Although pressure cells have been produced and installed successfully for decades,the accuracy of measured pressure is often inadequate.Due to large differences between the stiffness of pressure cells and the surround...Although pressure cells have been produced and installed successfully for decades,the accuracy of measured pressure is often inadequate.Due to large differences between the stiffness of pressure cells and the surrounding media,there is a considerable difference between applied pressure and that measured from pressure cells.It is often difficult and expensive to make a pressure cell with stiffness（modulus of elasticity） similar to the surrounding material in which it will be embedded.In order to improve this situation,a casing material with proportional dimensions is recommended as a means to obtain reliable results.In our study,the effect of using casing in the installation of pressure cells is investigated,providing the characteristics of casing.Some practical recommendations are presented to improve the accuracy of the results using casing.展开更多
Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing...Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.展开更多
In this study, we selected 9 typical coal samples with different metamorphic grades as the study subjects,measured their initial 30-min gas desorption at 30℃ and different pressure using a self-developed gas adsorpti...In this study, we selected 9 typical coal samples with different metamorphic grades as the study subjects,measured their initial 30-min gas desorption at 30℃ and different pressure using a self-developed gas adsorption/desorption device. Based on the characteristics of gas desorption from coal samples, we proposed a direct fitting method for measurement of gas content in coalbed, analyzed the effects of sampling time on the measurement results and determined the reasonable sampling time of coal samples with different metamorphic grades at different gas adsorption pressure at equilibrium. The results show that (1)the error of gas contents obtained using the direct fitting method relative to that obtained using indirect method is less than 10%, which meets the actual on-site requirements and verifies the feasibility of the direct fitting method;(2) when the relative error is controlled within ±10%, the reasonable sampling time of coal samples is linearly related to the gas adsorption pressure at equilibrium;(3) the reasonable sampling time of coal samples with the same metamorphic grade exhibits a shortening trend with increasing gas adsorption pressure at equilibrium;(4) for coal samples with similar gas adsorption pressure at equilibrium, the reasonable sampling time of coal samples displays a shortening trend with increasing metamorphic grade. Overall, the study provides a basis for improving the measurement accuracy of gas content in coalbed.展开更多
文摘Although pressure cells have been produced and installed successfully for decades,the accuracy of measured pressure is often inadequate.Due to large differences between the stiffness of pressure cells and the surrounding media,there is a considerable difference between applied pressure and that measured from pressure cells.It is often difficult and expensive to make a pressure cell with stiffness（modulus of elasticity） similar to the surrounding material in which it will be embedded.In order to improve this situation,a casing material with proportional dimensions is recommended as a means to obtain reliable results.In our study,the effect of using casing in the installation of pressure cells is investigated,providing the characteristics of casing.Some practical recommendations are presented to improve the accuracy of the results using casing.
文摘Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.
基金the support of the National Natural Science Foundation of China(Nos.51674158,51604168 and 51504142)the Natural Science Foundation of Shandong Province(No.ZR2016EEQ18)+2 种基金the SDUST Research Fund(No.2015JQJH105)the Qingdao Postdoctoral Applied Research Project(No.2015204)the Taishan Scholar Talent Team Support Plan for Advantaged&Unique Discipline Areas
文摘In this study, we selected 9 typical coal samples with different metamorphic grades as the study subjects,measured their initial 30-min gas desorption at 30℃ and different pressure using a self-developed gas adsorption/desorption device. Based on the characteristics of gas desorption from coal samples, we proposed a direct fitting method for measurement of gas content in coalbed, analyzed the effects of sampling time on the measurement results and determined the reasonable sampling time of coal samples with different metamorphic grades at different gas adsorption pressure at equilibrium. The results show that (1)the error of gas contents obtained using the direct fitting method relative to that obtained using indirect method is less than 10%, which meets the actual on-site requirements and verifies the feasibility of the direct fitting method;(2) when the relative error is controlled within ±10%, the reasonable sampling time of coal samples is linearly related to the gas adsorption pressure at equilibrium;(3) the reasonable sampling time of coal samples with the same metamorphic grade exhibits a shortening trend with increasing gas adsorption pressure at equilibrium;(4) for coal samples with similar gas adsorption pressure at equilibrium, the reasonable sampling time of coal samples displays a shortening trend with increasing metamorphic grade. Overall, the study provides a basis for improving the measurement accuracy of gas content in coalbed.
