In this paper, the optimization design of the low strength mechanical test and orthogonal test have been analyzed in order to simulate the mechanical properties of thick and extra-thick coal seam accurately in a simil...In this paper, the optimization design of the low strength mechanical test and orthogonal test have been analyzed in order to simulate the mechanical properties of thick and extra-thick coal seam accurately in a similar material simulation test. The results show that the specimen can reach a wider range of strength when cement has been used compared to that of gypsum, suggesting that cement is more suitable for making coal seam in similar material simulation tests. The uniaxial compressive strength is more sensitive to cement than coal or sand. The proportion of coal and sand do not play a decisive role in uniaxial compressive strength. The uniaxial compressive strength and specimen density decrease as the mass percent of coal and aggregate–binder ratio rise. There is a positive correlation between uniaxial compressive strength and density. The No. 5 proportion(cement: sand: water: activated carbon: coal = 6:6:7:1.1:79.9)was chosen to be used in the similar material simulation test of steeply dipping and extra-thick coal seam with a density of 0.913 g/cm^3 and an uniaxial compressive strength of 0.076 MPa which are in accordance with the similarity theory. The phenomenon of overburden stratum movement, fracture development and floor pressure relief were obtained during the similar material simulation test by using the proportion.展开更多
This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of ...This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.展开更多
Different phenomenological equations based on plasticity, primary creep （as a viscoplastic mechanism）, secondary creep （as another viscoplastic mechanism） and different combinations of these equations are presente...Different phenomenological equations based on plasticity, primary creep （as a viscoplastic mechanism）, secondary creep （as another viscoplastic mechanism） and different combinations of these equations are presented and used to describe the material inelastic deformation in uniaxial test. Agreement of the models with experimental results and with the theoretical concepts and physical realities is the criterion of choosing the most appropriate formulation for uniaxial test. A model is thus proposed in which plastic deformation, primary creep and secondary creep contribute to the inelastic deformation. However, it is believed that the hardening parameter is composed of plastic and primary creep parts. Accordingly, the axial plastic strain in a uniaxial test may no longer be considered as the hardening parameter. Therefore, a proportionality concept is proposed to calculate the plastic contribution of deformation.展开更多
基金support of National Natural Science Foundation Project of China (51304128 & 51304237) the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents of China (2013RCJJ049)
文摘In this paper, the optimization design of the low strength mechanical test and orthogonal test have been analyzed in order to simulate the mechanical properties of thick and extra-thick coal seam accurately in a similar material simulation test. The results show that the specimen can reach a wider range of strength when cement has been used compared to that of gypsum, suggesting that cement is more suitable for making coal seam in similar material simulation tests. The uniaxial compressive strength is more sensitive to cement than coal or sand. The proportion of coal and sand do not play a decisive role in uniaxial compressive strength. The uniaxial compressive strength and specimen density decrease as the mass percent of coal and aggregate–binder ratio rise. There is a positive correlation between uniaxial compressive strength and density. The No. 5 proportion(cement: sand: water: activated carbon: coal = 6:6:7:1.1:79.9)was chosen to be used in the similar material simulation test of steeply dipping and extra-thick coal seam with a density of 0.913 g/cm^3 and an uniaxial compressive strength of 0.076 MPa which are in accordance with the similarity theory. The phenomenon of overburden stratum movement, fracture development and floor pressure relief were obtained during the similar material simulation test by using the proportion.
基金This research is funded by the National Natural Science Foundation of China(41807285,41762020,51879127 and 51769014E)Natural Science Foundation of Hebei Province(D2022202005).
文摘This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.
文摘Different phenomenological equations based on plasticity, primary creep （as a viscoplastic mechanism）, secondary creep （as another viscoplastic mechanism） and different combinations of these equations are presented and used to describe the material inelastic deformation in uniaxial test. Agreement of the models with experimental results and with the theoretical concepts and physical realities is the criterion of choosing the most appropriate formulation for uniaxial test. A model is thus proposed in which plastic deformation, primary creep and secondary creep contribute to the inelastic deformation. However, it is believed that the hardening parameter is composed of plastic and primary creep parts. Accordingly, the axial plastic strain in a uniaxial test may no longer be considered as the hardening parameter. Therefore, a proportionality concept is proposed to calculate the plastic contribution of deformation.
