The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly ...The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly related to the frequency and distribution of discontinuities in the rock mass and quantified by rock quality designation(RQD).This paper analyzes the data of hydraulic conductivity and discontinuities sampled at different depths during the borehole investigations in the limestone and sandstone formations for the construction of hydraulic structures in Oman.Cores recovered from boreholes provide RQD data,and in situ Lugeon tests elucidate the permeability.A modern technique of multivariate adaptive regression splines(MARS)assisted in correlating permeability and RQD along with the depth.In situ permeability shows a declining trend with increasing RQD,and the depth of investigation is within 50 m.This type of relationship can be developed based on detailed initial investigations at the site where the hydraulic conductivity of discontinuous rocks is required to be delineated.The relationship can approximate the permeability by only measuring the RQD in later investigations on the same site,thus saving the time and cost of the site investigations.The applicability of the relationship developed in this study to another location requires a lithological similarity of the rock mass that can be verified through preliminary investigation at the site.展开更多
Rock quality designation(RQD)has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling.Anisotropy index of jointing degree(AI_...Rock quality designation(RQD)has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling.Anisotropy index of jointing degree(AI_(jd))was formulated by Zheng et al.(2018)by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space.In accordance with spacing terminology by ISRM(1981),defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD.To overcome this limitation,theoretical RQD(TRQD_(t))introduced by Priest and Hudson(1976)can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution.Anisotropy index of the jointing degree was improved using TRQD_(t) which was adjusted to wider joint spacing by considering Priest(1993)’s recommendation on the use of variable threshold value(t)in TRQD_(t) formulation.After applications of the improved anisotropy index of a jointing degree(AI'_(jd))to hypothetical jointed rock mass cases,the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AI'_(jd) formulation by considering the ratings of persistency of joints as proposed by Bieniawski(1989)’s rock mass rating(RMR)classification.Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree(W_AI'_(jd)).A structural anisotropy classification was developed using the RQD classification proposed by Deere(1963).The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.展开更多
Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep...Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.展开更多
The Qilian Mountain permafrost area located in the northern of Qinghai-Tibet Plateau is a favorable place for natural gas hydrate formation and enrichment,due to its well-developed fractures and abundant gas sources.U...The Qilian Mountain permafrost area located in the northern of Qinghai-Tibet Plateau is a favorable place for natural gas hydrate formation and enrichment,due to its well-developed fractures and abundant gas sources.Understanding the formation and distribution of multi-component gas hydrates in fractures is crucial in accurately evaluating the hydrate reservoir resources in this area.The hydrate formation experiments were carried out using the core samples drilled from hydrate-bearing sediments in Qilian Mountain permafrost area and the multi-component gas with similar composition to natural gas hydrates in Qilian Mountain permafrost area.The formation and distribution characteristics of multi-component gas hydrates in core samples were observed in situ by X-ray Computed Tomography(X-CT)under high pressure and low temperature conditions.Results show that hydrates are mainly formed and distributed in the fractures with good connectivity.The ratios of volume of hydrates formed in fractures to the volume of fractures are about 96.8%and 60.67%in two different core samples.This indicates that the fracture surface may act as a favorable reaction site for hydrate formation in core samples.Based on the field geological data and the experimental results,it is preliminarily estimated that the inventory of methane stored in the fractured gas hydrate in Qilian Mountain permafrost area is about 8.67×1013 m3,with a resource abundance of 8.67×108 m3/km2.This study demonstrates the great resource potential of fractured gas hydrate and also provides a new way to further understand the prospect of natural gas hydrate and other oil and gas resources in Qilian Mountain permafrost area.展开更多
Purpose-This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.Design/methodology/approach-First,taking the Liuyuan Tunnel of Huangg...Purpose-This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.Design/methodology/approach-First,taking the Liuyuan Tunnel of Huanggang-Huangmei High-Speed Railway as an example and taking deflection of the third principal stress of the surrounding rock at a vault after tunnel excavation as the criterion,the critical buried depth of the large section tunnel was determined.Then,the strength reduction method was employed to calculate the tunnel safety factor under different rock classes and thickness-span ratios,and mathematical statistics was conducted to identify the relationships of the tunnel safety factor with the thickness-span ratio and the basic quality(BQ)index of the rock for different rock classes.Finally,the influences of thickness-span ratio,groundwater,initial stress of rock and structural attitude factors were considered to obtain the corrected BQ,based on which the stability of a large cross-section tunnel with a depth of more than 100 m during mechanized operation was analyzed.This evaluation method was then applied to Liuyuan Tunnel and Cimushan No.2 Tunnel of Chongqing Urban Expressway for verification.Findings-This study shows that under different rock classes,the tunnel safety factor is a strict power function of the thickness-span ratio,while a linear function of the BQ to some extent.It is more suitable to use the corrected BQ as a quantitative index to evaluate tunnel stability according to the actual conditions of the site.Originality/value-The existing industry standards do not consider the influence of buried depth and span in the evaluation of tunnel stability.The stability evaluation method of large section tunnel considering the correction of overburden span ratio proposed in this paper achieves higher accuracy for the stability evaluation of surrounding rock in a full or large-section mechanized excavation of double line high-speed railway tunnels.展开更多
Extensive research has confirmed the successful prediction of rock mass quality in tunnel boring machine(TBM)construction using machine learning methods based on big data collected during the boring process.However,th...Extensive research has confirmed the successful prediction of rock mass quality in tunnel boring machine(TBM)construction using machine learning methods based on big data collected during the boring process.However,the developed model cannot be applied to a new project owing to the different mechanical and environmental features involved in different projects.This study tries to combine the datasets of three TBM projects whose cutterhead diameters are 5.2,7.9 and 9.8 m,respectively.In this study,machine learning focused on predictions of a binary rock mass quality system was implemented using this unified dataset by adding the diameter and disc cutter number as new attributes into the input.The process consists of:(1)individual learning for the three respective datasets,(2)shuffled learning for the unified dataset containing randomly distributed information from the three projects,and(3)crossed learning aimed at validating that the algorithm developed on the unified dataset can produce predictions with equally acceptable accuracies as those obtained in the individual learning.It is anticipated that with more datasets joining this cross-project learning,we will be able to develop a machine learning algorithm that is suitable for new projects with a wide range of cutterhead diameters and disc cutter numbers at the beginning of the tunnel excavation.展开更多
This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines(TBMs).The rock mass quality ratings,which are based o...This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines(TBMs).The rock mass quality ratings,which are based on the Chinese code for geological survey,were used to provide"labels"suitable for supervised learning.As a result,the generation of machine prediction for rock mass grades reason-ably agreed with the ground truth documented in geological maps.In contrast,the main operational parameters,i.e.,thrust and torque,can be reasonably predicted based on historical data.Consequently,18 collapse sections of the Yinsong project have been successfully predicted by several researchers.Preliminary studies on the selection of the optimal penetration rate and cost were conducted.This review also presents a summary of the main achievements in response to the initiatives of the Lotus Pool Contest in China.For the first time,large and well-documented TBM performance data has been shared for joint scientific research.Moreover,the review discusses the technical problems that require further study and the perspectives in the future development of intelligent TBM construction based on big data and machine learning.展开更多
基金indebted to the Sohar University and the University of Buraimi, Oman, to support this study
文摘The assessment of in situ permeability of rock mass is challenging for large-scale projects such as reservoirs created by dams,where water tightness issues are of prime importance.The in situ permeability is strongly related to the frequency and distribution of discontinuities in the rock mass and quantified by rock quality designation(RQD).This paper analyzes the data of hydraulic conductivity and discontinuities sampled at different depths during the borehole investigations in the limestone and sandstone formations for the construction of hydraulic structures in Oman.Cores recovered from boreholes provide RQD data,and in situ Lugeon tests elucidate the permeability.A modern technique of multivariate adaptive regression splines(MARS)assisted in correlating permeability and RQD along with the depth.In situ permeability shows a declining trend with increasing RQD,and the depth of investigation is within 50 m.This type of relationship can be developed based on detailed initial investigations at the site where the hydraulic conductivity of discontinuous rocks is required to be delineated.The relationship can approximate the permeability by only measuring the RQD in later investigations on the same site,thus saving the time and cost of the site investigations.The applicability of the relationship developed in this study to another location requires a lithological similarity of the rock mass that can be verified through preliminary investigation at the site.
基金supports from the General Directorate of ETIMADEN enterprises during the field studies at Simav open pit mine。
文摘Rock quality designation(RQD)has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling.Anisotropy index of jointing degree(AI_(jd))was formulated by Zheng et al.(2018)by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space.In accordance with spacing terminology by ISRM(1981),defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD.To overcome this limitation,theoretical RQD(TRQD_(t))introduced by Priest and Hudson(1976)can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution.Anisotropy index of the jointing degree was improved using TRQD_(t) which was adjusted to wider joint spacing by considering Priest(1993)’s recommendation on the use of variable threshold value(t)in TRQD_(t) formulation.After applications of the improved anisotropy index of a jointing degree(AI'_(jd))to hypothetical jointed rock mass cases,the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AI'_(jd) formulation by considering the ratings of persistency of joints as proposed by Bieniawski(1989)’s rock mass rating(RMR)classification.Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree(W_AI'_(jd)).A structural anisotropy classification was developed using the RQD classification proposed by Deere(1963).The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.
基金supported by the National Natural Science Foundation of China(Grant Nos.51979253,51879245)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant No.CUGCJ1821).
文摘Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.
基金the financial support of the National Natural Science Foundation of China(42176212,41976074 and 41302034)the Marine S&T Fund of Shandong Province for Laoshan Laboratory(2021QNLM020002)the Marine Geological Survey Program(DD20221704)。
文摘The Qilian Mountain permafrost area located in the northern of Qinghai-Tibet Plateau is a favorable place for natural gas hydrate formation and enrichment,due to its well-developed fractures and abundant gas sources.Understanding the formation and distribution of multi-component gas hydrates in fractures is crucial in accurately evaluating the hydrate reservoir resources in this area.The hydrate formation experiments were carried out using the core samples drilled from hydrate-bearing sediments in Qilian Mountain permafrost area and the multi-component gas with similar composition to natural gas hydrates in Qilian Mountain permafrost area.The formation and distribution characteristics of multi-component gas hydrates in core samples were observed in situ by X-ray Computed Tomography(X-CT)under high pressure and low temperature conditions.Results show that hydrates are mainly formed and distributed in the fractures with good connectivity.The ratios of volume of hydrates formed in fractures to the volume of fractures are about 96.8%and 60.67%in two different core samples.This indicates that the fracture surface may act as a favorable reaction site for hydrate formation in core samples.Based on the field geological data and the experimental results,it is preliminarily estimated that the inventory of methane stored in the fractured gas hydrate in Qilian Mountain permafrost area is about 8.67×1013 m3,with a resource abundance of 8.67×108 m3/km2.This study demonstrates the great resource potential of fractured gas hydrate and also provides a new way to further understand the prospect of natural gas hydrate and other oil and gas resources in Qilian Mountain permafrost area.
基金supported by the NSFC HSR Fundamental Research Joint Fund (Grant No.U1934213)。
文摘Purpose-This study aims to research the large cross-section tunnel stability evaluation method corrected after considering the thickness-span ratio.Design/methodology/approach-First,taking the Liuyuan Tunnel of Huanggang-Huangmei High-Speed Railway as an example and taking deflection of the third principal stress of the surrounding rock at a vault after tunnel excavation as the criterion,the critical buried depth of the large section tunnel was determined.Then,the strength reduction method was employed to calculate the tunnel safety factor under different rock classes and thickness-span ratios,and mathematical statistics was conducted to identify the relationships of the tunnel safety factor with the thickness-span ratio and the basic quality(BQ)index of the rock for different rock classes.Finally,the influences of thickness-span ratio,groundwater,initial stress of rock and structural attitude factors were considered to obtain the corrected BQ,based on which the stability of a large cross-section tunnel with a depth of more than 100 m during mechanized operation was analyzed.This evaluation method was then applied to Liuyuan Tunnel and Cimushan No.2 Tunnel of Chongqing Urban Expressway for verification.Findings-This study shows that under different rock classes,the tunnel safety factor is a strict power function of the thickness-span ratio,while a linear function of the BQ to some extent.It is more suitable to use the corrected BQ as a quantitative index to evaluate tunnel stability according to the actual conditions of the site.Originality/value-The existing industry standards do not consider the influence of buried depth and span in the evaluation of tunnel stability.The stability evaluation method of large section tunnel considering the correction of overburden span ratio proposed in this paper achieves higher accuracy for the stability evaluation of surrounding rock in a full or large-section mechanized excavation of double line high-speed railway tunnels.
基金supported by the Core Research Project of Power Construction Corporation of China(Grant No.DJ-HXGG-2021-01)the Basic Research Project of the China Institute of Water Resources and Hydropower Research(Grant No.GE0145B022021)the Natural Science Foundation of Shaanxi Province(Grant No.2021JLM-50)。
文摘Extensive research has confirmed the successful prediction of rock mass quality in tunnel boring machine(TBM)construction using machine learning methods based on big data collected during the boring process.However,the developed model cannot be applied to a new project owing to the different mechanical and environmental features involved in different projects.This study tries to combine the datasets of three TBM projects whose cutterhead diameters are 5.2,7.9 and 9.8 m,respectively.In this study,machine learning focused on predictions of a binary rock mass quality system was implemented using this unified dataset by adding the diameter and disc cutter number as new attributes into the input.The process consists of:(1)individual learning for the three respective datasets,(2)shuffled learning for the unified dataset containing randomly distributed information from the three projects,and(3)crossed learning aimed at validating that the algorithm developed on the unified dataset can produce predictions with equally acceptable accuracies as those obtained in the individual learning.It is anticipated that with more datasets joining this cross-project learning,we will be able to develop a machine learning algorithm that is suitable for new projects with a wide range of cutterhead diameters and disc cutter numbers at the beginning of the tunnel excavation.
基金supported by grants from the National Key R&D Program of China(Grant No.2018YFB1702504)the National Natural Science Foundation of China(Grant Nos.52179121,51879284)+3 种基金the State Key Laboratory of Simulations and Regulation of Water Cycle in River Basin,China(Grant No.SKL2022ZD05)the IWHR Research&Development Support Program,China(Grant No.GE0145B012021)the Natural Science Foundation of Shaanxi Province,China(Grant No.2021JLM-50)the National Key R&D Program of China(Grant No.2022YFE0200400).
文摘This review discusses the application scenarios of the machine learning-supported performance prediction and the optimization effi-ciency of tunnel boring machines(TBMs).The rock mass quality ratings,which are based on the Chinese code for geological survey,were used to provide"labels"suitable for supervised learning.As a result,the generation of machine prediction for rock mass grades reason-ably agreed with the ground truth documented in geological maps.In contrast,the main operational parameters,i.e.,thrust and torque,can be reasonably predicted based on historical data.Consequently,18 collapse sections of the Yinsong project have been successfully predicted by several researchers.Preliminary studies on the selection of the optimal penetration rate and cost were conducted.This review also presents a summary of the main achievements in response to the initiatives of the Lotus Pool Contest in China.For the first time,large and well-documented TBM performance data has been shared for joint scientific research.Moreover,the review discusses the technical problems that require further study and the perspectives in the future development of intelligent TBM construction based on big data and machine learning.
