收稿日期: 2024-03-28
修回日期: 2024-08-08
网络出版日期: 2024-09-19
基金资助
国家自然科学基金面上项目“深部进路开采采场环境感知与爆破参数智能优化”(52374153)
Prediction Method of Rock Cohesion and Internal Friction Angle Based on Ensemble Tree Algorithm
Received date: 2024-03-28
Revised date: 2024-08-08
Online published: 2024-09-19
岩石的黏聚力(c)和内摩擦角(φ)是岩石工程设计及稳定性评价的重要参数,其直接测量需通过多组三轴或剪切试验,耗时多且成本高。基于4个易获取的岩石物理力学参数(纵波波速VP、密度ρ、单轴抗压强度UCS和巴西抗拉强度BTS),构建了用于预测c和φ值的智能模型。共收集了199组含不同岩石类型的数据,采用5种集成树算法开发预测模型,使用贝叶斯优化算法对模型的超参数进行优化。模型评估结果表明:构建的模型均具有较好的预测性能,其中极端随机树模型表现最佳(测试R2>0.97)。敏感性分析表明:VP、UCS和BTS对c值的预测结果影响较大,ρ对φ值的预测结果影响较大。研究成果已成功应用于金川矿区,验证了模型的实用性,开发的图形用户界面便于工程技术人员使用。
李地元 , 杨博 , 刘子达 , 刘永平 , 赵君杰 . 基于集成树算法的岩石黏聚力和内摩擦角预测方法[J]. 黄金科学技术, 2024 , 32(5) : 847 -859 . DOI: 10.11872/j.issn.1005-518.2024.05.086
The cohesion(c) and internal friction angle(φ) of rock are critical parameters in the design and stability assessment of rock engineering projects.Direct measurement of these parameters necessitates condu-cting numerous rock triaxial or shear tests,which are both time-intensive and expensive.This study proposes the development of intelligent models to predict the values of c and φ based on four readily obtainable parameters:P-wave velocity(VP),density(ρ),uniaxial compressive strength(UCS),and Brazilian tensile strength(BTS).A total of 199 datasets containing various rock types were collected and randomly partitioned into a training set(80%) and a test set(2%).The distribution characteristics and correlations among the data were analyzed using scatter plots for data distribution and correlation plots for variables.To address discrepancies in characteristic attributes,such as magnitude and order of magnitude across different input variables,a normalization function was applied.Subsequently,five ensemble trees were utilized to develop predictive models for rock shear strength parameters.Bayesian optimization was employed to optimize the hyperparameters of the models.Concurrently,five-fold cross-validation was implemented during model training.To evaluate the performance of the models,four widely recognized regression metrics were utilized:The coefficient of determination (R2),root mean square error (RMSE),mean absolute error (MAE),and variance accounted for (VAF).Additionally,a ranking system was introduced to provide a comprehensive assessment of the five models.The model evaluation demonstrated that the constructed models exhibited robust predictive performance,with the extremely randomized tree model outperforming others.Specifically,for predicting the value of c,the R2 was 0.993,the RMSE was 0.45,the MAE was 0.309,and the VAF was 99.306%.For predicting the value of φ,the R2 was 0.97,the RMSE was 0.823,the MAE was 0.612,and the VAF was 97.058%.Furthermore,the application of the SHAP interpretation method for sensitivity analysis indicated that VP,UCS,and BTS significantly influenced on the prediction of c,whereas ρ had a substantial impact on the prediction of φ.Finally,rock blocks were collected and processed into samples for physical-mechanical testing to determine the VP,ρ,UCS,BTS,c,and φ values of rocks at various locations within the Jinchuan Ⅱ and Ⅳ mining areas in China.The model was effectively utilized to predict the c and φ values for rocks in the Jinchuan mining area,thereby validating its practicability.Furthermore,a graphical user interface was developed to facilitate ease of use for engineers and technicians in the field.
null | Armaghani D J, Hajihassani M, Bejarbaneh B Y,et al,2014.Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network[J].Measurement,55:487-498. |
null | Breiman L,1996.Bagging predictors[J].Machine Learning,24(2):123-140. |
null | Breiman L,2001.Random forests[J].Machine Learning,45(1):5-32. |
null | Chen Shaojie, Feng Fan, Li Xibing,et al,2023.Research progress and key issues of laboratory test and numerical simulation for labbing failure in hard rock under complex mining conditions[J].Journal of China University of Mining and Technology,52(5):868-888. |
null | Cui Jiaxu, Yang Bo,2018.Survey on Bayesian optimization methodology and applications[J].Journal of Software,29(10):3068-3090. |
null | Deng Hongwei, Luo Liang,2023.PPV prediction model based on random forest optimized by SMA algorithm[J].Gold Science and Technology,31(4):624-634. |
null | Fang Boyang, Zhao Guoyan, Ma Ju,et al,2023.Prediction study on loosening ring of surrounding rock around roadways using the optimized ensemble learning algorithms based on adaboost [J].Gold Science and Technology,31(3):497-506. |
null | Gao Ansen, Qi Chengzhi, Luo Yi,et al,2022.Study on time-dependent damage creep model of rock shear failure under compression[J].Metal Mine,51(9):81-86. |
null | Geurts P, Ernst D, Wehenkel L,2006.Extremely randomized trees[J].Machine Learning,63(1):3-42. |
null | Hajdarwish A, Shakoor A, Wells N A,2013.Investigating statistical relationships among clay mineralogy,index engineering properties,and shear strength parameters of mudrocks[J].Engineering Geology,159:45-58. |
null | Kainthola A, Singh P K, Verma D,et al,2015.Prediction of strength parameters of himalayan rocks:A statistical and ANFIS approach[J].Geotechnical and Geological Engineering,33(5):1255-1278. |
null | Li D, Liu Z, Xiao P,et al,2022.Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization[J].Underground Space,7(5):833-846. |
null | Liu Qiang, Li Xibing, Liang Weizhang,2018.PCA-RF model for the classification of rock mass quality and its application[J].Gold Science and Technology,26(1):49-55. |
null | Lundberg S M, Lee S I,2017.A unified approach to interpreting model predictions[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.Red Hook,NY,USA:Curran Associates Inc. |
null | Mahmoodzadeh A, Mohammadi M, Salim S G,et al,2022.Machine learning techniques to predict rock strength parameters[J].Rock Mechanics and Rock Engineering,55(3):1721-1741. |
null | Prokhorenkova L, Gusev G, Vorobev A,et al,2018.CatBoost:Unbiased boosting with categorical features[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems.Red Hook,NY,USA:Curran Associates Inc. |
null | Shahani N M, Ullah B, Shah K S,et al,2022.Predicting angle of internal friction and cohesion of rocks based on machine learning algorithms[J].Mathematics,10(20):3875. |
null | Sharma S, Ahmed S, Naseem M,et al,2021.A survey on applications of artificial intelligence for pre-parametric project cost and soil shear-strength estimation in construction and geotechnical engineering[J].Sensors,21(2):463. |
null | Shen J, Jimenez R,2018.Predicting the shear strength parameters of sandstone using genetic programming[J].Bulletin of Engineering Geology and the Environment,77(4):1647-1662. |
null | Tan Wenkan, Hu Nanyan, Ye Yicheng,et al,2022.Rockburst intensity classification prediction based on four ensemble learning[J].Chinese Journal of Rock Mechanics and Engineering,41(Supp.2):3250-3259. |
null | Tang Jiejun, Wang Yixian,2007.An optimized method for determining shear strength parameters of hard rock[J].Journal of Central South University of Forestry and Technology,(3):95-100. |
null | Tsang L, He B, Rashid A S A,et al,2022.Predicting the young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques[J].Applied Sciences,12(20):10258. |
null | Xie Zhiying, Xu Ke, Lu Yifan,et al,2024.Influence of single fissure position on mechanical deformation and failure modes of composite rock specimens under triaxial compression[J].Gold Science and Technology,32(3):458-469. |
null | Xiu Zhanguo, Wang Shuhong, Wang Feili,et al,2021.Comparative experimental study on the shear behavior of cemented paste backfill and surrounding rock-backfill interface[J].Chinese Journal of Rock Mechanics and Engineering,40(8):1628-1642. |
null | Zhang C, Li D, Wang C,et al,2022.Effect of confining pressure on shear fracture behavior and surface morphology of granite by the short core in compression test[J].Theoretical and Applied Fracture Mechanics,121:103506. |
null | Zhao Kui, Deng Xiaoping, Zeng Peng,et al,2013.Calculation methods of parameters of shear strength by triaxial compression test[J].Mining Research and Development,33(4):27-29,104. |
null | Zhou J, Qiu Y, Zhu S,et al,2021.Estimation of the TBM adva-nce rate under hard rock conditions using XGBoost and Ba-yesian optimization[J].Underground Space,6(5):506-515. |
null | 陈绍杰,冯帆,李夕兵,等,2023.复杂开采条件与深部硬岩板裂化破坏试验与模拟研究进展和关键问题[J].中国矿业大学学报,52(5):868-888. |
null | 崔佳旭,杨博,2018.贝叶斯优化方法和应用综述[J].软件学报,29(10):3068-3090. |
null | 邓红卫,罗亮,2023.基于SMA算法优化随机森林的PPV预测模型[J].黄金科学技术,31(4):624-634. |
null | 方博扬,赵国彦,马举,等,2023.Adaboost集成学习优化的巷道围岩松动圈预测研究[J].黄金科学技术,31(3):497-506. |
null | 高安森,戚承志,罗伊,等,2022.轴向荷载下岩石剪切破坏时效损伤蠕变模型研究[J].金属矿山,51(9):81-86. |
null | 刘强,李夕兵,梁伟章,2018.岩体质量分类的PCA-RF模型及应用[J].黄金科学技术,26(1):49-55. |
null | 谭文侃,胡南燕,叶义成,等,2022.基于四大集成学习的岩爆烈度分级预测[J].岩石力学与工程学报,41(增2):3250-3259. |
null | 唐杰军,汪亦显,2007.硬岩抗剪强度参数的最优化确定法[J].中南林业科技大学学报,(3):95-100. |
null | 谢志英,许可,陆逸帆,等,2024.三轴压缩下单裂隙位置对复合岩样力学变形与破坏模式的影响[J].黄金科学技术,32(3):458-469. |
null | 修占国,王述红,王斐笠,等,2021.充填体和围岩—充填体界面剪切特性对比试验研究[J].岩石力学与工程学报,40(8):1628-1642. |
null | 赵奎,邓晓平,曾鹏,等,2013.三轴试验确定抗剪强度参数的计算方法[J].矿业研究与开发,33(4):27-29,104. |
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