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 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
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
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
A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness upda...A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning's roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning's roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning's roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.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 soil surface roughness and hydraulic roughness coefficient are important hydraulic resistance characteristic parameters. Precisely estimating the hydraulic roughness coefficient is important to understanding mecha...The soil surface roughness and hydraulic roughness coefficient are important hydraulic resistance characteristic parameters. Precisely estimating the hydraulic roughness coefficient is important to understanding mechanisms of overland flow. Four tillage practices, including cropland raking, artificial hoeing, artificial digging, and straight slopes, were considered based on the local agricultural conditions to simulate different values of soil surface roughness in the Loess Plateau. The objective of this study was to investigate the relationship between the soil surface roughness and hydraulic roughness coefficient on sloping farmland using artificial rainfall simulation. On a slope with a gradient of 10°, a significant logarithmic function was developed between the soil surface roughness and Manning＇s roughness coefficient, and an exponential function was derived to describe the relationship between the soil surface roughness and Reynolds number. On the slope with a gradient of 15°, a significant power function was developed to reflect the relationship between the soil surface roughness and Manning＇s roughness coefficient, and a linear function was derived to relate the soil surface roughness to the Reynolds number. These findings can provide alternative ways to estimate the hydraulic roughness coefficient for different types of soil surface roughness.展开更多
Manning＇s roughness coefficient was estimated for a gravel-bed river reach using field measurements of water level and discharge, and the applicability of various methods used for estimation of the roughness coeffici...Manning＇s roughness coefficient was estimated for a gravel-bed river reach using field measurements of water level and discharge, and the applicability of various methods used for estimation of the roughness coefficient was evaluated. Results show that the roughness coefficient tends to decrease with increasing discharge and water depth, and over a certain range it appears to remain constant. Comparison of roughness coefficients calculated by field measurement data with those estimated by other methods shows that, although the field-measured values provide approximate roughness coefficients for relatively large discharge, there seems to be rather high uncertainty due to the difference in resultant values. For this reason, uncertainty related to the roughness coefficient was analyzed in terms of change in computed variables. On average, a 20% increase of the roughness coefficient causes a 7% increase in the water depth and an 8% decrease in velocity, but there may be about a 15% increase in the water depth and an equivalent decrease in velocity for certain cross-sections in the study reach. Finally, the validity of estimated roughness coefficient based on field measurements was examined. A 10% error in discharge measurement may lead to more than 10% uncertainty in roughness coefficient estimation, but corresponding uncertainty in computed water depth and velocity is reduced to approximately 5%. Conversely, the necessity for roughness coefficient estimation by field measurement is confirmed.展开更多
This paper presents a method to calibrate pipe roughness coefficient (i.e., Manning n-factor) with genetic algorithm (GA) under multiple loading conditions. Due to the old pipe age as well as deleting valves and blend...This paper presents a method to calibrate pipe roughness coefficient (i.e., Manning n-factor) with genetic algorithm (GA) under multiple loading conditions. Due to the old pipe age as well as deleting valves and blends in the skeleton of distribution network, most of the pipes in hydraulic model of practical water distribution system (WDS) are rough. The commonly used Hazen-Williams C-factor is therefore replaced by Manning n-factor in calibrating WDS hydraulic model. Adjustment to GA is designed, and the program efficiency is improved. A case study shows that the adjustment can save 60% of the total runtime. About 90% of the relative differences between simulated and observed pressures at monitoring locations are lower than 3%, which suggests that the proposed adjustment to the calibration is efficient and effective.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
To accurately analyze proppant transport in rough intersecting fractures and elucidate the interaction mechanisms among liquid,particles,and rough walls,this study reconstructed a numerical model of fractures in inhom...To accurately analyze proppant transport in rough intersecting fractures and elucidate the interaction mechanisms among liquid,particles,and rough walls,this study reconstructed a numerical model of fractures in inhomogeneous reservoirs with varying brittleness index(BI).Various auto-correlation Gaussian rough fracture models were created using Matlab to assess roughness through the fractal dimension method.This research innovatively combined Boolean operations to establish three-dimensional rough fracture models,incorporating(Computational Fluid Dynamics)CFD-DEM(Discrete Element Method)with a bidirectional method for cosimulation.The proppant transport in fractures was categorized into three zones based on the difference in the turbulent kinetic energy.Artificially induced fracture roughness increases fluid retention and turbulence,causing plugging effects and limiting proppant flow into branch fractures.Additionally,compared with the superior deposition and significant support effects of the spherical proppant,the low-sphericity proppant traveled farther under fracturing fluid,inducing more pronounced plugging near curved fracture intersections;the variation in fracture intersection angles primarily impacted the wall shear stress within the flow field,indicating smaller angles led to higher shear energy at the intersection.Compared with the intersection angle of 30°,the height and area deposited in the 90 branch fracture increased by 52.25%and 65.33%,respectively:notably,injecting proppant from smaller to larger particles(S:M:L)and a low velocity effectively ensured fracture conductivity near the wellbore at joint roughness coefficient(JRC)≥46 while achieving satis-factory placement in the branch fracture,making it a recommended approach.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
Fracture conductivity is a key factor to determine the fracturing effect.Optimizing proppant particle size distribution is critical for ensuring efficient proppant placement within fractures.To address challenges asso...Fracture conductivity is a key factor to determine the fracturing effect.Optimizing proppant particle size distribution is critical for ensuring efficient proppant placement within fractures.To address challenges associated with the low-permeability reservoirs in the Lufeng Oilfield of the South China Sea—including high heterogeneity,complex lithology,and suboptimal fracturing outcomes—JRC(Joint Roughness Coefficient)was employed to quantitatively characterize the lithological properties of the target formation.A CFD-DEM(Computational Fluid Dynamics-Discrete Element Method)two-way coupling approach was then utilized to construct a fracture channel model that simulates proppant transport dynamics.Theproppant particle size under different lithology was optimized.Theresults show that:(1)In rough fractures,proppant particles exhibit more chaotic migration behavior compared to their movement on smooth surfaces,thereby increasing the risk of fracture plugging;(2)Within the same particle size range,for proppants with mesh sizes of 40/70 or 20/40,fracture conductivity decreases as roughness increases.In contrast,for 30/50 mesh proppants,conductivity initially increases and then decreases with rising roughness;(3)Under identical roughness conditions,the following recommendations apply based on fracture conductivity behavior relative to proppant particle size:When JRC<46,conductivity increases with larger particle sizes,with 20/40 mesh proppant recommended;When JRC>46,conductivity decreases as particle size increases;40/70 mesh proppant is thus recommended to maintain effective conductivity;At JRC=46,conductivity first increases then decreases with increasing particle size,making 30/50mesh the optimal choice.Theresearch findings provide a theoretical foundation for optimizing fracturing designs and enhancing fracturing performance in the field.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
To better estimate the rock joint shear strength,accurately determining the rock joint roughness coefficient(JRC)is the first step faced by researchers and engineers.However,there are incomplete,imprecise,and indeterm...To better estimate the rock joint shear strength,accurately determining the rock joint roughness coefficient(JRC)is the first step faced by researchers and engineers.However,there are incomplete,imprecise,and indeterminate problems during the process of calculating the JRC.This paper proposed to investigate the indeterminate information of rock joint roughness through a neutrosophic number approach and,based on this information,reported a method to capture the incomplete,uncertain,and imprecise information of the JRC in uncertain environments.The uncertainties in the JRC determination were investigated by the regression correlations based on commonly used statistical parameters,which demonstrated the drawbacks of traditional JRC regression correlations in handling the indeterminate information of the JRC.Moreover,the commonly used statistical parameters cannot reflect the roughness contribution differences of the asperities with various scales,which induces additional indeterminate information.A method based on the neutrosophic number(NN)and spectral analysis was proposed to capture the indeterminate information of the JRC.The proposed method was then applied to determine the JRC values for sandstone joint samples collected from a rock landslide.The comparison between the JRC results obtained by the proposed method and experimental results validated the effectiveness of the NN.Additionally,comparisons made between the spectral analysis and common statistical parameters based on the NN also demonstrated the advantage of spectral analysis.Thus,the NN and spectral analysis combined can effectively handle the indeterminate information in the rock joint roughness.展开更多
In this paper,a preliminary study is given on the drag (i.e.bulk transfer for momentum) coefficient,on the basis of data from four sets of AWS in Tibet during the first observational year from July 1993 to July 1994 a...In this paper,a preliminary study is given on the drag (i.e.bulk transfer for momentum) coefficient,on the basis of data from four sets of AWS in Tibet during the first observational year from July 1993 to July 1994 according to China Japan Asian Monsoon Cooperative Research Program.The results show that the drag coefficient over the Tibetan Plateau is 3.3 to 4.4×103.In addition,monthly and diurnal variations of drag coefficient and the relationship among the drag coefficients and the bulk Richardson number,surface roughness length and wind speed at 10 m height are discussed in detail.展开更多
This study aims to investigate mechanical properties and failure mechanisms of layered rock with rough joint surfaces under direct shear loading.Cubic layered samples with dimensions of 100 mm×100 mm×100 mm ...This study aims to investigate mechanical properties and failure mechanisms of layered rock with rough joint surfaces under direct shear loading.Cubic layered samples with dimensions of 100 mm×100 mm×100 mm were casted using rock-like materials,with anisotropic angle(α)and joint roughness coefficient(JRC)ranging from 15°to 75°and 2-20,respectively.The direct shear tests were conducted under the application of initial normal stress(σ_(n)) ranging from 1-4 MPa.The test results indicate significant differences in mechanical properties,acoustic emission(AE)responses,maximum principal strain fields,and ultimate failure modes of layered samples under different test conditions.The peak stress increases with the increasingαand achieves a maximum value atα=60°or 75°.As σ_(n) increases,the peak stress shows an increasing trend,with correlation coefficients R² ranging from 0.918 to 0.995 for the linear least squares fitting.As JRC increases from 2-4 to 18-20,the cohesion increases by 86.32%whenα=15°,while the cohesion decreases by 27.93%whenα=75°.The differences in roughness characteristics of shear failure surface induced byαresult in anisotropic post-peak AE responses,which is characterized by active AE signals whenαis small and quiet AE signals for a largeα.For a given JRC=6-8 andσ_(n)=1 MPa,asαincreases,the accumulative AE counts increase by 224.31%(αincreased from 15°to 60°),and then decrease by 14.68%(αincreased from 60°to 75°).The shear failure surface is formed along the weak interlayer whenα=15°and penetrates the layered matrix whenα=60°.Whenα=15°,as σ_(n) increases,the adjacent weak interlayer induces a change in the direction of tensile cracks propagation,resulting in a stepped pattern of cracks distribution.The increase in JRC intensifies roughness characteristics of shear failure surface for a smallα,however,it is not pronounced for a largeα.The findings will contribute to a better understanding of the mechanical responses and failure mechanisms of the layered rocks subjected to shear loads.展开更多
Conventional numerical solutions developed to describe the geomechanical behavior of rock interfaces subjected to differential load emphasize peak and residual shear strengths.The detailed analysis of preand post-peak...Conventional numerical solutions developed to describe the geomechanical behavior of rock interfaces subjected to differential load emphasize peak and residual shear strengths.The detailed analysis of preand post-peak shear stress-displacement behavior is central to various time-dependent and dynamic rock mechanic problems such as rockbursts and structural instabilities in highly stressed conditions.The complete stress-displacement surface(CSDS)model was developed to describe analytically the pre-and post-peak behavior of rock interfaces under differential loads.Original formulations of the CSDS model required extensive curve-fitting iterations which limited its practical applicability and transparent integration into engineering tools.The present work proposes modifications to the CSDS model aimed at developing a comprehensive and modern calibration protocol to describe the complete shear stressdisplacement behavior of rock interfaces under differential loads.The proposed update to the CSDS model incorporates the concept of mobilized shear strength to enhance the post-peak formulations.Barton’s concepts of joint roughness coefficient(JRC)and joint compressive strength(JCS)are incorporated to facilitate empirical estimations for peak shear stress and normal closure relations.Triaxial/uniaxial compression test and direct shear test results are used to validate the updated model and exemplify the proposed calibration method.The results illustrate that the revised model successfully predicts the post-peak and complete axial stressestrain and shear stressedisplacement curves for rock joints.展开更多
Gas spark switch is one of the key parts in pulsed power technology.Electrode erosion has great influence on the switch performance and lifetime.In this paper,a field distortion gas switch is selected for the experime...Gas spark switch is one of the key parts in pulsed power technology.Electrode erosion has great influence on the switch performance and lifetime.In this paper,a field distortion gas switch is selected for the experiment and a great deal of discharging experiments have been conducted in different test conditions.The forming process of etch pit as well as its influencing factors is discussed briefly and surface roughness coefficient(SRC) of the electrode is put forward to evaluate the state of electrode erosion.Experimental results show that current peak plays an important role in electrode erosion when waveforms of discharge current are the same,and electric charge and oscillation frequency of discharge current also have great effect on the electrode erosion when waveforms of discharge current are different.With the increase of discharge times,SRC decreases slowly at first and then decreases quickly after three thousand of discharge times.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 (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 the Special Fund for Public Welfare(Meteorology)of China(Grants No.GYHY201006037 and GYHY200906007)
文摘A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning's roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning's roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning's roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.
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.
基金supported by the National Natural Science Foundation of China(Grant No40901138)the Project of the State Key Laboratory of Earth Surface Processes and Resource Ecology(Grant No 2008-KF-05)the Project of the State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau(Grant No10501-283)
文摘The soil surface roughness and hydraulic roughness coefficient are important hydraulic resistance characteristic parameters. Precisely estimating the hydraulic roughness coefficient is important to understanding mechanisms of overland flow. Four tillage practices, including cropland raking, artificial hoeing, artificial digging, and straight slopes, were considered based on the local agricultural conditions to simulate different values of soil surface roughness in the Loess Plateau. The objective of this study was to investigate the relationship between the soil surface roughness and hydraulic roughness coefficient on sloping farmland using artificial rainfall simulation. On a slope with a gradient of 10°, a significant logarithmic function was developed between the soil surface roughness and Manning＇s roughness coefficient, and an exponential function was derived to describe the relationship between the soil surface roughness and Reynolds number. On the slope with a gradient of 15°, a significant power function was developed to reflect the relationship between the soil surface roughness and Manning＇s roughness coefficient, and a linear function was derived to relate the soil surface roughness to the Reynolds number. These findings can provide alternative ways to estimate the hydraulic roughness coefficient for different types of soil surface roughness.
基金supported by the 2006 Core Construction Technology Development Project(Grant No.06KSHS-B01) through the ECORIVER21 Research Center in KICTTEP of MOCT KOREA
文摘Manning＇s roughness coefficient was estimated for a gravel-bed river reach using field measurements of water level and discharge, and the applicability of various methods used for estimation of the roughness coefficient was evaluated. Results show that the roughness coefficient tends to decrease with increasing discharge and water depth, and over a certain range it appears to remain constant. Comparison of roughness coefficients calculated by field measurement data with those estimated by other methods shows that, although the field-measured values provide approximate roughness coefficients for relatively large discharge, there seems to be rather high uncertainty due to the difference in resultant values. For this reason, uncertainty related to the roughness coefficient was analyzed in terms of change in computed variables. On average, a 20% increase of the roughness coefficient causes a 7% increase in the water depth and an 8% decrease in velocity, but there may be about a 15% increase in the water depth and an equivalent decrease in velocity for certain cross-sections in the study reach. Finally, the validity of estimated roughness coefficient based on field measurements was examined. A 10% error in discharge measurement may lead to more than 10% uncertainty in roughness coefficient estimation, but corresponding uncertainty in computed water depth and velocity is reduced to approximately 5%. Conversely, the necessity for roughness coefficient estimation by field measurement is confirmed.
基金Supported by National Natural Science Foundation of China (No 50778121)Science and Technology Innovation Special Foundation of Tianjin (NO 06FZZDSH00900)
文摘This paper presents a method to calibrate pipe roughness coefficient (i.e., Manning n-factor) with genetic algorithm (GA) under multiple loading conditions. Due to the old pipe age as well as deleting valves and blends in the skeleton of distribution network, most of the pipes in hydraulic model of practical water distribution system (WDS) are rough. The commonly used Hazen-Williams C-factor is therefore replaced by Manning n-factor in calibrating WDS hydraulic model. Adjustment to GA is designed, and the program efficiency is improved. A case study shows that the adjustment can save 60% of the total runtime. About 90% of the relative differences between simulated and observed pressures at monitoring locations are lower than 3%, which suggests that the proposed adjustment to the calibration is efficient and effective.
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.
文摘To accurately analyze proppant transport in rough intersecting fractures and elucidate the interaction mechanisms among liquid,particles,and rough walls,this study reconstructed a numerical model of fractures in inhomogeneous reservoirs with varying brittleness index(BI).Various auto-correlation Gaussian rough fracture models were created using Matlab to assess roughness through the fractal dimension method.This research innovatively combined Boolean operations to establish three-dimensional rough fracture models,incorporating(Computational Fluid Dynamics)CFD-DEM(Discrete Element Method)with a bidirectional method for cosimulation.The proppant transport in fractures was categorized into three zones based on the difference in the turbulent kinetic energy.Artificially induced fracture roughness increases fluid retention and turbulence,causing plugging effects and limiting proppant flow into branch fractures.Additionally,compared with the superior deposition and significant support effects of the spherical proppant,the low-sphericity proppant traveled farther under fracturing fluid,inducing more pronounced plugging near curved fracture intersections;the variation in fracture intersection angles primarily impacted the wall shear stress within the flow field,indicating smaller angles led to higher shear energy at the intersection.Compared with the intersection angle of 30°,the height and area deposited in the 90 branch fracture increased by 52.25%and 65.33%,respectively:notably,injecting proppant from smaller to larger particles(S:M:L)and a low velocity effectively ensured fracture conductivity near the wellbore at joint roughness coefficient(JRC)≥46 while achieving satis-factory placement in the branch fracture,making it a recommended approach.
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 China NationalOffshore Oil Corporation(CNOOC)14th Five-Year Plan Major Science and Technology Project:Research on Integrated Geological Engineering Technology for Fracturing and Development of Offshore Low-Permeability Reservoirs(Grant NO.KJGG2022-0701).Mao Jiang,Chengyong Peng,JiangshuWu and Xuesong Xing.https://www.cnooc.com.cn.
文摘Fracture conductivity is a key factor to determine the fracturing effect.Optimizing proppant particle size distribution is critical for ensuring efficient proppant placement within fractures.To address challenges associated with the low-permeability reservoirs in the Lufeng Oilfield of the South China Sea—including high heterogeneity,complex lithology,and suboptimal fracturing outcomes—JRC(Joint Roughness Coefficient)was employed to quantitatively characterize the lithological properties of the target formation.A CFD-DEM(Computational Fluid Dynamics-Discrete Element Method)two-way coupling approach was then utilized to construct a fracture channel model that simulates proppant transport dynamics.Theproppant particle size under different lithology was optimized.Theresults show that:(1)In rough fractures,proppant particles exhibit more chaotic migration behavior compared to their movement on smooth surfaces,thereby increasing the risk of fracture plugging;(2)Within the same particle size range,for proppants with mesh sizes of 40/70 or 20/40,fracture conductivity decreases as roughness increases.In contrast,for 30/50 mesh proppants,conductivity initially increases and then decreases with rising roughness;(3)Under identical roughness conditions,the following recommendations apply based on fracture conductivity behavior relative to proppant particle size:When JRC<46,conductivity increases with larger particle sizes,with 20/40 mesh proppant recommended;When JRC>46,conductivity decreases as particle size increases;40/70 mesh proppant is thus recommended to maintain effective conductivity;At JRC=46,conductivity first increases then decreases with increasing particle size,making 30/50mesh the optimal choice.Theresearch findings provide a theoretical foundation for optimizing fracturing designs and enhancing fracturing performance in the field.
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This work is supported by Key Program of National Natural Science Foundation of China(No.41931295)General Program of National Natural Science Foundation of China(No.41877258)。
文摘To better estimate the rock joint shear strength,accurately determining the rock joint roughness coefficient(JRC)is the first step faced by researchers and engineers.However,there are incomplete,imprecise,and indeterminate problems during the process of calculating the JRC.This paper proposed to investigate the indeterminate information of rock joint roughness through a neutrosophic number approach and,based on this information,reported a method to capture the incomplete,uncertain,and imprecise information of the JRC in uncertain environments.The uncertainties in the JRC determination were investigated by the regression correlations based on commonly used statistical parameters,which demonstrated the drawbacks of traditional JRC regression correlations in handling the indeterminate information of the JRC.Moreover,the commonly used statistical parameters cannot reflect the roughness contribution differences of the asperities with various scales,which induces additional indeterminate information.A method based on the neutrosophic number(NN)and spectral analysis was proposed to capture the indeterminate information of the JRC.The proposed method was then applied to determine the JRC values for sandstone joint samples collected from a rock landslide.The comparison between the JRC results obtained by the proposed method and experimental results validated the effectiveness of the NN.Additionally,comparisons made between the spectral analysis and common statistical parameters based on the NN also demonstrated the advantage of spectral analysis.Thus,the NN and spectral analysis combined can effectively handle the indeterminate information in the rock joint roughness.
文摘In this paper,a preliminary study is given on the drag (i.e.bulk transfer for momentum) coefficient,on the basis of data from four sets of AWS in Tibet during the first observational year from July 1993 to July 1994 according to China Japan Asian Monsoon Cooperative Research Program.The results show that the drag coefficient over the Tibetan Plateau is 3.3 to 4.4×103.In addition,monthly and diurnal variations of drag coefficient and the relationship among the drag coefficients and the bulk Richardson number,surface roughness length and wind speed at 10 m height are discussed in detail.
基金financial support from the National Natural Science Foundation of China(Nos.52174092,51904290,52004272,52104125,42372328,and U23B2091)Natural Science Foundation of Jiangsu Province,China(Nos.BK20220157 and BK20240209)+3 种基金the Fundamental Research Funds for the Central Universities,China(No.2022YCPY0202)Xuzhou Science and Technology Project,China(Nos.KC21033 and KC22005)Yunlong Lake Laboratory of Deep Underground Science and Engineering Project,China(No.104023002)the Graduate Innovation Program of China University of Mining and Technology(No.2023WLTCRCZL052)。
文摘This study aims to investigate mechanical properties and failure mechanisms of layered rock with rough joint surfaces under direct shear loading.Cubic layered samples with dimensions of 100 mm×100 mm×100 mm were casted using rock-like materials,with anisotropic angle(α)and joint roughness coefficient(JRC)ranging from 15°to 75°and 2-20,respectively.The direct shear tests were conducted under the application of initial normal stress(σ_(n)) ranging from 1-4 MPa.The test results indicate significant differences in mechanical properties,acoustic emission(AE)responses,maximum principal strain fields,and ultimate failure modes of layered samples under different test conditions.The peak stress increases with the increasingαand achieves a maximum value atα=60°or 75°.As σ_(n) increases,the peak stress shows an increasing trend,with correlation coefficients R² ranging from 0.918 to 0.995 for the linear least squares fitting.As JRC increases from 2-4 to 18-20,the cohesion increases by 86.32%whenα=15°,while the cohesion decreases by 27.93%whenα=75°.The differences in roughness characteristics of shear failure surface induced byαresult in anisotropic post-peak AE responses,which is characterized by active AE signals whenαis small and quiet AE signals for a largeα.For a given JRC=6-8 andσ_(n)=1 MPa,asαincreases,the accumulative AE counts increase by 224.31%(αincreased from 15°to 60°),and then decrease by 14.68%(αincreased from 60°to 75°).The shear failure surface is formed along the weak interlayer whenα=15°and penetrates the layered matrix whenα=60°.Whenα=15°,as σ_(n) increases,the adjacent weak interlayer induces a change in the direction of tensile cracks propagation,resulting in a stepped pattern of cracks distribution.The increase in JRC intensifies roughness characteristics of shear failure surface for a smallα,however,it is not pronounced for a largeα.The findings will contribute to a better understanding of the mechanical responses and failure mechanisms of the layered rocks subjected to shear loads.
基金The authors acknowledge the financial support from Natural Sciences and Engineering Research Council of Canada through its Discovery Grant program(RGPIN-2022-03893)École de Technologie Supérieure(ÉTS)construction engineering research funding.
文摘Conventional numerical solutions developed to describe the geomechanical behavior of rock interfaces subjected to differential load emphasize peak and residual shear strengths.The detailed analysis of preand post-peak shear stress-displacement behavior is central to various time-dependent and dynamic rock mechanic problems such as rockbursts and structural instabilities in highly stressed conditions.The complete stress-displacement surface(CSDS)model was developed to describe analytically the pre-and post-peak behavior of rock interfaces under differential loads.Original formulations of the CSDS model required extensive curve-fitting iterations which limited its practical applicability and transparent integration into engineering tools.The present work proposes modifications to the CSDS model aimed at developing a comprehensive and modern calibration protocol to describe the complete shear stressdisplacement behavior of rock interfaces under differential loads.The proposed update to the CSDS model incorporates the concept of mobilized shear strength to enhance the post-peak formulations.Barton’s concepts of joint roughness coefficient(JRC)and joint compressive strength(JCS)are incorporated to facilitate empirical estimations for peak shear stress and normal closure relations.Triaxial/uniaxial compression test and direct shear test results are used to validate the updated model and exemplify the proposed calibration method.The results illustrate that the revised model successfully predicts the post-peak and complete axial stressestrain and shear stressedisplacement curves for rock joints.
基金Project Supported by National Natural Science Foundation of China(No.50637010)
文摘Gas spark switch is one of the key parts in pulsed power technology.Electrode erosion has great influence on the switch performance and lifetime.In this paper,a field distortion gas switch is selected for the experiment and a great deal of discharging experiments have been conducted in different test conditions.The forming process of etch pit as well as its influencing factors is discussed briefly and surface roughness coefficient(SRC) of the electrode is put forward to evaluate the state of electrode erosion.Experimental results show that current peak plays an important role in electrode erosion when waveforms of discharge current are the same,and electric charge and oscillation frequency of discharge current also have great effect on the electrode erosion when waveforms of discharge current are different.With the increase of discharge times,SRC decreases slowly at first and then decreases quickly after three thousand of discharge times.
