calibration, unloading rockburst

中图分类号:

X936

喻豪, 王超, 贺子旺, 刘宇, 金子浚, 亓帅. 基于机器学习的PBM细观参数标定及卸荷岩爆模拟研究[J]. 中国安全科学学报, 2026, 36(8): 260-268.

Yu Hao, Wang Chao, He Ziwang, Liu Yu, Jin Zijun, Qi Shuai. Machine learning-based calibration of PBM meso-parameters and numerical simulation of unloading rockburst[J]. China Safety Science Journal, 2026, 36(8): 260-268.

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编号	模型细观参数				宏观参数
编号	${\stackrel{-}{\mathit{E}}}^{\mathrm{*}}$/GPa	${\stackrel{-}{\mathit{\kappa }}}^{\mathrm{*}}$	${\stackrel{-}{\mathit{\sigma }}}_{\mathit{c}}$/MPa	$\stackrel{-}{\mathit{\phi }}$/(°)	R_c/MPa	E/GPa	ν
1	10.00	1.50	10.00	0.00	11.32	11.81	0.179
2	56.67	1.50	40.00	20.00	42.10	47.37	0.143
3	103.03	1.50	70.00	40.00	68.10	69.40	0.123
︙	︙	︙	︙	︙	︙	︙	︙
114	37.68	6.37	11.67	42.03	12.91	24.44	0.331
115	24.75	3.99	53.93	31.82	52.96	19.62	0.326
116	121.19	2.18	89.75	16.85	90.62	71.07	0.161

模型	${\stackrel{-}{\mathit{E}}}^{\mathrm{*}}$		${\stackrel{-}{\mathit{\kappa }}}^{\mathrm{*}}$		${\stackrel{-}{\mathit{\sigma }}}_{\mathit{c}}$
模型	平均误差/%	R²	平均误差/%	R²	平均误差/%	R²
RF	15.48	0.926 9	13.90	0.803 0	7.26	0.973 1
PSO-RF	14.92	0.953 9	17.43	0.838 6	5.28	0.983 0
SSA-RF	14.68	0.951 7	17.43	0.834 2	5.46	0.980 6
ETO-RF	14.16	0.954 0	17.61	0.836 4	5.00	0.981 4
BP	12.89	0.945 5	9.57	0.920 4	5.96	0.976 6
PSO-BP	6.17	0.980 8	6.32	0.950 5	5.34	0.987 4
SSA-BP	8.25	0.973 3	7.62	0.964 9	5.92	0.984 2
ETO-BP	6.13	0.981 5	5.32	0.954 5	3.52	0.992 3
SVR	14.41	0.938 2	12.70	0.906 4	7.50	0.980 8
PSO-SVR	10.49	0.969 3	7.66	0.930 7	5.87	0.990 7
SSA-SVR	9.42	0.958 2	11.60	0.901 1	6.51	0.986 2
ETO-SVR	6.24	0.962 6	6.88	0.971 6	5.30	0.990 6
KNN	14.03	0.838 4	21.18	0.665 3	12.56	0.934 8
PSO-KNN	13.90	0.937 2	16.06	0.794 0	12.34	0.951 0
SSA-KNN	15.63	0.904 2	17.20	0.791 6	10.56	0.978 6
ETO-KNN	14.75	0.905 1	16.09	0.786 7	9.64	0.969 8

来源	岩石类型	R_c/MPa	E/GPa	ν
重庆彭水页岩气区块储层	页岩(1号)^[28]	65.2	26.0	0.237
重庆彭水页岩气区块储层	页岩(2号)^[28]	87.0	24.2	0.248
—	砂岩^[29]	47.31	16.71	0.270
Carrara矿区	大理岩(1号)^[30]	88.3	49.0	0.190
锦屏二级水电站引水隧洞	大理岩(2号)^[31]	199.2	24.13	0.282

岩样	R_c			E			v
岩样	实测值/MPa	模拟值/ MPa	误差/ %	实测值/GPa	模拟值/GPa	误差/ %	实测值	模拟值	误差/ %
1号页岩	65.2	67.89	4.13	26.0	26.30	1.15	0.237	0.232	2.11
2号页岩	87.0	88.23	1.41	24.2	24.88	2.81	0.248	0.241	2.82
砂岩	47.31	47.70	0.82	16.71	17.43	4.31	0.27	0.257	4.81
1号大理岩	88.3	86.58	1.95	49.0	51.07	4.22	0.19	0.189	0.53
2号大理岩	199.2	197.5	0.85	24.13	25.29	4.81	0.282	0.294	4.26

基于机器学习的PBM细观参数标定及卸荷岩爆模拟研究

Machine learning-based calibration of PBM meso-parameters and numerical simulation of unloading rockburst

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