基于集成学习与贝叶斯优化的岩石抗压强度预测  

Prediction for Rock Compressive Strength Based on Ensemble Learning and Bayesian Optimization

作　　者：吴禄源 李建会 马丹 王自法[3,4] 张建伟 袁超[5] 冯义[6] 李辉[6] Wu Luyuan;Li Jianhui;Ma Dan;Wang Zifa;Zhang Jianwei;Yuan Chao;Feng Yi;Li Hui(School of Civil Engineering and Architecture,Henan University,Kaifeng 475004,China;State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology,Xuzhou 221116,China;Institute of Engineering Mechanics,China Earthquake Administration,Harbin 150080,China;Institute of Disaster Prevention and Mitigation of China Earthquake Engineering(Guangdong),Shaoguan 512026,China;College of Architecture and Civil Engineering,Xi’an University of Science and Technology,Xi’an 710054,China;China Railway First Survey and Design Institute Group Co.Ltd.,Xi’an 710043,China)

机构地区：[1]河南大学土木建筑学院,河南开封475004 [2]中国矿业大学煤炭资源与安全开采国家重点实验室,江苏徐州221116 [3]中国地震局工程力学研究所,黑龙江哈尔滨150080 [4]中震科建(广东)防灾减灾研究院,广东韶关512026 [5]西安科技大学建筑与土木学院,陕西西安710054 [6]中铁第一勘察设计院集团有限公司,陕西西安710043

出　　处：《地球科学》2023年第5期1686-1695,共10页Earth Science

基　　金：国家自然科学基金项目(Nos.41977238,51978634);河南省自然科学基金青年基金项目(No.232300421331);河南省高等学校重点科研项目(No.23A440005).

摘　　要：岩石抗压强度是评估岩体工程稳定性的重要力学参数,传统统计回归方法对于岩石抗压强度预测存在一定的局限性.为此,提出了一种利用简单岩石力学参数实现岩石抗压强度智能预测的方法,首先收集了620组含不同类型岩石的三轴试验数据,然后分别采用随机森林(Random Forest,RF)、极限梯度提升树(XGBoost,XGB)和轻量梯度提升机(LightGBM,LGB)3种主流的集成学习算法建立了岩石抗压强度预测模型,使用贝叶斯优化算法在模型训练过程中进行超参数优化,最后利用决定系数(R2)、平均绝对百分比误差(MAPE)和均方根误差(RMSE)对优化后模型的泛化能力进行了综合评估和对比分析.此外,利用LGB模型对输入特征进行重要性分析,以评估不同输入特征对模型泛化性能的影响重要程度.研究结果表明:所建立的3种模型对岩石抗压强度均取得了较好的预测结果,其中LGB模型泛化性能优于另外两种模型(R2=0.978,RMSE=5.58,MAPE=9.70%),且运行耗时相对最少.弹性模量(E)、围压(σ_(3))和密度(ρ)对模型的泛化性能影响较大,泊松比(v)影响较小.提出的预测模型对于岩石抗压强度预测有良好的适用性,为机器学习与岩土工程的结合提供了新的思路.Rock compressive strength is an important mechanical parameter to evaluate the stability of rock mass engineering.The traditional statistical regression method has some limitations on the prediction of rock compressive strength.To this end,in this paper it proposes a method for intelligent prediction of rock compressive strength using simple rock mechanics parameters.Firstly,620 sets of triaxial test data containing different types of rocks were collected and preprocessed.Then,three main stream ensemble learning algorithms,Random forest,XGBoost and LightGBM,were used to establish a rock compressive strength prediction model,and Bayesian optimization algorithm was used to optimize the hyperparameters during model training.Finally,the coefficient of determination(R2),mean absolute percentage error(MAPE)and root mean square error(RMSE)were used to evaluate and compare the generalization ability of the optimized model.In addition,the importance of input features was analyzed by LGB model,to evaluate the importance of input features on the generalization ability of the model.The results show that the three models have achieved good prediction results for rock compressive strength.And the generalization ability of the LGB model is slightly better than that of the other two models(R2=0.978,RMSE=5.58,MAPE=9.70%),and the running time is relatively minimum.Elastic modulus(E),confining pressure(σ_(3))and density(ρ)have great influence on generalization ability of model,while Poisson’s ratio(v)has little influence.The prediction model has good applicability to rock strength prediction,and provides a new idea for the combination of machine learning and geotechnical engineering.

关 键 词：岩石强度 集成学习 贝叶斯优化 随机森林 极限梯度提升树 轻量梯度提升机 工程地质. 

分 类 号：P64[天文地球—地质矿产勘探]