Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black ...Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.展开更多
This study focused on developing a risk assessment method for explosion at a coal reclaim tunnel （CRT） facility. The method was developed based on an analytical hierarchy process （AHP）, which is an expert system t...This study focused on developing a risk assessment method for explosion at a coal reclaim tunnel （CRT） facility. The method was developed based on an analytical hierarchy process （AHP）, which is an expert system that quantifies the factors of explosion incidents, based on events and hierarchies. In this paper, the proposed model was modification from original AHP model, specifically modifying the structure from ＂alternative＇s results＂ to ＂total risk-rating＇s results＂. The total risk-rating is obtained by summing up risk-rating of each factor, where the risk-rating is a multiplication product of the risk value by the AHP weighted value. To support decision-making using the expert system, data on the real conditions of the CRT were collected and analyzed. A physical modeling of the CRT with laboratory-scale experiments was carried out to show the impact of a ventilation system in CRT on diluting the methane gas and coal dust, in order to support the quantification of AHP risk value. The criteria to evaluate the risk of explosion was constructed from six components that are： fuel, oxygen, ignition, confinement, dispersion, and monitoring system. Those components had fifty-two factors that serve as sub-components （root causes）. The main causes of explosion in CRT were found to be： mechanical ventilation failure and abnormal ventilation, breakdown of monitoring system, and coal spontaneous-combustion. Assessments of two CRT facilities at Mine A and Mine B were carried out as a case study in order to check the reliability of the developed AHP method. The results showed that the risk rating of Mine A was classified as high and Mine B was classified as medium, which is in a good agreement with the site conditions.展开更多
In order to analyze the stability of the underground rock structures,knowing the sensitivity of geomechanical parameters is important.To investigate the priority of these geomechanical properties in the stability of c...In order to analyze the stability of the underground rock structures,knowing the sensitivity of geomechanical parameters is important.To investigate the priority of these geomechanical properties in the stability of cavern,a sensitivity analysis has been performed on a single cavern in various rock mass qualities according to RMR using Phase 2.The stability of cavern has been studied by investigating the side wall deformation.Results showed that most sensitive properties are coefficient of lateral stress and modulus of deformation.Also parameters of Hoek-Brown criterion and r c have no sensitivity when cavern is in a perfect elastic state.But in an elasto-plastic state,parameters of Hoek-Brown criterion and r c affect the deformability;such effect becomes more remarkable with increasing plastic area.Other parameters have different sensitivities concerning rock mass quality(RMR).Results have been used to propose the best set of parameters for study on prediction of sidewall displacement.展开更多
In the last few decades, the utilization of coal to generate electricity was rapidly increasing. Consequently, the production of coal combustion ash (CCA) as a by-product of coal utilization as primary energy sources ...In the last few decades, the utilization of coal to generate electricity was rapidly increasing. Consequently, the production of coal combustion ash (CCA) as a by-product of coal utilization as primary energy sources was increased. The physical and geochemical characteristics of CCA were site-specific which determined by both inherent coal-source quality and combustion condition. This study was intended to characterize the physical, chemical and mineralogical properties of a coal-combustion ash (CCA) from a site specific power plant and evaluate the leachate characteristic of some scenario on the co-placement of CCA with coal-mine waste rock. The physical properties such as specific gravity, dry density, porosity and particle size distribution were determined. Chemically, the CCA sample is enriched mainly in silica, aluminum, iron, and magnesium along with a little amount of calcium and sodium which includes in the class C fly ash category. Moreover, it is found that the mineral phases identified in the sample were quartz, mullite, aragonite, magnetite, hematite, and spinel. Co-placement experiment with mudstone waste rock shows that the CCA, though it has limited contribution to the decreasing permeability, has important contributed to increase the quality of leachate through releasing higher alkalinity. Moreover, addition of CCA did not affect to the increase of the trace metal element in the leachate. Hence, utilization of CCA by co-placement with coal mine waste rock in the dumping area is visible to be implemented.展开更多
There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Functio...There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Function(Af),Sinuosity of mountain front(Smf),Valley floor index(Vf),Hypsometric index(Hi),Mean Axial slope of channel index(MASC)and Drainage Basin Shape(Bs),have been utilized to determine the relative tectonic activity index(IAT)to recognize,eventually,the geo-structural model of the study area.Faults and folds control the geo-structural activities of the study area,and the geomorphic indices are being affected in consequence of their activities.The intensity of these activities is different throughout the plain.There are many geomorphic evidences,related to active transform fault which are detectable all over the study area such as deviated rivers,quaternary sediments transformation,fault traces.Therefore,recognition of geo-structural model of the study area is extremely vital.Field study,then,approved the results of geomorphic indices calculation in determining the geo-structural model of the study area.Results depicted that the geostructural model of the study area is a kind of Horsetail splay form which is in accordance to the relative tectonic activity of the study area.Based on the above mentioned results it can be predicted that the splays are the trail of Neyshabour fault.展开更多
Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems....Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity(UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning(ML) for analyzing wave signals and predict rock properties such as crack density(CD) – the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid(RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks(CNN) and Long Short-Term Memory(LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2> 96% with mean square error(MSE) < 25e-4(normalized values). Overall, we found that:(i) performance of both CNN and LSTM is highly promising,(ii) accuracy of the transmitted signals is slightly higher than the reflected signals,(iii) accuracy of 2D signals is marginally higher than 1D signals,(iv)accuracy of horizontal and vertical component signals are comparable,(v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling.展开更多
Large underground caverns are commonly used in variety of applications. In many cases, because of the geomechanical limitations of dimensions and requirement of high volume, several parallel caverns are used. Plastic ...Large underground caverns are commonly used in variety of applications. In many cases, because of the geomechanical limitations of dimensions and requirement of high volume, several parallel caverns are used. Plastic zone integration requires a larger rock pillar distance of theses adjacent caverns while eco- nomic and access reasons require smaller distance. In lran many underground projects are located in West and South West, Asmari formation covers a large part of these regions. The stability of underground spaces that are constructed or will be constructed in this formation has been investigated. A proper cross section based on plastic analysis and a stability criterion has been proposed for each region. Finally, in each case, allowable rock pillar between adjacent caverns with similar dimension was determined with two methods （numerical analysis and fire service law）. Results show that Fire Service Law uses a very con- servative safety factor and it was proposed to use a correction factor for allowable distance based on application of underground space.展开更多
Crack growth modeling has always been one of the major challenges in fracture mechanics.Among all numerical methods,the extended finite element method(XFEM)has recently attracted much attention due to its ability to e...Crack growth modeling has always been one of the major challenges in fracture mechanics.Among all numerical methods,the extended finite element method(XFEM)has recently attracted much attention due to its ability to estimate the discontinuous deformation field.However,XFEM modeling does not directly lead to reliable results,and choosing a strategy of implementation is inevitable,especially in porous media.In this study,two prevalent XFEM strategies are evaluated:a)applying reduced Young’s modulus to pores and b)using different partitions to the model and enriching each part individually.We mention the advantages and limitations of each strategy via both analytical and experimental validations.Finally,the crack growth is modeled in a natural porous media(Fontainebleau sandstone).Our investigations proved that although both strategies can identically predict the stress distribution in the sample,the first strategy simulates only the initial crack propagation,while the second strategy could model multiple cracks growths.Both strategies are reliable and highly accurate in calculating the stress intensity factor,but the second strategy can compute a more reliable reaction force.Experimental tests showed that the second strategy is a more accurate strategy in predicting the preferred crack growth path and determining the maximum strength of the sample.展开更多
文摘Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.
文摘This study focused on developing a risk assessment method for explosion at a coal reclaim tunnel （CRT） facility. The method was developed based on an analytical hierarchy process （AHP）, which is an expert system that quantifies the factors of explosion incidents, based on events and hierarchies. In this paper, the proposed model was modification from original AHP model, specifically modifying the structure from ＂alternative＇s results＂ to ＂total risk-rating＇s results＂. The total risk-rating is obtained by summing up risk-rating of each factor, where the risk-rating is a multiplication product of the risk value by the AHP weighted value. To support decision-making using the expert system, data on the real conditions of the CRT were collected and analyzed. A physical modeling of the CRT with laboratory-scale experiments was carried out to show the impact of a ventilation system in CRT on diluting the methane gas and coal dust, in order to support the quantification of AHP risk value. The criteria to evaluate the risk of explosion was constructed from six components that are： fuel, oxygen, ignition, confinement, dispersion, and monitoring system. Those components had fifty-two factors that serve as sub-components （root causes）. The main causes of explosion in CRT were found to be： mechanical ventilation failure and abnormal ventilation, breakdown of monitoring system, and coal spontaneous-combustion. Assessments of two CRT facilities at Mine A and Mine B were carried out as a case study in order to check the reliability of the developed AHP method. The results showed that the risk rating of Mine A was classified as high and Mine B was classified as medium, which is in a good agreement with the site conditions.
文摘In order to analyze the stability of the underground rock structures,knowing the sensitivity of geomechanical parameters is important.To investigate the priority of these geomechanical properties in the stability of cavern,a sensitivity analysis has been performed on a single cavern in various rock mass qualities according to RMR using Phase 2.The stability of cavern has been studied by investigating the side wall deformation.Results showed that most sensitive properties are coefficient of lateral stress and modulus of deformation.Also parameters of Hoek-Brown criterion and r c have no sensitivity when cavern is in a perfect elastic state.But in an elasto-plastic state,parameters of Hoek-Brown criterion and r c affect the deformability;such effect becomes more remarkable with increasing plastic area.Other parameters have different sensitivities concerning rock mass quality(RMR).Results have been used to propose the best set of parameters for study on prediction of sidewall displacement.
文摘In the last few decades, the utilization of coal to generate electricity was rapidly increasing. Consequently, the production of coal combustion ash (CCA) as a by-product of coal utilization as primary energy sources was increased. The physical and geochemical characteristics of CCA were site-specific which determined by both inherent coal-source quality and combustion condition. This study was intended to characterize the physical, chemical and mineralogical properties of a coal-combustion ash (CCA) from a site specific power plant and evaluate the leachate characteristic of some scenario on the co-placement of CCA with coal-mine waste rock. The physical properties such as specific gravity, dry density, porosity and particle size distribution were determined. Chemically, the CCA sample is enriched mainly in silica, aluminum, iron, and magnesium along with a little amount of calcium and sodium which includes in the class C fly ash category. Moreover, it is found that the mineral phases identified in the sample were quartz, mullite, aragonite, magnetite, hematite, and spinel. Co-placement experiment with mudstone waste rock shows that the CCA, though it has limited contribution to the decreasing permeability, has important contributed to increase the quality of leachate through releasing higher alkalinity. Moreover, addition of CCA did not affect to the increase of the trace metal element in the leachate. Hence, utilization of CCA by co-placement with coal mine waste rock in the dumping area is visible to be implemented.
文摘There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Function(Af),Sinuosity of mountain front(Smf),Valley floor index(Vf),Hypsometric index(Hi),Mean Axial slope of channel index(MASC)and Drainage Basin Shape(Bs),have been utilized to determine the relative tectonic activity index(IAT)to recognize,eventually,the geo-structural model of the study area.Faults and folds control the geo-structural activities of the study area,and the geomorphic indices are being affected in consequence of their activities.The intensity of these activities is different throughout the plain.There are many geomorphic evidences,related to active transform fault which are detectable all over the study area such as deviated rivers,quaternary sediments transformation,fault traces.Therefore,recognition of geo-structural model of the study area is extremely vital.Field study,then,approved the results of geomorphic indices calculation in determining the geo-structural model of the study area.Results depicted that the geostructural model of the study area is a kind of Horsetail splay form which is in accordance to the relative tectonic activity of the study area.Based on the above mentioned results it can be predicted that the splays are the trail of Neyshabour fault.
基金the Deutsche Forschungsgemeinschaft (DFG) for financial support of the CODA-project (FOR 2825)。
文摘Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity(UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning(ML) for analyzing wave signals and predict rock properties such as crack density(CD) – the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid(RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks(CNN) and Long Short-Term Memory(LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2> 96% with mean square error(MSE) < 25e-4(normalized values). Overall, we found that:(i) performance of both CNN and LSTM is highly promising,(ii) accuracy of the transmitted signals is slightly higher than the reflected signals,(iii) accuracy of 2D signals is marginally higher than 1D signals,(iv)accuracy of horizontal and vertical component signals are comparable,(v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling.
文摘Large underground caverns are commonly used in variety of applications. In many cases, because of the geomechanical limitations of dimensions and requirement of high volume, several parallel caverns are used. Plastic zone integration requires a larger rock pillar distance of theses adjacent caverns while eco- nomic and access reasons require smaller distance. In lran many underground projects are located in West and South West, Asmari formation covers a large part of these regions. The stability of underground spaces that are constructed or will be constructed in this formation has been investigated. A proper cross section based on plastic analysis and a stability criterion has been proposed for each region. Finally, in each case, allowable rock pillar between adjacent caverns with similar dimension was determined with two methods （numerical analysis and fire service law）. Results show that Fire Service Law uses a very con- servative safety factor and it was proposed to use a correction factor for allowable distance based on application of underground space.
文摘Crack growth modeling has always been one of the major challenges in fracture mechanics.Among all numerical methods,the extended finite element method(XFEM)has recently attracted much attention due to its ability to estimate the discontinuous deformation field.However,XFEM modeling does not directly lead to reliable results,and choosing a strategy of implementation is inevitable,especially in porous media.In this study,two prevalent XFEM strategies are evaluated:a)applying reduced Young’s modulus to pores and b)using different partitions to the model and enriching each part individually.We mention the advantages and limitations of each strategy via both analytical and experimental validations.Finally,the crack growth is modeled in a natural porous media(Fontainebleau sandstone).Our investigations proved that although both strategies can identically predict the stress distribution in the sample,the first strategy simulates only the initial crack propagation,while the second strategy could model multiple cracks growths.Both strategies are reliable and highly accurate in calculating the stress intensity factor,but the second strategy can compute a more reliable reaction force.Experimental tests showed that the second strategy is a more accurate strategy in predicting the preferred crack growth path and determining the maximum strength of the sample.
