Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC...Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.展开更多
The scale effect on shear strength of rock joints is well-documented.However,whether scale effects are negative,positive,or even exist or not is still controversial.Joint roughness significantly influences the shear s...The scale effect on shear strength of rock joints is well-documented.However,whether scale effects are negative,positive,or even exist or not is still controversial.Joint roughness significantly influences the shear strength of rock joints.Compared to the shear tests,using the joint roughness coefficient(JRC)and its roughness parameters offers a more convenient method for describing the scale effect on shear strength.However,it is crucial to understand that the scale effect mechanisms of JRC are distinct from those of shear strength.Therefore,this paper aims to clarify these distinct mechanisms.By digitally extracting roughness parameters from granite samples,it is found that the scale effect of roughness parameters mainly comes from the sampling methods and the geometric characteristics of parameters.Furthermore,a full data sampling method considering heterogeneity is proposed to obtain more representative roughness parameters.To reveal the scale effect mechanisms of shear strength,Gaussian filtering is firstly used to separate the waviness and unevenness components of roughness,facilitating a deeper understanding of the geometric characteristics of roughness.It is suggested that the wavelength of the waviness component can reflect the scale effect on shear strength.Secondly,numerical simulations of ideal artificial joint models are conducted to validate that the wavelength of the waviness component serves as the dividing point between positive and negative scale effects.The mechanical mechanisms of positive and negative scale effects are also interpreted.Finally,these mechanisms successfully elucidate the occurrence patterns of the scale effect on natural joint profiles.展开更多
The joint roughness coefficient(JRC) is one of the key parameters for evaluating the shear strength of rock joints.Because of the scale effect in the JRC,reliable JRC values are of great importance for most rock engin...The joint roughness coefficient(JRC) is one of the key parameters for evaluating the shear strength of rock joints.Because of the scale effect in the JRC,reliable JRC values are of great importance for most rock engineering projects.During the collection process of JRC samples,the redundancy or insufficiency of representative rock joint surface topography(RJST) information in serial length JRC samples is the essential reason that affects the reliability of the scale effect results.Therefore,this paper proposes an adaptive sampling method,in which we use the entropy consistency measure Q(a) to evaluate the consistency of the joint morphology information contained in adjacent JRC samples.Then the sampling interval is automatically adjusted according to the threshold Q(at) of the entropy consistency measure to ensure that the degree of change of RJST information between JRC samples is the same,and ultimately makes the representative RJST information in the collected JRC samples more balanced.The application results of actual cases show that the proposed method can obtain the scale effect in the JRC efficiently and reliably.展开更多
Joint roughness coefficient（JRC） is the key parameter for the empirical estimation of joint shear strength by using the JRC-JCS（joint wall compressive strength） model.Because JRC has such characteristics as nonuni...Joint roughness coefficient（JRC） is the key parameter for the empirical estimation of joint shear strength by using the JRC-JCS（joint wall compressive strength） model.Because JRC has such characteristics as nonuniformity,anisotropy,and unhomogeneity,directional statistical measurement of JRC is the precondition for ensuring the reliability of the empirical estimation method.However,the directional statistical measurement of JRC is time-consuming.In order to present an ideal measurement method of JRC,new profilographs and roughness rulers were developed according to the properties of rock joint undulating shape based on the review of measurement methods of JRC.Operation methods of the profilographs and roughness rulers were also introduced.A case study shows that the instruments and operation methods produce an effective means for the statistical measurement of JRC.展开更多
The joint roughness coefficient (JRC), introduced in Barton (1973) represented a new method in rock mechanics and rock engineering to deal with problems related to joint roughness and shear strength estimation. It has...The joint roughness coefficient (JRC), introduced in Barton (1973) represented a new method in rock mechanics and rock engineering to deal with problems related to joint roughness and shear strength estimation. It has the advantages of its simple form, easy estimation, and explicit consideration of scale effects, which make it the most widely accepted parameter for roughness quantification since it was proposed. As a result, JRC has attracted the attention of many scholars who have developed JRC-related methods in many areas, such as geological engineering, multidisciplinary geosciences, mining mineral processing, civil engineering, environmental engineering, and water resources. Because of such a developing trend, an overview of JRC is presented here to provide a clear perspective on the concepts, methods, applications, and trends related to its extensions. This review mainly introduces the origin and connotation of JRC, JRC-related roughness measurement, JRC estimation methods, JRC-based roughness characteristics investigation, JRC-based rock joint property description, JRC's influence on rock mass properties, and JRC-based rock engineering applications. Moreover, the representativeness of the joint samples and the determination of the sampling interval for rock joint roughness measurements are discussed. In the future, the existing JRC-related methods will likely be further improved and extended in rock engineering.展开更多
To determinate the water diffusion coefficients and dynamics in adhesive/carben fiber reinforced epoxy resin composite joints, energy dispersive X-ray spectroscopy analysis（EDX） is used to establish the content chan...To determinate the water diffusion coefficients and dynamics in adhesive/carben fiber reinforced epoxy resin composite joints, energy dispersive X-ray spectroscopy analysis（EDX） is used to establish the content change of oxy- gen in the adhesive in adhesive/carbon fther reinforced epoxy resin composite joints. As water is made up of oxygen and hydrogen, the water diffusion coefficients and dynamics in adhesive/carben fiber reinforced epoxy resin composite joints can be obtained from the change in the content of oxygen in the adhesive during humidity aging, via EDX analy-sis. The authors have calculated the water diffusion coefficients and dynamics in the adhesive/carbon fiber reinforced epoxy resin composite joints with the aid of beth energy dispersive X-ray spectroscopy and elemental analysis. The de- termined results with EDX analysis are almost the same as those determined with elemental analysis and the results al- so show that the durability of the adhesive/carbon fther reinforced epoxy resin composite joints subjected to silane cou- pling agent treatment is better than those subjected to sand paper burnishing treatment and chemical oxidation treat- ment.展开更多
A computerized method for determining rock joint coefficients is presented.Two relative similarity indicators are introduced to classify surface morphology of rock joints.The classification enables to compare investig...A computerized method for determining rock joint coefficients is presented.Two relative similarity indicators are introduced to classify surface morphology of rock joints.The classification enables to compare investigated and database rock joints.Such a comparison aims at finding the couple of surfaces that are distinguished by the highest dynamical conformity.The first absolute indicator results from the Fourier matrix and evaluates wavy shapes of surfaces.The second absolute indicator quantifies the heights of surface reliefs and is defined as the root mean square height of the surface outline.Numerical reliability of these indicators is tested within the surface analysis of a series of limestone specimens.Besides the computerized assessment,25 people have performed visual assessment of these limestone specimens.The results of visual assessments have been statistically processed and compared to the results received from the computerized procedure.The newly introduced absolute indicators have proved to be prospective numerical tools for evaluating joint rock coefficients.展开更多
Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass.Therefore,the joint roughness coefficient(JRC)estimation is of paramount importance in geomechanics engineering applicat...Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass.Therefore,the joint roughness coefficient(JRC)estimation is of paramount importance in geomechanics engineering applications.Studies show that the application of statistical parameters alone may not produce a sufficiently reliable estimation of the JRC values.Therefore,alternative data-driven methods are proposed to assess the JRC values.In this study,Gaussian process(GP),K-star,random forest(RF),and extreme gradient boosting(XGBoost)models are employed,and their performance and accuracy are compared with those of benchmark regression formula(i.e.Z2,Rp,and SDi)for the JRC estimation.To analyze the models’performance,112 rock joint profile datasets having eight common statistical parameters(R_(ave),R_(max),SD_(h),iave,SD_(i),Z_(2),R_(p),and SF)and one output variable(JRC)are utilized,of which 89 and 23 datasets are used for training and validation of models,respectively.The interpretability of the developed XGBoost model is presented in terms of feature importance ranking,partial dependence plots(PDPs),feature interaction,and local interpretable model-agnostic explanations(LIME)techniques.Analyses of results show that machine learning models demonstrate higher accuracy and precision for estimating JRC values compared with the benchmark empirical equations,indicating the generalization ability of the data-driven models in better estimation accuracy.展开更多
The coefficient and dynamics of water diffusion in adhesive-graphite joints were calculated in-situ with energy dispersive X-ray (EDX) analysis, a method that is significantly simpler than elemental analysis. Water di...The coefficient and dynamics of water diffusion in adhesive-graphite joints were calculated in-situ with energy dispersive X-ray (EDX) analysis, a method that is significantly simpler than elemental analysis. Water diffusion coefficient and dynamics of adhesive-graphite joints treated by different surface treatment methods were also investigated. Calculation results indicated that the water diffusion rate in adhesive-graphite joints treated by sandpaper was higher than that treated by chemical oxidation or by silane couple agent. Also the durability of graphite joints treated by coupling agent is superior to that treated by chemical oxidation or sandpaper burnishing.展开更多
It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment.The highly deterministic maximum likelihood estimator has a high accuracy,but th...It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment.The highly deterministic maximum likelihood estimator has a high accuracy,but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method.In this paper,a robust es-timation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets.The compound reflection coefficient is es-timated from the received data of the array and then a one-di-mension generalized steering vector is constructed to estimate the target height.The algorithm is robust to the reflecting sur-face height error and the ground reflection coefficient error.Fi-nally,the experiment and simulation results demonstrate the validity of the proposed method.展开更多
To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network...To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge.However,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications.To address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper.Then a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time delay.The EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading decision.The offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor stability.Thus,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the dilemma.The proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini coefficient.The simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher EoU.The simulation code of the proposed algorithm is available:https://github.com/Grox888/Mobile_Edge_Computing/tree/GCAGA.展开更多
Rock joints always have a smaller strength,and it plays an important influence on the overall strength of rock mass.The mechanical behavior of rock joints is mainly governed by the surface topography,normal stress,and...Rock joints always have a smaller strength,and it plays an important influence on the overall strength of rock mass.The mechanical behavior of rock joints is mainly governed by the surface topography,normal stress,and failure degree.In this study,a series of direct shear tests for four different rough rock joints under five normal stresses was carried out.The shear and normal stiffnesses were first determined,and the shear shrinkage effect was represented by a shear-normal coupling coefficient.Assuming that the strength of the joint is composed of frictional and cohesive parts,the evolutions of cohesion,friction angle with joint roughness coefficient(JRC),and plastic shear displacement are obtained.The dilatancy behavior is described by the dilation angle,which is considered a function of JRC,plastic shear displacement,and normal stress.A cohesive-frictional elastoplastic constitutive model is hence proposed.The theoretical curves under constant normal stress conditions of the proposed model are in good agreement with the experimental results.The shear behaviors under constant normal stiffness and constant normal displacement conditions can be predicted using the new constitutive model.展开更多
The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optim...The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.展开更多
Joints are widely distributed structural defects in rock masses,and their geometric characteristics play a decisive role in the overall stability of rocks under complex stress conditions.To clarify the influence of jo...Joints are widely distributed structural defects in rock masses,and their geometric characteristics play a decisive role in the overall stability of rocks under complex stress conditions.To clarify the influence of joint geometry on the mechanical behavior of jointed rock under such conditions,this study investigated the mechanical properties and failure mechanisms of composite jointed rock specimens with varying joint roughness and joint dip angles.Three typical failure modes under triaxial loading were identified,and a mechanical analysis model incorporating joint roughness and dip angle was established.The failure mechanism was revealed,and a discrete element model was developed to analyze the micro-damage evolution process of the specimens.The results show that the mechanical parameters of the specimens exhibit pronounced anisotropy.Both the elastic modulus and peak strength reach their minimum values at a joint dip angle of 60°.Increasing joint roughness significantly reduces the degree of anisotropy and enhances the energy storage capacity of the specimens.A strong linear relationship is observed between the elastic strain energy and the peak deviatoric stress,confirming the applicability of the linear energy storage law in composite jointed rocks.Discrete element simulations revealed the evolution path and dominant types of microcracks between the joint and matrix.The joint dip angle governs the transition of dominant crack types from tensile to shear and then back to tensile.Increased joint roughness significantly suppresses damage localization along the joint and results in an approximately 20%increase in the proportion of shear microcracks within the matrix.These findings clarify the regulatory role of joint geometrical parameters in the damage evolution process.展开更多
Understanding the shear mechanical behaviors and instability mechanisms of rock joints under dynamic loading remains a complex challenge.This research conducts a series of direct shear tests on real rock joints subjec...Understanding the shear mechanical behaviors and instability mechanisms of rock joints under dynamic loading remains a complex challenge.This research conducts a series of direct shear tests on real rock joints subjected to cyclic normal loads to assess the influence of dynamic normal loading amplitude(F_(d)),dynamic normal loading frequency(f_(v)),initial normal loading(F_(s)),and the joint roughness coefficient(JRC)on the mechanical properties and instability responses of these joints.The results show that unstable sliding is often accompanied by friction weakening due to dynamic normal loads.A significant negative correlation exists between cyclic normal loads and the normal displacement during the shearing process.Dynamic normal load paths vary the contact states of asperities on the rough joint surfaces,impacting the stick-slip instability mechanism of the joints,which in turn affects both the magnitude and location of the stress drop during the stick-slip events,particularly during the unloading phases.An increasing F_(d) results in a more stable shearing behavior and a reduction in the amplitude of stick-slip stress drops.The variation in f_(v) influences the amplitude of stress drop for the joints during shear,characterized by an initial decrease(f_(v)=0.25-2 Hz)before exhibiting an increment(f_(v)=2-4 Hz).As F_(s) increases,sudden failures of the interlocked rough surfaces are more prone to occur,thus producing enhanced instability and a more substantial stress drop.Additionally,a larger JRC intensifies the instability of the joints,which would induce a more pronounced decline in the stick-slip stress.The Rate and state friction(RSF)law can provide an effective explanation for the unstable sliding phenomena of joints during the oscillations of normal loads.The findings may provide certain useful references for a deeper comprehension of the sliding behaviors exhibited by rock joints when subjected to cyclic dynamic disturbances.展开更多
Multi-component exploration has many advantages over ordinary P-wave exploration. PP/PS joint AVO analysis and inversion are useful and powerful methods to discriminate between reservoir and non-productive lithology. ...Multi-component exploration has many advantages over ordinary P-wave exploration. PP/PS joint AVO analysis and inversion are useful and powerful methods to discriminate between reservoir and non-productive lithology. In this paper, we derive a new PS-wave reflection coefficient approximation equation which is more accurate at larger incidence angles. The equation is simplified for small incidence angles, which makes AVO analysis clearer and easier for angles less than 30 degrees. Based on this approximation, a PP/PS joint inversion is introduced. A real data example shows that oil sands, brine sands and shales can be differentiated based on the P- to S-wave velocity ratio from the PP/PS joint inversion. Fluid factors and Poisson＇s ratio also indicate an anomaly in the target zone at the oil well location.展开更多
A recently developed computerized method for assessing the rock joint coefficients is discussed. The performances of formerly introduced relative similarity indicators, along with the correlation coefficient, are subj...A recently developed computerized method for assessing the rock joint coefficients is discussed. The performances of formerly introduced relative similarity indicators, along with the correlation coefficient, are subjected to critical analysis. These relative numerical indicators are replaced by two absolute indicators whose properties better describe surface textures of rock joints. The first absolute indicator results from the Fourier Matrix and evaluates wavy shapes of surfaces. The second absolute indicator quantifies the heights of surface reliefs, and is defined as the root mean square height of the surface outline. The behavior of the newly introduced numerical indicators are investigated by means of the deterministic periodic surface reliefs. The practical application of the new indicators is presented and the convenient performances of both the indicators are documented.展开更多
Accurate measurement of the evolution of rock joint void geometry is essential for comprehending the distribution characteristics of asperities responsible for shear and seepage behaviors.However,existing techniques o...Accurate measurement of the evolution of rock joint void geometry is essential for comprehending the distribution characteristics of asperities responsible for shear and seepage behaviors.However,existing techniques often require specialized equipment and skilled operators,posing practical challenges.In this study,a cost-effective photogrammetric approach is proposed.Particularly,local coordinate systems are established to facilitate the alignment and precise quantification of the relative position between two halves of a rock joint.Push/pull tests are conducted on rock joints with varying roughness levels to induce different contact states.A high-precision laser scanner serves as a benchmark for evaluating the photogrammetry method.Despite certain deviations exist,the measured evolution of void geometry is generally consistent with the qualitative findings of previous studies.The photogrammetric measurements yield comparable accuracy to laser scanning,with maximum errors of 13.2%for aperture and 14.4%for void volume.Most joint matching coefficient(JMC)measurement errors are below 20%.Larger measurement errors occur primarily in highly mismatched rock joints with JMC values below 0.2,but even in cases where measurement errors exceed 80%,the maximum JMC error is only 0.0434.Thus,the proposed photogrammetric approach holds promise for widespread application in void geometry measurements in rock joints.展开更多
Direct shear tests were conducted on the rock joints under constant normal load(CNL), while the acoustic emission(AE) signals generated during shear tests were monitored with PAC Micro-II system. Before and after shea...Direct shear tests were conducted on the rock joints under constant normal load(CNL), while the acoustic emission(AE) signals generated during shear tests were monitored with PAC Micro-II system. Before and after shearing, the surfaces of rock joints were measured by the Talysurf CLI 2000. By correlating the AE events with the shear stress-shear displacement curve, one can observe four periods of the whole course of shearing of rock joints. By the contrast of AE location and actual damage zone, it is elucidated that the AE event is related to the morphology of the joint. With the increase of shearing times, the shear behavior of rock joints gradually presents from the response of brittle behavior to that of ductile behavior. By combining the results of topography measurement, four morphological parameters of joint surface, S p(the maximum height of joint surface), N(number of islands), A(projection area) and V(volume of joint) were introduced, which decrease with shearing. Both the joint roughness coefficient(JRC) and joint matching coefficient(JMC) drop with shearing, and the shear strength of rock joints can be predicted by the JRC-JMC model. It establishes the relationship between micro-topography and macroscopic strength, which have the same change rule with shearing.展开更多
基金funding from the National Natural Science Foundation of China (Grant No.42277175)the pilot project of cooperation between the Ministry of Natural Resources and Hunan Province“Research and demonstration of key technologies for comprehensive remote sensing identification of geological hazards in typical regions of Hunan Province” (Grant No.2023ZRBSHZ056)the National Key Research and Development Program of China-2023 Key Special Project (Grant No.2023YFC2907400).
文摘Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.
基金funded by the National Natural Science Foundation Projects(Grant Nos.41772287 and 42277132)the Key R&D Project of Zhejiang Province(Grant No.2021C03159).
文摘The scale effect on shear strength of rock joints is well-documented.However,whether scale effects are negative,positive,or even exist or not is still controversial.Joint roughness significantly influences the shear strength of rock joints.Compared to the shear tests,using the joint roughness coefficient(JRC)and its roughness parameters offers a more convenient method for describing the scale effect on shear strength.However,it is crucial to understand that the scale effect mechanisms of JRC are distinct from those of shear strength.Therefore,this paper aims to clarify these distinct mechanisms.By digitally extracting roughness parameters from granite samples,it is found that the scale effect of roughness parameters mainly comes from the sampling methods and the geometric characteristics of parameters.Furthermore,a full data sampling method considering heterogeneity is proposed to obtain more representative roughness parameters.To reveal the scale effect mechanisms of shear strength,Gaussian filtering is firstly used to separate the waviness and unevenness components of roughness,facilitating a deeper understanding of the geometric characteristics of roughness.It is suggested that the wavelength of the waviness component can reflect the scale effect on shear strength.Secondly,numerical simulations of ideal artificial joint models are conducted to validate that the wavelength of the waviness component serves as the dividing point between positive and negative scale effects.The mechanical mechanisms of positive and negative scale effects are also interpreted.Finally,these mechanisms successfully elucidate the occurrence patterns of the scale effect on natural joint profiles.
基金supported by the National Natural Science Foundation of China(No.42207175)。
文摘The joint roughness coefficient(JRC) is one of the key parameters for evaluating the shear strength of rock joints.Because of the scale effect in the JRC,reliable JRC values are of great importance for most rock engineering projects.During the collection process of JRC samples,the redundancy or insufficiency of representative rock joint surface topography(RJST) information in serial length JRC samples is the essential reason that affects the reliability of the scale effect results.Therefore,this paper proposes an adaptive sampling method,in which we use the entropy consistency measure Q(a) to evaluate the consistency of the joint morphology information contained in adjacent JRC samples.Then the sampling interval is automatically adjusted according to the threshold Q(at) of the entropy consistency measure to ensure that the degree of change of RJST information between JRC samples is the same,and ultimately makes the representative RJST information in the collected JRC samples more balanced.The application results of actual cases show that the proposed method can obtain the scale effect in the JRC efficiently and reliably.
基金supported by the National Natural Science Foundation of China (Nos. 40672186, 50809059)the Natural Science Foundation of Zhejiang Province (No.Y505008)
文摘Joint roughness coefficient（JRC） is the key parameter for the empirical estimation of joint shear strength by using the JRC-JCS（joint wall compressive strength） model.Because JRC has such characteristics as nonuniformity,anisotropy,and unhomogeneity,directional statistical measurement of JRC is the precondition for ensuring the reliability of the empirical estimation method.However,the directional statistical measurement of JRC is time-consuming.In order to present an ideal measurement method of JRC,new profilographs and roughness rulers were developed according to the properties of rock joint undulating shape based on the review of measurement methods of JRC.Operation methods of the profilographs and roughness rulers were also introduced.A case study shows that the instruments and operation methods produce an effective means for the statistical measurement of JRC.
基金funded by the National Natural Science Foun-dation of China(Grant Nos.42177117 and 42207175)Zhejiang Provincial Natural Science Foundation(Grant No.LQ16D020001).
文摘The joint roughness coefficient (JRC), introduced in Barton (1973) represented a new method in rock mechanics and rock engineering to deal with problems related to joint roughness and shear strength estimation. It has the advantages of its simple form, easy estimation, and explicit consideration of scale effects, which make it the most widely accepted parameter for roughness quantification since it was proposed. As a result, JRC has attracted the attention of many scholars who have developed JRC-related methods in many areas, such as geological engineering, multidisciplinary geosciences, mining mineral processing, civil engineering, environmental engineering, and water resources. Because of such a developing trend, an overview of JRC is presented here to provide a clear perspective on the concepts, methods, applications, and trends related to its extensions. This review mainly introduces the origin and connotation of JRC, JRC-related roughness measurement, JRC estimation methods, JRC-based roughness characteristics investigation, JRC-based rock joint property description, JRC's influence on rock mass properties, and JRC-based rock engineering applications. Moreover, the representativeness of the joint samples and the determination of the sampling interval for rock joint roughness measurements are discussed. In the future, the existing JRC-related methods will likely be further improved and extended in rock engineering.
基金Supported by Commission of Science Technology and Industry for National Defense of China(No.JPPT-115-477).
文摘To determinate the water diffusion coefficients and dynamics in adhesive/carben fiber reinforced epoxy resin composite joints, energy dispersive X-ray spectroscopy analysis（EDX） is used to establish the content change of oxy- gen in the adhesive in adhesive/carbon fther reinforced epoxy resin composite joints. As water is made up of oxygen and hydrogen, the water diffusion coefficients and dynamics in adhesive/carben fiber reinforced epoxy resin composite joints can be obtained from the change in the content of oxygen in the adhesive during humidity aging, via EDX analy-sis. The authors have calculated the water diffusion coefficients and dynamics in the adhesive/carbon fiber reinforced epoxy resin composite joints with the aid of beth energy dispersive X-ray spectroscopy and elemental analysis. The de- termined results with EDX analysis are almost the same as those determined with elemental analysis and the results al- so show that the durability of the adhesive/carbon fther reinforced epoxy resin composite joints subjected to silane cou- pling agent treatment is better than those subjected to sand paper burnishing treatment and chemical oxidation treat- ment.
基金the Grant Agency of the Czech Republic under contract No.13-03403S.
文摘A computerized method for determining rock joint coefficients is presented.Two relative similarity indicators are introduced to classify surface morphology of rock joints.The classification enables to compare investigated and database rock joints.Such a comparison aims at finding the couple of surfaces that are distinguished by the highest dynamical conformity.The first absolute indicator results from the Fourier matrix and evaluates wavy shapes of surfaces.The second absolute indicator quantifies the heights of surface reliefs and is defined as the root mean square height of the surface outline.Numerical reliability of these indicators is tested within the surface analysis of a series of limestone specimens.Besides the computerized assessment,25 people have performed visual assessment of these limestone specimens.The results of visual assessments have been statistically processed and compared to the results received from the computerized procedure.The newly introduced absolute indicators have proved to be prospective numerical tools for evaluating joint rock coefficients.
文摘Joint roughness is one of the most important issues in the hydromechanical behavior of rock mass.Therefore,the joint roughness coefficient(JRC)estimation is of paramount importance in geomechanics engineering applications.Studies show that the application of statistical parameters alone may not produce a sufficiently reliable estimation of the JRC values.Therefore,alternative data-driven methods are proposed to assess the JRC values.In this study,Gaussian process(GP),K-star,random forest(RF),and extreme gradient boosting(XGBoost)models are employed,and their performance and accuracy are compared with those of benchmark regression formula(i.e.Z2,Rp,and SDi)for the JRC estimation.To analyze the models’performance,112 rock joint profile datasets having eight common statistical parameters(R_(ave),R_(max),SD_(h),iave,SD_(i),Z_(2),R_(p),and SF)and one output variable(JRC)are utilized,of which 89 and 23 datasets are used for training and validation of models,respectively.The interpretability of the developed XGBoost model is presented in terms of feature importance ranking,partial dependence plots(PDPs),feature interaction,and local interpretable model-agnostic explanations(LIME)techniques.Analyses of results show that machine learning models demonstrate higher accuracy and precision for estimating JRC values compared with the benchmark empirical equations,indicating the generalization ability of the data-driven models in better estimation accuracy.
文摘The coefficient and dynamics of water diffusion in adhesive-graphite joints were calculated in-situ with energy dispersive X-ray (EDX) analysis, a method that is significantly simpler than elemental analysis. Water diffusion coefficient and dynamics of adhesive-graphite joints treated by different surface treatment methods were also investigated. Calculation results indicated that the water diffusion rate in adhesive-graphite joints treated by sandpaper was higher than that treated by chemical oxidation or by silane couple agent. Also the durability of graphite joints treated by coupling agent is superior to that treated by chemical oxidation or sandpaper burnishing.
基金supported by the National Natural Science Foundation of China(61771367)the Science and Technology on Communication Networks Laboratory(6142104190204).
文摘It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment.The highly deterministic maximum likelihood estimator has a high accuracy,but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method.In this paper,a robust es-timation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets.The compound reflection coefficient is es-timated from the received data of the array and then a one-di-mension generalized steering vector is constructed to estimate the target height.The algorithm is robust to the reflecting sur-face height error and the ground reflection coefficient error.Fi-nally,the experiment and simulation results demonstrate the validity of the proposed method.
文摘To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge.However,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications.To address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper.Then a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time delay.The EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading decision.The offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor stability.Thus,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the dilemma.The proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini coefficient.The simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher EoU.The simulation code of the proposed algorithm is available:https://github.com/Grox888/Mobile_Edge_Computing/tree/GCAGA.
基金financial support from the National Natural Science Foundation of China(Grant Nos.52074269,52474157 and 51979272).
文摘Rock joints always have a smaller strength,and it plays an important influence on the overall strength of rock mass.The mechanical behavior of rock joints is mainly governed by the surface topography,normal stress,and failure degree.In this study,a series of direct shear tests for four different rough rock joints under five normal stresses was carried out.The shear and normal stiffnesses were first determined,and the shear shrinkage effect was represented by a shear-normal coupling coefficient.Assuming that the strength of the joint is composed of frictional and cohesive parts,the evolutions of cohesion,friction angle with joint roughness coefficient(JRC),and plastic shear displacement are obtained.The dilatancy behavior is described by the dilation angle,which is considered a function of JRC,plastic shear displacement,and normal stress.A cohesive-frictional elastoplastic constitutive model is hence proposed.The theoretical curves under constant normal stress conditions of the proposed model are in good agreement with the experimental results.The shear behaviors under constant normal stiffness and constant normal displacement conditions can be predicted using the new constitutive model.
基金funded by the National Natural Science Foundation of China(Grant Nos.42472345,52325905,42407202).
文摘The joint roughness coefficient(JRC)is a key parameter in the assessment of mechanical properties and the stability of rock masses.This paper presents a novel approach to JRC evaluation using a genetic algorithm-optimized backpropagation(GA-BP)neural network.Conventional JRC evaluations have typically depended on two-dimensional(2D)and three-dimensional(3D)parameter calculation methods,which fail to fully capture the nonlinear relationship between the complex surface morphology of joints and their roughness.Our analysis from shear tests on eight different joint types revealed that the strength and failure characteristics of the joints not only exhibit directional dependence but also positively correlate with surface dip angles,heights,and back slope morphological features.Subsequently,five simple statistical parameters,i.e.average dip angle,median dip angle,average height,height coefficient of variation,and back slope feature value(K),were utilized to quantify these characteristics.For the prediction of JRC,we compiled and analyzed 105 datasets,each containing these five statistical parameters and their corresponding JRC values.A GA-BP neural network model was then constructed using this dataset,with the five morphological characteristic statistics serving as inputs and the JRC values as outputs.A comparative analysis was performed between the GA-BP neural network model,the statistical parameter method,and the fractal parameter method.This analysis confirmed that our proposed method offers higher accuracy in evaluating the roughness coefficient and shear strength of joints.
基金supported by the National Natural Science Foundation of China(Nos.52304108,52274148)China University of Mining and Technology-Beijing Undergraduate Innovation Training Program(No.202515011).
文摘Joints are widely distributed structural defects in rock masses,and their geometric characteristics play a decisive role in the overall stability of rocks under complex stress conditions.To clarify the influence of joint geometry on the mechanical behavior of jointed rock under such conditions,this study investigated the mechanical properties and failure mechanisms of composite jointed rock specimens with varying joint roughness and joint dip angles.Three typical failure modes under triaxial loading were identified,and a mechanical analysis model incorporating joint roughness and dip angle was established.The failure mechanism was revealed,and a discrete element model was developed to analyze the micro-damage evolution process of the specimens.The results show that the mechanical parameters of the specimens exhibit pronounced anisotropy.Both the elastic modulus and peak strength reach their minimum values at a joint dip angle of 60°.Increasing joint roughness significantly reduces the degree of anisotropy and enhances the energy storage capacity of the specimens.A strong linear relationship is observed between the elastic strain energy and the peak deviatoric stress,confirming the applicability of the linear energy storage law in composite jointed rocks.Discrete element simulations revealed the evolution path and dominant types of microcracks between the joint and matrix.The joint dip angle governs the transition of dominant crack types from tensile to shear and then back to tensile.Increased joint roughness significantly suppresses damage localization along the joint and results in an approximately 20%increase in the proportion of shear microcracks within the matrix.These findings clarify the regulatory role of joint geometrical parameters in the damage evolution process.
基金funding support from the National Natural Science Foundation of China(Grant Nos.52174092,and 51904290)open fund of Key Laboratory of Safety and High-efficiency Coal Mining,Ministry of Education(Anhui University of Science and Technology)(Grant No.JYBSYS202311).
文摘Understanding the shear mechanical behaviors and instability mechanisms of rock joints under dynamic loading remains a complex challenge.This research conducts a series of direct shear tests on real rock joints subjected to cyclic normal loads to assess the influence of dynamic normal loading amplitude(F_(d)),dynamic normal loading frequency(f_(v)),initial normal loading(F_(s)),and the joint roughness coefficient(JRC)on the mechanical properties and instability responses of these joints.The results show that unstable sliding is often accompanied by friction weakening due to dynamic normal loads.A significant negative correlation exists between cyclic normal loads and the normal displacement during the shearing process.Dynamic normal load paths vary the contact states of asperities on the rough joint surfaces,impacting the stick-slip instability mechanism of the joints,which in turn affects both the magnitude and location of the stress drop during the stick-slip events,particularly during the unloading phases.An increasing F_(d) results in a more stable shearing behavior and a reduction in the amplitude of stick-slip stress drops.The variation in f_(v) influences the amplitude of stress drop for the joints during shear,characterized by an initial decrease(f_(v)=0.25-2 Hz)before exhibiting an increment(f_(v)=2-4 Hz).As F_(s) increases,sudden failures of the interlocked rough surfaces are more prone to occur,thus producing enhanced instability and a more substantial stress drop.Additionally,a larger JRC intensifies the instability of the joints,which would induce a more pronounced decline in the stick-slip stress.The Rate and state friction(RSF)law can provide an effective explanation for the unstable sliding phenomena of joints during the oscillations of normal loads.The findings may provide certain useful references for a deeper comprehension of the sliding behaviors exhibited by rock joints when subjected to cyclic dynamic disturbances.
基金supported by the Natural Science Foundation of China (Grant Nos 40974066 and 40821062)the National Basic Research Program of China (Grant No. 2007CB209602)
文摘Multi-component exploration has many advantages over ordinary P-wave exploration. PP/PS joint AVO analysis and inversion are useful and powerful methods to discriminate between reservoir and non-productive lithology. In this paper, we derive a new PS-wave reflection coefficient approximation equation which is more accurate at larger incidence angles. The equation is simplified for small incidence angles, which makes AVO analysis clearer and easier for angles less than 30 degrees. Based on this approximation, a PP/PS joint inversion is introduced. A real data example shows that oil sands, brine sands and shales can be differentiated based on the P- to S-wave velocity ratio from the PP/PS joint inversion. Fluid factors and Poisson＇s ratio also indicate an anomaly in the target zone at the oil well location.
基金supported by the Grant Agency of the Czech Republic (No. 13-03403S)
文摘A recently developed computerized method for assessing the rock joint coefficients is discussed. The performances of formerly introduced relative similarity indicators, along with the correlation coefficient, are subjected to critical analysis. These relative numerical indicators are replaced by two absolute indicators whose properties better describe surface textures of rock joints. The first absolute indicator results from the Fourier Matrix and evaluates wavy shapes of surfaces. The second absolute indicator quantifies the heights of surface reliefs, and is defined as the root mean square height of the surface outline. The behavior of the newly introduced numerical indicators are investigated by means of the deterministic periodic surface reliefs. The practical application of the new indicators is presented and the convenient performances of both the indicators are documented.
基金supported by the National Natural Science Foundation of China (Nos.42207175 and 42177117)the Ningbo Natural Science Foundation (No.2022J115)。
文摘Accurate measurement of the evolution of rock joint void geometry is essential for comprehending the distribution characteristics of asperities responsible for shear and seepage behaviors.However,existing techniques often require specialized equipment and skilled operators,posing practical challenges.In this study,a cost-effective photogrammetric approach is proposed.Particularly,local coordinate systems are established to facilitate the alignment and precise quantification of the relative position between two halves of a rock joint.Push/pull tests are conducted on rock joints with varying roughness levels to induce different contact states.A high-precision laser scanner serves as a benchmark for evaluating the photogrammetry method.Despite certain deviations exist,the measured evolution of void geometry is generally consistent with the qualitative findings of previous studies.The photogrammetric measurements yield comparable accuracy to laser scanning,with maximum errors of 13.2%for aperture and 14.4%for void volume.Most joint matching coefficient(JMC)measurement errors are below 20%.Larger measurement errors occur primarily in highly mismatched rock joints with JMC values below 0.2,but even in cases where measurement errors exceed 80%,the maximum JMC error is only 0.0434.Thus,the proposed photogrammetric approach holds promise for widespread application in void geometry measurements in rock joints.
基金Projects(51274249,51174228)supported by the National Natural Science Foundation of China
文摘Direct shear tests were conducted on the rock joints under constant normal load(CNL), while the acoustic emission(AE) signals generated during shear tests were monitored with PAC Micro-II system. Before and after shearing, the surfaces of rock joints were measured by the Talysurf CLI 2000. By correlating the AE events with the shear stress-shear displacement curve, one can observe four periods of the whole course of shearing of rock joints. By the contrast of AE location and actual damage zone, it is elucidated that the AE event is related to the morphology of the joint. With the increase of shearing times, the shear behavior of rock joints gradually presents from the response of brittle behavior to that of ductile behavior. By combining the results of topography measurement, four morphological parameters of joint surface, S p(the maximum height of joint surface), N(number of islands), A(projection area) and V(volume of joint) were introduced, which decrease with shearing. Both the joint roughness coefficient(JRC) and joint matching coefficient(JMC) drop with shearing, and the shear strength of rock joints can be predicted by the JRC-JMC model. It establishes the relationship between micro-topography and macroscopic strength, which have the same change rule with shearing.
