In the process of 2-D compressional wave propagation in a rock mass with multiple parallel joints along the radian direction normal to the joints, the maximum possible wave amplitude corresponding to the points betwee...In the process of 2-D compressional wave propagation in a rock mass with multiple parallel joints along the radian direction normal to the joints, the maximum possible wave amplitude corresponding to the points between the two adjacent joints in the joint set is controlled by superposition of the multiple transmitted and the reflected waves, measured by the maximum rebound ratio. Parametric studies on the maximum rebound ratio along the radian direction normal to the joints were performed in universal distinct element code. The results show that the maximum rebound ratio is influenced by three factors, i.e., the normalized normal stiffness of joints, the ratio of joint spacing to wavelength and the joint from which the wave rebounds. The relationship between the maximum rebound ratio and the influence factors is generalized into five charts. Those charts can be used as the prediction model for estimating the maximum rebound ratio.展开更多
针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为...针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为降低专家的主观偏差,应用基于层级的模糊权重评估(Fuzzy level based weight assessment,FLBWA)法来计算各评价指标权重;继而结合改进的Borda-全乘比例多目标优化(Borda-multi-objective optimization on the basis of ratio analysis plus full multiplicative form,Borda-MULTIMOORA)法计算开发适宜性指数,从而能够更加准确、高效地得到评价结果;之后,基于灰狼优化算法的反向传播(Grey wolf optimizer with back propagation,GWO-BP)神经网络构建并训练智能模型,将适宜性分析转化为自动化、高效化和智能化的过程;最后,以山东省风浪联合开发区划为例验证该模型的可行性和合理性。根据实例验证,该模型可以实现风浪联合开发区划的智能化,为相关领域的研究和政府规划提供参考。展开更多
Shear band (SB), axial, lateral and volumetric strains as well as Poisson’s ratio of anisotropic jointed rock specimen (JRS) were modeled by Fast Lagrangian Analysis of Continua (FLAC). Failure criterion of rock was ...Shear band (SB), axial, lateral and volumetric strains as well as Poisson’s ratio of anisotropic jointed rock specimen (JRS) were modeled by Fast Lagrangian Analysis of Continua (FLAC). Failure criterion of rock was a composited Mohr-Coulomb criterion with tension cut-off. An inclined joint was treated as square elements of ideal plastic material beyond the peak strength. Several FISH functions were written to automatically find the addresses of elements in the joint and to calculate the entire deformational characteristics of plane strain JRS. The results show that for moderate joint inclination (JI), strain is only concentrated into the joint governing the behavior of JRS, leading to ideal plastic responses in axial and lateral directions. For higher JI, the post-peak stress-axial and lateral strain curves become steeper as JI increases owing to the increase of new SB’s length. Lateral expansion and precursor to the unstable failure are the most apparent, resulting in the highest Poisson’s ratio and even negative volumetric strain. For lower JI, the entire post-peak deformational characteristics are independent of JI. The lowest lateral expansion occurs, leading to the lowest Poisson’s ratio and positive volumetric strain all along. The present prediction on anisotropic strength in plane strain compression qualitatively agrees with the results in triaxial tests of rocks. The JI calculated by Jaeger’s formula overestimates that related to the minimum strength. Advantages of the present numerical model over the Jaeger’s model are pointed out.展开更多
The proposed prediction model for estimating the maximum rebound ratio was applied to a field explosion test, Mandai test in Singapore. The estimated possible maximum peak particle velocities(PPVs) were compared with ...The proposed prediction model for estimating the maximum rebound ratio was applied to a field explosion test, Mandai test in Singapore. The estimated possible maximum peak particle velocities(PPVs) were compared with the field records. Three of the four available field-recorded PPVs lie exactly below the estimated possible maximum values as expected, while the fourth available field-recorded PPV lies close to and a bit higher than the estimated maximum possible PPV. The comparison results show that the predicted PPVs from the proposed prediction model for the maximum rebound ratio match the field-recorded PPVs better than those from two empirical formulae. The very good agreement between the estimated and field-recorded values validates the proposed prediction model for estimating PPV in a rock mass with a set of joints due to application of a two dimensional compressional wave at the boundary of a tunnel or a borehole.展开更多
基金Projects(50278057) supported by the National Natural Science Foundation of China project(2002CB412703) supported by Major State Basic Research Development Program of China
文摘In the process of 2-D compressional wave propagation in a rock mass with multiple parallel joints along the radian direction normal to the joints, the maximum possible wave amplitude corresponding to the points between the two adjacent joints in the joint set is controlled by superposition of the multiple transmitted and the reflected waves, measured by the maximum rebound ratio. Parametric studies on the maximum rebound ratio along the radian direction normal to the joints were performed in universal distinct element code. The results show that the maximum rebound ratio is influenced by three factors, i.e., the normalized normal stiffness of joints, the ratio of joint spacing to wavelength and the joint from which the wave rebounds. The relationship between the maximum rebound ratio and the influence factors is generalized into five charts. Those charts can be used as the prediction model for estimating the maximum rebound ratio.
文摘针对目前海洋能区划研究中存在的计算复杂、耗时长和成本高等问题,本研究基于改进的多准则决策(Multiple criteria decision making,MCDM)方法和人工神经网络(Artificial neural network,ANN),提出了一种风浪联合开发区划智能模型。为降低专家的主观偏差,应用基于层级的模糊权重评估(Fuzzy level based weight assessment,FLBWA)法来计算各评价指标权重;继而结合改进的Borda-全乘比例多目标优化(Borda-multi-objective optimization on the basis of ratio analysis plus full multiplicative form,Borda-MULTIMOORA)法计算开发适宜性指数,从而能够更加准确、高效地得到评价结果;之后,基于灰狼优化算法的反向传播(Grey wolf optimizer with back propagation,GWO-BP)神经网络构建并训练智能模型,将适宜性分析转化为自动化、高效化和智能化的过程;最后,以山东省风浪联合开发区划为例验证该模型的可行性和合理性。根据实例验证,该模型可以实现风浪联合开发区划的智能化,为相关领域的研究和政府规划提供参考。
基金Project(50309004) supported by the National Natural Science Foundation of China
文摘Shear band (SB), axial, lateral and volumetric strains as well as Poisson’s ratio of anisotropic jointed rock specimen (JRS) were modeled by Fast Lagrangian Analysis of Continua (FLAC). Failure criterion of rock was a composited Mohr-Coulomb criterion with tension cut-off. An inclined joint was treated as square elements of ideal plastic material beyond the peak strength. Several FISH functions were written to automatically find the addresses of elements in the joint and to calculate the entire deformational characteristics of plane strain JRS. The results show that for moderate joint inclination (JI), strain is only concentrated into the joint governing the behavior of JRS, leading to ideal plastic responses in axial and lateral directions. For higher JI, the post-peak stress-axial and lateral strain curves become steeper as JI increases owing to the increase of new SB’s length. Lateral expansion and precursor to the unstable failure are the most apparent, resulting in the highest Poisson’s ratio and even negative volumetric strain. For lower JI, the entire post-peak deformational characteristics are independent of JI. The lowest lateral expansion occurs, leading to the lowest Poisson’s ratio and positive volumetric strain all along. The present prediction on anisotropic strength in plane strain compression qualitatively agrees with the results in triaxial tests of rocks. The JI calculated by Jaeger’s formula overestimates that related to the minimum strength. Advantages of the present numerical model over the Jaeger’s model are pointed out.
基金Project(50278057) supported by the National Natural Science Foundation of Chinaproject(2002CB412703) supported by the Major State Basic Research Development Program of China
文摘The proposed prediction model for estimating the maximum rebound ratio was applied to a field explosion test, Mandai test in Singapore. The estimated possible maximum peak particle velocities(PPVs) were compared with the field records. Three of the four available field-recorded PPVs lie exactly below the estimated possible maximum values as expected, while the fourth available field-recorded PPV lies close to and a bit higher than the estimated maximum possible PPV. The comparison results show that the predicted PPVs from the proposed prediction model for the maximum rebound ratio match the field-recorded PPVs better than those from two empirical formulae. The very good agreement between the estimated and field-recorded values validates the proposed prediction model for estimating PPV in a rock mass with a set of joints due to application of a two dimensional compressional wave at the boundary of a tunnel or a borehole.
