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Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 275-304页

作者： Junjie Zhao diyuan li Jingtai Jiang Pingkuang Luo School of Resources and Safety Engineering Central South UniversityChangsha410083China

Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.

关键词： Uniaxial compression strength strength prediction machine learning optimization algorithm

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Numerical simulation of microwave-induced cracking and melting of granite based on mineral microscopic models

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International Journal of Minerals,Metallurgy and Materials 2024年第7期31卷 1512-1524页

作者： Xiaoli Su diyuan li Junjie Zhao Mimi Wang Xing Su Aohui Zhou School of Resources and Safety Engineering Central South UniversityChangsha 410083China

This study introduces a coupled electromagnetic–thermal–mechanical model to reveal the mechanisms of microcracking and mineral melting of polymineralic rocks under microwave radiation.Experimental tests validate the rationality of the proposed model.Embedding microscopic mineral sections into the granite model for simulation shows that uneven temperature gradients create distinct molten,porous,and nonmolten zones on the fracture surface.Moreover,the varying thermal expansion coefficients and Young's moduli among the minerals induce significant thermal stress at the mineral boundaries.Quartz and biotite with higher thermal expansion coefficients are subjected to compression,whereas plagioclase with smaller coefficients experiences tensile stress.In the molten zone,quartz undergoes transgranular cracking due to theα–βphase transition.The local high temperatures also induce melting phase transitions in biotite and feldspar.This numerical study provides new insights into the distribution of thermal stress and mineral phase changes in rocks under microwave irradiation.

关键词： microwave numerical modeling microcracking phase change granite

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Dynamic mechanical properties and wave propagation of composite rock-mortar specimens based on SHPB tests

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International Journal of Mining Science and Technology 2022年第4期32卷 793-806页

作者： Zhenyu Han diyuan li Xibing li School of Resources and Safety Engineering Central South UniversityChangsha 410083China School of Civil Engineering Southeast UniversityNanjing 210096China Department of Civil Engineering Monash UniversityMelbourneVictoria 3800Australia

Filled inclusions in rock discontinuities play a key role in the mechanical characteristics of the rock and thereby influence the stability of rock engineering. In this study, a series of impact tests were performed using a split Hopkinson pressure bar system with high-speed photography to investigate the effect of interlayer strength on the wave propagation and fracturing process in composite rock-mortar specimens.The results indicate that the transmission coefficient, nominal dynamic strength, interlayer closure, and specific normal stiffness generally increase linearly with increasing interlayer stiffness. The cement mortar layer can serve as a buffer during the deformation of composite specimens. The digital images show that tensile cracks are typically initiated at the rock-mortar interface, propagate along the loading direction, and eventually result in a tensile failure regardless of the interlayer properties. However, when a relatively weaker layer is sandwiched between the rock matrix, an increasing amount of cement mortar is violently ejected and slight slabbing occurs near the rock-mortar interface.

关键词： Rock dynamics Wave propagation Rock-mortar Stiffness Energy Fracturing

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Intelligent method to experimentally identify the fracture mechanism of red sandstone

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International Journal of Minerals,Metallurgy and Materials 2023年第11期30卷 2134-2146页

作者： Zida liu diyuan li Quanqi Zhu Chenxi Zhang Jinyin Ma Junjie Zhao School of Resources and Safety Engineering Central South UniversityChangsha 410083China

Tensile and shear fractures are significant mechanisms for rock failure.Understanding the fractures that occur in rock can reveal rock failure mechanisms.Scanning electron microscopy(SEM)has been widely used to analyze tensile and shear fractures of rock on a mesoscopic scale.To quantify tensile and shear fractures,this study proposed an innovative method composed of SEM images and deep learning techniques to identify tensile and shear fractures in red sandstone.First,direct tensile and preset angle shear tests were performed for red sandstone to produce representative tensile and shear fracture surfaces,which were then observed by SEM.Second,these obtained SEM images were applied to develop deep learning models(AlexNet,VGG13,and SqueezeNet).Model evaluation showed that VGG13 was the best model,with a testing accuracy of 0.985.Third,the features of tensile and shear fractures of red sandstone learned by VGG13 were analyzed by the integrated gradient algorithm.VGG13 was then implemented to identify the distribution and proportion of tensile and shear fractures on the failure surfaces of rock fragments caused by uniaxial compression and Brazilian splitting tests.Results demonstrated the model feasibility and suggested that the proposed method can reveal rock failure mechanisms.

关键词： tensile and shear fractures rock failure deep learning scanning electron microscopy red sandstone

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Effects of external dynamic disturbances and structural plane on rock fracturing around deep underground cavern

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International Journal of Coal Science & Technology 2022年第1期9卷 99-119页

作者： Fan Feng Shaojie Chen Xingdong Zhao diyuan li Xianlai Wang Jiqiang Cui College of Energy and Mining Engineering Shandong University of Science and TechnologyQingdaoShandongChina State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainanAnhuiChina School of Resources and Civil Engineering Northeastern UniversityShenyangLiaoningChina School of Resources and Safety Engineering Central South UniversityChangshaHunanChina State Key Laboratory of Nickel and Cobalt Resources

The occurrence of disasters in deep mining engineering has been confirmed to be closely related to the external dynamic disturbances and geological discontinuities.Thus,a combined finite-element method was employed to simulate the failure process of an underground cavern,which provided insights into the failure mechanism of deep hard rock affected by factors such as the dynamic stress-wave amplitudes,disturbance direction,and dip angles of the structural plane.The crack-propagation process,stress-field distribution,displacement,velocity of failed rock,and failure zone around the circular cavern were analyzed to identify the dynamic response and failure properties of the underground structures.The simulation results indicate that the dynamic disturbance direction had less influence on the dynamic response for the constant in situ stress state,while the failure intensity and damage range around the cavern always exhibited a monotonically increasing trend with an increase in the dynamic load.The crack distribution around the circular cavern exhibited an asymmetric pattern,possibly owing to the stress-wave reflection behavior and attenuation effect along the propagation route.Geological discontinuities significantly affected the stability of nearby caverns subjected to dynamic disturbances,during which the failure intensity exhibited the pattern of an initial increase followed by a decrease with an increase in the dip angle of the structural plane.Additionally,the dynamic disturbance direction led to variations in the crack distribution for specific structural planes and stress states.These results indicate that the failure behavior should be the integrated response of the excavation unloading effect,geological conditions,and external dynamic disturbances.

关键词： Underground cavern Dynamic disturbances Structural plane Crack propagation Failure intensity Excavation unloading

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Intelligent rockburst prediction model with sample category balance using feedforward neural network and Bayesian optimization

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Underground Space 2022年第5期7卷 833-846页

作者： diyuan li Zida liu Peng Xiao Jian Zhou Danial Jahed Armaghani School of Resources and Safety Engineering Central South UniversityChangsha 410083China Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State University76Lenin ProspectChelyabinsk 454080Russia

The rockburst prediction becomes more and more challenging due to the development of deep underground projects and constructions.Increasing numbers of intelligent algorithms are used to predict and prevent rockburst.This paper investigated the drawbacks of neural networks in rockburst prediction,and aimed at these shortcomings,Bayesian optimization and the synthetic minority oversampling technique+Tomek link(SMOTETomek)were applied to efficiently develop the feedforward neural network(FNN)model for rockburst prediction.In this regard,314 real rockburst cases were collected to establish a database for modeling.The database was divided into a training set(80%)and a test set(20%).The maximum tangential stress,uniaxial compressive strength,tensile strength,stress ratio,brittleness ratio,and elastic strain energy were selected as input parameters.Bayesian optimization was implemented to find the optimal hyperparameters in FNN.To eliminate the effects of imbalanced category,SMOTETomek was adopted to process the training set to obtain a balanced training set.The FNN developed by the balanced training set received 90.48% accuracy in the test set,and the accuracy improved 12.7% compared to the imbalanced training set.For interpreting the FNN model,the permutation importance algorithm was introduced to analyze the relative importance of input variables.The elastic strain energy was the most essential variable,and some measures were proposed to prevent rockburst.To validate the practicability,the FNN developed by the balanced training set was utilized to predict rockburst in Sanshandao Gold Mine,China,and it had outstanding performance(accuracy 100%).

关键词： Rockburst prediction Feedforward neural network Bayesian optimization SMOTETomek

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Rock Strength Estimation Using Several Tree-Based ML Techniques

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Computer Modeling in Engineering & Sciences 2022年第12期133卷 799-824页

作者： Zida liu Danial Jahed Armaghani Pouyan Fakharian diyuan li Dmitrii Vladimirovich Ulrikh Natalia Nikolaevna Orekhova Khaled Mohamed Khedher School of Resources and Safety Engineering Central South UniversityChangsha410083China Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia Faculty of Civil Engineering Semnan UniversitySemnan35131-19111Iran Department of Geology Mine Surveying and Mineral ProcessingNosov Magnitogorsk State Technical UniversityMagnitogorsk455000Russia Department of Civil Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Civil Engineering High Institute of Technological StudiesMrezgua University CampusNabeul8000Tunisia

The uniaxial compressive strength(UCS)of rock is an essential property of rock material in different relevant applications,such as rock slope,tunnel construction,and foundation.It takes enormous time and effort to obtain the UCS values directly in the laboratory.Accordingly,an indirect determination of UCS through conducting several rock index tests that are easy and fast to carry out is of interest and importance.This study presents powerful boosting trees evaluation framework,i.e.,adaptive boosting machine,extreme gradient boosting machine(XGBoost),and category gradient boosting machine,for estimating the UCS of sandstone.Schmidt hammer rebound number,P-wave velocity,and point load index were chosen as considered factors to forecast UCS values of sandstone samples.Taylor diagrams and five regression metrics,including coefficient of determination(R2),root mean square error,mean absolute error,variance account for,and A-20 index,were used to evaluate and compare the performance of these boosting trees.The results showed that the proposed boosting trees are able to provide a high level of prediction capacity for the prepared database.In particular,itwas worth noting that XGBoost is the best model to predict sandstone strength and it achieved 0.999 training R^(2) and 0.958 testing R^(2).The proposed model had more outstanding capability than neural network with optimization techniques during training and testing phases.The performed variable importance analysis reveals that the point load index has a significant influence on predicting UCS of sandstone.

关键词： Uniaxial compressive strength rock index tests machine learning techniques boosting tree

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基于CT扫描技术的煤试件力学特性及裂隙扩展规律研究

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采矿与安全工程学报 2024年第3期41卷 611-622页

作者：弓昊李地元李铮胡皓宇中南大学资源与安全工程学院湖南长沙410083 中国矿业大学(北京)能源与矿业学院北京100083

我国中东部矿区相继进入深部开采阶段,随着构造应力的增大与开采扰动效应的增强,工作面煤体内部裂隙发育程度逐渐升高,导致煤壁失稳现象频发。为研究煤体在高地应力环境下的力学特性及裂纹扩展规律,基于CT扫描技术实现煤试件内部各组分... 详细信息

我国中东部矿区相继进入深部开采阶段,随着构造应力的增大与开采扰动效应的增强,工作面煤体内部裂隙发育程度逐渐升高,导致煤壁失稳现象频发。为研究煤体在高地应力环境下的力学特性及裂纹扩展规律,基于CT扫描技术实现煤试件内部各组分的精准识别,建立煤试件三维重构模型和精细化数值模型,并结合单轴压缩试验研究煤试件内部各组分空间分布特征对试件变形参数、峰值强度和裂隙扩展路径的影响。结果表明:原生裂隙倾角越大,试件初始模量值Ei降低,试件容易发生纵向劈裂破坏;原生裂隙倾角越小,试件峰值强度σ_(c)降低,试件容易发生拉剪混合破坏;高密度杂质体积占比越大,煤试件弹性模量值E_(t)增大,试件所呈现的脆性特征越强;原生裂隙体积占比越大,试件应变硬化特征明显,且试件压缩破坏后的裂隙体积占比与复杂程度也明显增大。煤试件裂隙的扩展路径主要沿着原生裂隙横向扩张和纵向延伸,由于裂隙的快速扩展,常常伴随着分叉裂隙的产生,且原生裂隙的倾向分布越复杂,产生分叉裂隙的数目就越多;当原生裂隙未经过高密度杂质时,裂隙扩展路径往往会绕过其位置扩展;当原生裂隙经过高密度杂质时,裂隙的扩展路径会贯通其位置扩展。数值模拟结果与室内试验结果吻合程度较高,数值模型微裂隙的扩展模式与CT扫描所得的裂隙扩展路径一致,验证了精细化数值模型的可靠性。

关键词： CT扫描三维重构力学特性裂隙扩展精细化数值模型

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Numerical investigation on the tensile fracturing behavior of rock-shotcrete interface based on discrete element method

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International Journal of Mining Science and Technology 2020年第3期30卷 293-301页

作者： Jiadong Qiu lin Luo Xibing li diyuan li Ying Chen Yong Luo Shenzhen Key Laboratory of Deep Underground Engineering Sciences and Green Energy Shenzhen UniversityShenzhen 518060China Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of EducationCentral South UniversityChangsha 410083China School of Resources and Safety Engineering Central South UniversityChangsha 410083China Center of Rock Mechanics and Geohazards Shaoxing UniversityShaoxing 312000China

Four groups of numerical models of Brazilian tests on rock-shotcrete interfaces were successfully conducted by PFC2D. The tensile strength and Young’s modulus of shotcrete were considered. Six different undulations of rock-shotcrete interface were set up. The influences of multiple parameters on the bearing characteristics of the rock-shotcrete interface were studied. The results showed that a better support performance can be obtained by increasing the Young’s modulus of shotcrete rather than the tensile strength of shotcrete. For different tensile strength and Young’s modulus, the increase of sawtooth height has different effects on the support performance. The failure mechanism of the rock-shotcrete interfaces was analysed in detail. The stress shielding effect and stress concentration effect caused by the shape characteristics of rock-shotcrete interface were observed. The influence of these parameters on the overall support performance should be fully considered in a reasonable support design.

关键词： Tensile strength PFC Shotcrete Fracturing behavior Rock interface

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Prediction of uniaxial compressive strength of rock based on lithology using stacking models

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2023年第4期2卷 56-69页

作者： Zida liu diyuan li Yongping liu Bo Yang Zong-Xian Zhang School of Resources and Safety Engineering Central South UniversityChangsha410083China Nickel Cobalt Research and Design Institute Jinchuan Group Co.Ltd.Jinchang737104China Oulu Mining School University of OuluOuluFinland

Uniaxial compressive strength(UCS)of rock is an essential parameter in geotechnical engineering.Point load strength(PLS),P-wave velocity,and Schmidt hammer rebound number(SH)are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS.This study collected 1080 datasets consisting of SH,P-wave velocity,PLS,and UCS.All datasets were integrated into three categories(sedimentary,igneous,and metamorphic rocks)according to lithology.Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types.Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types.UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks.Nonetheless,the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks.The developed predictive models can be applied to quickly predict UCS at engineering sites,which benefits the rapid and intelligent classification of rock masses.Moreover,the importance of SH,P-wave velocity,and PLS were analyzed for the estimation of UCS.SH was a reliable indicator for UCS evaluation across various rock types.P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks,but it was not reliable for assessing the UCS of metamorphic rocks.

关键词： Uniaxial compressive strength Point load strength P-wave velocity Schmidt hammer rebound number Stacking models

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