作为表征动力破碎的重要参数之一,平均破碎块度的研究对于揭示岩石破碎机理具有重要意义。尽管进行了大量理论与实验研究,但是还缺乏从裂纹动力学角度来澄清岩石破碎和块度形成机理。基于动态荷载作用下翼型裂纹扩展模型和J. R. Gladde...作为表征动力破碎的重要参数之一,平均破碎块度的研究对于揭示岩石破碎机理具有重要意义。尽管进行了大量理论与实验研究,但是还缺乏从裂纹动力学角度来澄清岩石破碎和块度形成机理。基于动态荷载作用下翼型裂纹扩展模型和J. R. Gladden柱体动力屈曲失稳模型,提出了一种预测岩石平均破碎块度的方法,并探究了应变率对动态强度和平均破碎块度的影响。研究结果表明,随着应变率的增加,动态强度增加,平均破碎块度减小,且应变率依赖性逐渐降低。模型平均破碎块度预测与实验数据吻合良好。展开更多
In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanica...In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanical property of joints,has been a focal point in the research field.There are limitations in the current peak shear strength(PSS)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a PSS prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors.To overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on Support Vector Regression(SVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).Based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models.We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models.The prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while SVR_linear is at 16.6%,SVR_poly is at 14.0%,and SVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%error.Additionally,we performed shear tests on granite joints to validate the predictive capability of the DL-based model.With the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes.展开更多
通过全球数字系统(global digital system,GDS)动三轴测试系统,对砂土进行了常规三轴压缩试验和偏压固结下的等p(平均应力)、等σ3(围压)、等σ1(围压)等不同应力路径的试验。通过对所有的实验结果进行了对比分析,研究了砂土材料在不同...通过全球数字系统(global digital system,GDS)动三轴测试系统,对砂土进行了常规三轴压缩试验和偏压固结下的等p(平均应力)、等σ3(围压)、等σ1(围压)等不同应力路径的试验。通过对所有的实验结果进行了对比分析,研究了砂土材料在不同应力路径下的应力-应变、变形特性、强度特性。试验研究结果表明,等压应力路径试验中试样都是先体积收缩随后出现体积膨胀现象,这与高围压下砂土的剪胀性变化情况不一样;偏压固结试验中,整个加载阶段前期表现为应变硬化,但是后期的软化现象不是很明显,这与等压固结试验应变硬化-软化现象略有不同。虽然常规三轴和偏压固结下的σ3等试验采用的是两种不同的固结方式,但是达到的峰值强度基本上是一样的,说明固结方式对于试样的强度没有太大的影响。偏压固结试验中,不同的应力路径达到峰值强度时所对应的轴向应变是不同的,而且峰值强度也不一样,说明不同的应力路径会对砂土的强度造成影响,同时也说明了砂土力学特性对于应力路径的依赖性。展开更多
文摘作为表征动力破碎的重要参数之一,平均破碎块度的研究对于揭示岩石破碎机理具有重要意义。尽管进行了大量理论与实验研究,但是还缺乏从裂纹动力学角度来澄清岩石破碎和块度形成机理。基于动态荷载作用下翼型裂纹扩展模型和J. R. Gladden柱体动力屈曲失稳模型,提出了一种预测岩石平均破碎块度的方法,并探究了应变率对动态强度和平均破碎块度的影响。研究结果表明,随着应变率的增加,动态强度增加,平均破碎块度减小,且应变率依赖性逐渐降低。模型平均破碎块度预测与实验数据吻合良好。
基金supported by the National Key Research and Development Program of China(2022YFC3080100)the National Natural Science Foundation of China(Nos.52104090,52208328 and 12272353)+1 种基金the Open Fund of Anhui Province Key Laboratory of Building Structure and Underground Engineering,Anhui Jianzhu University(No.KLBSUE-2022-06)the Open Research Fund of Key Laboratory of Construction and Safety of Water Engineering of the Ministry of Water Resources,China Institute of Water Resources and Hydropower Research(Grant No.IWHR-ENGI-202302)。
文摘In geotechnical and tunneling engineering,accurately determining the mechanical properties of jointed rock holds great significance for project safety assessments.Peak shear strength(PSS),being the paramount mechanical property of joints,has been a focal point in the research field.There are limitations in the current peak shear strength(PSS)prediction models for jointed rock:(i)the models do not comprehensively consider various influencing factors,and a PSS prediction model covering seven factors has not been established,including the sampling interval of the joints,the surface roughness of the joints,the normal stress,the basic friction angle,the uniaxial tensile strength,the uniaxial compressive strength,and the joint size for coupled joints;(ii)the datasets used to train the models are relatively limited;and(iii)there is a controversy regarding whether compressive or tensile strength should be used as the strength term among the influencing factors.To overcome these limitations,we developed four machine learning models covering these seven influencing factors,three relying on Support Vector Regression(SVR)with different kernel functions(linear,polynomial,and Radial Basis Function(RBF))and one using deep learning(DL).Based on these seven influencing factors,we compiled a dataset comprising the outcomes of 493 published direct shear tests for the training and validation of these four models.We compared the prediction performance of these four machine learning models with Tang’s and Tatone’s models.The prediction errors of Tang’s and Tatone’s models are 21.8%and 17.7%,respectively,while SVR_linear is at 16.6%,SVR_poly is at 14.0%,and SVR_RBF is at 12.1%.DL outperforms the two existing models with only an 8.5%error.Additionally,we performed shear tests on granite joints to validate the predictive capability of the DL-based model.With the DL approach,the results suggest that uniaxial tensile strength is recommended as the material strength term in the PSS model for more reliable outcomes.
基金Supported by the National Natural Science Foundation of China(11032001,51174012,50825403)Russian Foundation for Basic Research(11-01-91217)+3 种基金"973"Key State Research Program(010CB732003)The Innovation School Foundation(51021001)Beijing Natural Science Foundation(KZ200810016007)Scientific School of Modeling and Analysis of Nonlinear Systems(PHR201107123)
文摘通过全球数字系统(global digital system,GDS)动三轴测试系统,对砂土进行了常规三轴压缩试验和偏压固结下的等p(平均应力)、等σ3(围压)、等σ1(围压)等不同应力路径的试验。通过对所有的实验结果进行了对比分析,研究了砂土材料在不同应力路径下的应力-应变、变形特性、强度特性。试验研究结果表明,等压应力路径试验中试样都是先体积收缩随后出现体积膨胀现象,这与高围压下砂土的剪胀性变化情况不一样;偏压固结试验中,整个加载阶段前期表现为应变硬化,但是后期的软化现象不是很明显,这与等压固结试验应变硬化-软化现象略有不同。虽然常规三轴和偏压固结下的σ3等试验采用的是两种不同的固结方式,但是达到的峰值强度基本上是一样的,说明固结方式对于试样的强度没有太大的影响。偏压固结试验中,不同的应力路径达到峰值强度时所对应的轴向应变是不同的,而且峰值强度也不一样,说明不同的应力路径会对砂土的强度造成影响,同时也说明了砂土力学特性对于应力路径的依赖性。
