Against the background of“carbon peak and carbon neutrality,”it is of great practical significance to develop non-blast furnace ironmaking technology for the sustainable development of steel industry.Carbon-bearing ...Against the background of“carbon peak and carbon neutrality,”it is of great practical significance to develop non-blast furnace ironmaking technology for the sustainable development of steel industry.Carbon-bearing iron ore pellet is an innovative burden of direct reduction ironmaking due to its excellent self-reducing property,and the thermal strength of pellet is a crucial metallurgical property that affects its wide application.The carbon-bearing iron ore pellet without binders(CIPWB)was prepared using iron concentrate and anthracite,and the effects of reducing agent addition amount,size of pellet,reduction temperature and time on the thermal compressive strength of CIPWB during the reduction process were studied.Simultaneously,the mechanism of the thermal strength evolution of CIPWB was revealed.The results showed that during the low-temperature reduction process(300-500℃),the thermal compressive strength of CIPWB linearly increases with increasing the size of pellet,while it gradually decreases with increasing the anthracite ratio.When the CIPWB with 8%anthracite is reduced at 300℃for 60 min,the thermal strength of pellet is enhanced from 13.24 to 31.88 N as the size of pellet increases from 8.04 to 12.78 mm.Meanwhile,as the temperature is 500℃,with increasing the anthracite ratio from 2%to 8%,the thermal compressive strength of pellet under reduction for 60 min remarkably decreases from 41.47 to 8.94 N.Furthermore,in the high-temperature reduction process(600-1150℃),the thermal compressive strength of CIPWB firstly increases and then reduces with increasing the temperature,while it as well as the temperature corresponding to the maximum strength decreases with increasing the anthracite ratio.With adding 18%anthracite,the thermal compressive strength of pellet reaches the maximum value at 800℃,namely 35.00 N,and obtains the minimum value at 1050℃,namely 8.60 N.The thermal compressive strength of CIPWB significantly depends on the temperature,reducing agent dosage,and pellet size.展开更多
In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive streng...In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.展开更多
Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesi...Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesian optimization model(B-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed concrete.Furthermore,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional models.Moreover,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by water.Therefore,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.展开更多
Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biolo...Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biological properties of the scaffold(i.e.,degradability and cell growth rate).Lattice structure is a kind of periodic porous structure,which has some advantages of light weight and high strength,and is widely used in the preparation of bioceramic scaffolders.For the structure of the scaffold,high porosity and large pore size are important for bone growth,bone integration and promoting good mechanical interlocking between neighboring bones and the scaffold.However,scaffolds with a high porosity often lack mechanical strength.In addition,different parts of the bone have different structural requirements.In this paper,scaffolds with a non-uniform structure or a hierarchical structure were designed,with loose and porous exterior to facilitate cell adhesion,osteogenic differentiation and vascularization as well as relatively dense interior to provide sufficient mechanical support for bone repair.Methods In this work,composite ceramics scaffolds with 10%akermanite content were prepared by DLP technology.The scaffold had a high porosity outside to promote the growth of bone tissue,and a low porosity inside to withstand external forces.The compressive strength,fracture form,in-vitro degradation performance and bioactivity of graded bioceramic scaffolds were investigated.The models of scaffolds were imported into the DLP printer with a 405 nm light.The samples were printed with the intensity of 8 mJ/cm^(2)and a layer thickness of 50μm.Finally,the ceramic samples were sintered at 1100℃.The degradability of the hierarchical gyroid bioceramic scaffolds was evaluated through immersion in Tris-HCl solution and SBF solution at a ratio of 200 mL/g.The bioactivity of bioceramic was obtained via immersing them in SBF solution for two weeks.The concentrations of calcium,phosphate,silicon,and magnesium ions in the soaking solution were determined by an inductively coupled plasma optical emission spectrometer.Results and discussion In this work,a hierarchical Gyroid structure HA-AK10 scaffold(sintered at 1100℃)with a radial internal porosity of 50%and an external porosity of 70%is prepared,and the influence of structural form on the compressive strength and degradation performance of the scaffold is investigated.The biological activity of the bioceramics in vitro is also verified.The mechanical simulation results show that the stress distribution corresponds to the porosity distribution of the structure,and the low porosity is larger and the overall stress concentration phenomenon does not appear.After soaking in SBF solution,Si—OH is firstly formed on the surface of bioceramics,and then silicon gel layer is produced due to the presence of calcium and silicon ions.The silicon gel layer is dissociated into negatively charged groups under alkaline environment secondary adsorption of calcium ions and phosphate ions,forming amorphous calcium phosphate,and finally amorphous calcium phosphate crystals and adsorption of carbonate ions,forming carbonate hydroxyapatite.This indicates that the composite bioceramics have a good biological activity in-vitro and can provide a good environment for the growth of bone cells.A hierarchical Gyroid ceramic scaffold with a bone geometry is prepared via applying the hierarchical structure to the bone contour scaffold.The maximum load capacity of the hierarchical Gyroid ceramic scaffold is 8 times that of the uniform structure.Conclusions The hierarchical structure scaffold designed had good overall compressive performance,good degradation performance,and still maintained a good mechanical stability during degradation.In addition,in-vitro biological experimental results showed that the surface graded composite scaffold could have a good in-vitro biological activity and provide a good environment for bone cells.Compared to the heterosexual structure,the graded scaffold had greater mechanical properties.展开更多
The compressive strength of the pellets is a key indicator that determines the production efficiency in straight grate.It usually relies on manual sampling and testing,which is cumbersome and inefficient.To address th...The compressive strength of the pellets is a key indicator that determines the production efficiency in straight grate.It usually relies on manual sampling and testing,which is cumbersome and inefficient.To address this,a time series prediction model for pellet compressive strength was developed,combining a gradient boosting decision tree with a temporal convolutional network(GBDT-TCN).Firstly,the key physical characteristics of the pellet production process were established through the feature construction method,and then the multicollinear features were eliminated based on the Spearman correlation coefficient.The final selection of feature parameters,amounting to 9,was determined using recursive feature elimination(RFE)method.Finally,the GBDT algorithm was used to establish the nonlinear relationship between these features and the compressive strength.The GBDT prediction results and process data were constructed into a time series dataset,which was input into the TCN unit cascade model.The time series information was captured through the distribution coefficient of the loss function in the time series.Results illustrate that the GBDT-TCN method proposed performs well in the task of predicting the compressive strength of pellets.Compared with the prediction model using only GBDT,the accuracy within±100 N is increased from 83.33%to 90.00%.展开更多
Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timec...Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.展开更多
This study focuses on empirical modeling of the strength characteristics of urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization with ordinary Portland cement(OPC)...This study focuses on empirical modeling of the strength characteristics of urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization with ordinary Portland cement(OPC).For dataset collection,an extensive experimental program was designed to estimate the unconfined compressive strength(Qu)of heavy metal-contaminated soils collected from awide range of land use pattern,i.e.residential,industrial and roadside soils.Accordingly,a robust comparison of predictive performances of four data-driven models including extreme learning machines(ELMs),gene expression programming(GEP),random forests(RFs),and multiple linear regression(MLR)has been presented.For completeness,a comprehensive experimental database has been established and partitioned into 80%for training and 20%for testing the developed models.Inputs included varying levels of heavy metals like Cd,Cu,Cr,Pb and Zn,along with OPC.The results revealed that the GEP model outperformed its counterparts:explaining approximately 96%of the variability in both training(R2=0.964)and testing phases(R^(2)=0.961),and thus achieving the lowest RMSE and MAE values.ELM performed commendably but was slightly less accurate than GEP whereas MLR had the lowest performance metrics.GEP also provided the benefit of traceable mathematical equation,enhancing its applicability not just as a predictive but also as an explanatory tool.Despite its insights,the study is limited by its focus on a specific set of heavy metals and urban soil samples of a particular region,which may affect the generalizability of the findings to different contamination profiles or environmental conditions.The study recommends GEP for predicting Qu in heavy metal-contaminated soils,and suggests further research to adapt these models to different environmental conditions.展开更多
Foam concrete is widely used in engineering due to its lightweight and high porosity.Its compressive strength,a key performance indicator,is influenced by multiple factors,showing nonlinear variation.As compressive st...Foam concrete is widely used in engineering due to its lightweight and high porosity.Its compressive strength,a key performance indicator,is influenced by multiple factors,showing nonlinear variation.As compressive strength tests for foam concrete take a long time,a fast and accurate prediction method is needed.In recent years,machine learning has become a powerful tool for predicting the compressive strength of cement-based materials.However,existing studies often use a limited number of input parameters,and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.This study selects foam concrete density,water-to-cement ratio(W/C),supplementary cementitious material replacement rate(SCM),fine aggregate to binder ratio(FA/Binder),superplasticizer content(SP),and age of the concrete(Age)as input parameters,with compressive strength as the output.Five different machine learning models were compared,and sensitivity analysis,based on Shapley Additive Explanations(SHAP),was used to assess the contribution of each input parameter.The results show that Gaussian Process Regression(GPR)outperforms the other models,with R2,RMSE,MAE,and MAPE values of 0.95,1.6,0.81,and 0.2,respectively.It is because GPR,optimized through Bayesian methods,better fits complex nonlinear relationships,especially considering a large number of input parameters.Sensitivity analysis indicates that the influence of input parameters on compressive strength decreases in the following order:foam concrete density,W/C,Age,FA/Binder,SP,and SCM.展开更多
During rock drilling and blasting activities,stemming blast holes is to prevent high-pressure explosive gases from the holes,thereby enhancing the overall blasting effectiveness.Hence,it is imperative to investigate t...During rock drilling and blasting activities,stemming blast holes is to prevent high-pressure explosive gases from the holes,thereby enhancing the overall blasting effectiveness.Hence,it is imperative to investigate the dynamic mechanical properties of the stem-ming materials.In this study,impact compression tests were conducted on self-swelling cartridges(SSCs)using a split Hopkinson pres-sure bar(SHPB),aiming to evaluate dynamic performances across strain rate range of 20 to 65 s^(−1).Test results indicate that the dynamic compressive strength of SSCs exhibits the following trends:it increases with increasing density of SSC,decreases with an increase in insertion gap,and follows an initial rise and subsequent fall trend with an increase in water absorption.The order of significance among these factors is density>water absorption>insertion gaps.SSCs exhibit a pronounced strain-rate strengthening dependence in dynamic compressive strength.Furthermore,both the compressive peak stress and peak strain of SSCs follow a well-defined quadratic upward trend with increasing strain rates.As the strain rate increases,the degree of fragmentation,absorbed energy,and dynamic increase factor exhibit an upward trend.Model experimental results indicate that,compared to cementitious stemming materials,SSCs can prolong the duration of gas explosion action.Therefore,SSCs are more suitable for high strain-rate applications such as blasting stemming and rock burst control.展开更多
This study investigates the effect of different in situ conditions like flaw infill,heat-treatment temperatures,and sample porosities on the anisotropic compressive response of jointed samples with an impersistent fla...This study investigates the effect of different in situ conditions like flaw infill,heat-treatment temperatures,and sample porosities on the anisotropic compressive response of jointed samples with an impersistent flaw.Jointed samples of different porosities are prepared by mixing Plaster of Paris(POP)with different water contents,i.e.60%(i.e.for lower porosity)and 80%(i.e.for higher porosity).These samples are grouted with different infill materials,i.e.un-grouted,cement and sand-cement(3:1)-bio-concrete(SCB)mix and subsequently subjected to different temperatures,i.e.100℃,200℃ and 300℃.The results reveal the distinct stages in the stress-strain responses of samples characterized by initial micro-cracks closure,elastic transition,and non-linear response till peak followed by a post-peak behaviour.The un-grouted samples exhibit their lowest strength at 30°joint orientation.The ratios of maximum to minimum strength are 3.11 and 3.22 with varying joint orientations for lower and higher porosity samples,respectively.Strengths of cement and SCB mix grouted samples are increased for all joint orientations ranging between 16.13%-69.83%and 18.04%-73%at low porosity and 22%-48.66%and 27.77%-51.57%at high porosity,respectively as compared to the un-grouted samples.However,the strength of the grouted samples is decreased by 66.94%-75.47%and 77.17%-81.05%at lower porosity,and 79.37%-82.86%and 81.29%-95.55%at higher porosity for cement and for SCB grouts with an increase in the heating temperature from 30℃ to 300℃,respectively.These observations could be due to the suppression of favourable crack initiation locations,i.e.flaw tips along the samples due to the filling of the crack by grouting and generation of thermal cracks with temperature.The mechanism of strength behaviour is elucidated in detail based on fracture propagation analysis and the anisotropic response of with or,without grouted samples.展开更多
This study systematically investigated the coupling effects of confiningpressure and strain rate on the dynamic strength of granite through dynamic triaxial compression tests.A dynamic strength criterion was developed...This study systematically investigated the coupling effects of confiningpressure and strain rate on the dynamic strength of granite through dynamic triaxial compression tests.A dynamic strength criterion was developed to incorporate these coupling effects for further analysis.Moreover,the research thoroughly revealed the underlying mechanism by which these coupling effects influencethe rock strength.The results revealed that both confiningpressure and strain rate significantly enhanced the dynamic strength of rock;however,a mutual inhibition effect emerged under their coupling.Specifically,as the confiningpressure increased,the strengthening effect of strain rate gradually diminished.Conversely,increasing the strain rate weakened the strengthening effect of confiningpressure.The proposed strength criterion successfully predicted rock strength under various confiningpressures(0-225 MPa)and strain rates(10^(-6)-600 s^(-1)).It achieved an average prediction error of only 8.3%,which represents a 65%improvement in accuracy compared to models that consider confiningpressure and strain rate effects independently.At the micro-mechanism level,increasing confiningpressure and strain rate promoted crack propagation in a transgranular(TG)mode,thereby enhancing the overall rock strength.However,under the coupling effects,the interference and interaction of TG cracks weakened the overall strengthening effect.This indicated that the competitive interaction between confiningpressure and strain rate during crack propagation constitutes the intrinsic mechanism underlying their mutual inhibitory effect on rock strength.This study provides a more accurate theoretical basis for understanding the dynamic responses of rocks and contributes valuable insights for disaster prevention and control in deep rock engineering projects.展开更多
The determination of discontinuity shear strength is an important concern in rock engineering.Previous research mainly focused on the shear behavior of discontinuities with identical joint wall compressive strengths(D...The determination of discontinuity shear strength is an important concern in rock engineering.Previous research mainly focused on the shear behavior of discontinuities with identical joint wall compressive strengths(DIJCS).However,the shear behavior of discontinuities with different joint wall compressive strengths(DDJCS)and 3D surface morphology had been rarely reported.In this study,matched mortar DDJCSs were prepared using 3D printed photosensitive resin molds.Direct shear tests were carried out under three kinds of normal stress(ranging from 0.5 to 3.0 MPa)to analyze the shear strength and contact zones of DDJCS during shearing.The results show that the contact zones of DDJCS during shearing are scattered in the steep zones facing the shear direction.It is verified that Grasselli and Develi’s directional surface roughness characterization method can be used to predict the shear-induced potential contact zones of DDJCS.When the critical apparent dip angle is equal to the peak dilation angle,the predicted contact area agrees well with the actual contact area.A 3D directional roughness parameter with clear physical meaning was introduced to characterize discontinuity surface roughness.A 3D modified joint roughness coefficient-joint wall compressive strength(JRC-JCS)criterion that can both predict the shear strength of DDJCS and DIJCS was proposed based on the newly defined roughness parameter.The proposed criterion was validated by 77 direct shear tests presented by this study and 163 direct shear tests presented by other investigators.The results show that the proposed criterion was generally reliable for the peak shear strength prediction of DDJCS and DIJCS(within 16%).It is also found that the new criterion can capture the anisotropy of the peak shear strength of DDJCS.The anisotropy of DDJCS decreases with increasing normal stress.It should be noted that the anisotropy of the shear strength of DDJCS was not investigated experimentally,and further experiments should be conducted to verify it.展开更多
Ceramic spheres,typically with a particle diameter of less than 0.8 mm,are frequently utilized as a critical proppant material in hydraulic fracturing for petroleum and natural gas extraction.Porous ceramic spheres wi...Ceramic spheres,typically with a particle diameter of less than 0.8 mm,are frequently utilized as a critical proppant material in hydraulic fracturing for petroleum and natural gas extraction.Porous ceramic spheres with artificial inherent pores are an important type of lightweight proppant,enabling their transport to distant fracture extremities and enhancing fracture conductivity.However,the focus frequently gravitates towards the low-density advantage,often overlooking the pore geometry impacts on compressive strength by traditional strength evaluation.This paper numerically bypasses such limitations by using a combined finite and discrete element method(FDEM)considering experimental results.The mesh size of the model undergoes validation,followed by the calibration of cohesive element parameters via the single particle compression test.The stimulation elucidates that proppants with a smaller pore size(40μm)manifest crack propagation evolution at a more rapid pace in comparison to their larger-pore counterparts,though the influence of pore diameter on overall strength is subtle.The inception of pores not only alters the trajectory of crack progression but also,with an increase in porosity,leads to a discernible decline in proppant compressive strength.Intriguingly,upon crossing a porosity threshold of 10%,the decrement in strength becomes more gradual.A denser congregation of pores accelerates crack propagation,undermining proppant robustness,suggesting that under analogous conditions,hollow proppants might not match the strength of their porous counterparts.This exploration elucidates the underlying mechanisms of proppant failure from a microstructural perspective,furnishing pivotal insights that may guide future refinements in the architectural design of porous proppant.展开更多
The uniaxial compressive strength(UCS)of rocks is a vital geomechanical parameter widely used for rock mass classification,stability analysis,and engineering design in rock engineering.Various UCS testing methods and ...The uniaxial compressive strength(UCS)of rocks is a vital geomechanical parameter widely used for rock mass classification,stability analysis,and engineering design in rock engineering.Various UCS testing methods and apparatuses have been proposed over the past few decades.The objective of the present study is to summarize the status and development in theories,test apparatuses,data processing of the existing testing methods for UCS measurement.It starts with elaborating the theories of these test methods.Then the test apparatus and development trends for UCS measurement are summarized,followed by a discussion on rock specimens for test apparatus,and data processing methods.Next,the method selection for UCS measurement is recommended.It reveals that the rock failure mechanism in the UCS testing methods can be divided into compression-shear,compression-tension,composite failure mode,and no obvious failure mode.The trends of these apparatuses are towards automation,digitization,precision,and multi-modal test.Two size correction methods are commonly used.One is to develop empirical correlation between the measured indices and the specimen size.The other is to use a standard specimen to calculate the size correction factor.Three to five input parameters are commonly utilized in soft computation models to predict the UCS of rocks.The selection of the test methods for the UCS measurement can be carried out according to the testing scenario and the specimen size.The engineers can gain a comprehensive understanding of the UCS testing methods and its potential developments in various rock engineering endeavors.展开更多
Uniaxial compressive strength(UCS)is a significant mechanical measure in rock engineering,key for classifying rock masses,conducting stability assessments,and guiding design processes.Over the past five decades,numero...Uniaxial compressive strength(UCS)is a significant mechanical measure in rock engineering,key for classifying rock masses,conducting stability assessments,and guiding design processes.Over the past five decades,numerous techniques and devices have emerged for UCS measurement.This paper presents a comprehensive literature review of existing methodologies and advancements in UCS testing,examining the theoretical foundations,testing equipment,data processing techniques,and criteria for selecting appropriate UCS testing methods.Additionally,the study highlights a shift toward automated,precise,and computational approaches with multiple inputs(i.e.multiple regression,machine learning,ML)for UCS prediction.Approximately 221 articles published by various researchers between 2000 and 2024 related to ML were reviewed,focusing on the application of ML models,including artificial neural networks(ANNs),adaptive-network-based fuzzy inference system(ANFIS),random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGBoost),in UCS prediction.The review also observed the growing use of hybrid models integrating ML with optimization techniques,significantly improving UCS estimation.Numerous empirical correlations,both direct and indirect,have been established in the past several years based on the physical properties of rocks.However,utilizing these proposed equations to reliably estimate UCS remains challenging due to the variability in lithology,rock origin,and other factors.This study systematically presents the established correlation expressions,considering their lithology,the number of samples used to establish the expressions,the coefficient of determination(R2),and the locations.This allows geologists and engineers to easily apply these established expressions in situations where direct estimation is not possible,enabling them to approximate UCS results.展开更多
The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive st...The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive strength of mortars made with ternary blends of cement, activated coal gangue and fly ash. Based on the results of a minimum of seven design points, three special cubic polynomial models are used to establish the strength predicating equations at different ages for mortars. Five experimental checkpoints were also designed to verify the precision of the equations. The most frequent errors of the predicted values are within 3%. A simple and practical way is provided for determining the optimal proportion of two admixtures when they are used in concrete.展开更多
A series of compression tests were conducted on 150 groups of cement paste specimens with side lengths ranging from 40 mm to 200 mm. The specimens include cube specimens and prism specimens with height to width ratio ...A series of compression tests were conducted on 150 groups of cement paste specimens with side lengths ranging from 40 mm to 200 mm. The specimens include cube specimens and prism specimens with height to width ratio of 2. The experiment results show that size effect exists in the cubic compressive strength and prismatic compressive strength of the cement paste, and larger specimens resist less in terms of strength than smaller ones. The cubic compressive strength and the prismatic compressive strength of the specimens with side length of 200 mm are respectively about 91% and 89% of the compressive strength of the specimens with the side length of 40 mm. Water to binder ratio has a significant influence on the size effect of the compressive strengths of the cement paste. With a decrease in the water to binder ratio, the size effect is significantly enhanced. When the water to binder ratio is 0.2, the size effects of the cubic compressive strength and the prismatic compressive strength of the cement paste are 1.6 and 1.4 times stronger than those of a water to binder ratio of 0.6. Furthermore, a series of formulas are proposed to calculate the size effect of the cubic compressive strength and the prismatic compressive strength of cement paste, and the results of the size effect predicted by the formulas are in good agreement with the experiment results.展开更多
A theoretical calculation method of the axial compressive strength of a high strength concrete with fibre reinforced plastics (FRP) constraint is proposed. It is shown by test verification that the FRP strength devoti...A theoretical calculation method of the axial compressive strength of a high strength concrete with fibre reinforced plastics (FRP) constraint is proposed. It is shown by test verification that the FRP strength devotion factor used for this method is in accordance with actual conditions. FRP is not up to the ultimate strength when the concrete reaches the ultimate strength, whose strength devotion factor is in the range of 0.28 to 0.59, which is related to an elastic modulus. The method can be used to estimate axial compressive strength of the concrete strengthened with FRP. The theoretical strength is 10% to 30% higher than the measured one. The deviation comes mainly from a non-ideal bonding condition of FRP-concrete interfaces and discrete property of the testing data of compressive strength.展开更多
To test the influence of binder strength, porous concretes with 4 binder strengths between 30.0-135.0 MPa and 5 void ratios between 15%-35% were tested. The results indicated that for the same aggregate, the rates of ...To test the influence of binder strength, porous concretes with 4 binder strengths between 30.0-135.0 MPa and 5 void ratios between 15%-35% were tested. The results indicated that for the same aggregate, the rates of strength reduction due to the increases in void ratio were the same for binders with different strengths. To study the influence of aggregate size, 3 single size aggregates with nominal sizes of 5.0, 13.0 and 20.0 mm （Nos. 7, 6 and 5 according to JIS A 5001） were used to make porous concrete. The strengths of porous concrete are found to be dependent on aggregate size. The rate of strength reduction of porous concrete with small aggregate size is found to be higher than that with larger aggregate size. At the same void ratio, the strength of porous concrete with large aggregate is larger than that with small aggregate. The general equations for porous concrete are related to compressive strength and void ratio for different binder strengths and aggregate sizes.展开更多
Surfaces of grade III fly ashes were modified through mixing with carbide slag and calcining at 850 ℃ for 1 h. Mineralogical compositions and surface morphology of fly ashes before and after modification were charact...Surfaces of grade III fly ashes were modified through mixing with carbide slag and calcining at 850 ℃ for 1 h. Mineralogical compositions and surface morphology of fly ashes before and after modification were characterized by X-ray diffraction （XRD） and scanning electron microscopy （SEM）, respectively. Effect of surface-modified fly ashes on compressive strength and autogenous shrinkage of blended cement pastes was investigated. Microstructures of cement pastes were examined by backscattered electron （BSE） imaging and mercury intrusion porosimetry （MIP）. The experimental results showed that β-C2S was formed on the surfaces of fly ashes after modification. Hydration ofβ-C2S on the surface-modified fly ashes densified interface zone and enhanced bond strength between particles of fly ashes and hydrated clinkers. In addition, surface modification of fly ashes tended to decrease total porosity and 10-50 nm pores of cement pastes. Surface modification of fly ashes increased compressive strength and reduced autogenous shrinkage of cement pastes.展开更多
基金support of the National Natural Science Foundation of China(52074080,52004001,and 51574002).
文摘Against the background of“carbon peak and carbon neutrality,”it is of great practical significance to develop non-blast furnace ironmaking technology for the sustainable development of steel industry.Carbon-bearing iron ore pellet is an innovative burden of direct reduction ironmaking due to its excellent self-reducing property,and the thermal strength of pellet is a crucial metallurgical property that affects its wide application.The carbon-bearing iron ore pellet without binders(CIPWB)was prepared using iron concentrate and anthracite,and the effects of reducing agent addition amount,size of pellet,reduction temperature and time on the thermal compressive strength of CIPWB during the reduction process were studied.Simultaneously,the mechanism of the thermal strength evolution of CIPWB was revealed.The results showed that during the low-temperature reduction process(300-500℃),the thermal compressive strength of CIPWB linearly increases with increasing the size of pellet,while it gradually decreases with increasing the anthracite ratio.When the CIPWB with 8%anthracite is reduced at 300℃for 60 min,the thermal strength of pellet is enhanced from 13.24 to 31.88 N as the size of pellet increases from 8.04 to 12.78 mm.Meanwhile,as the temperature is 500℃,with increasing the anthracite ratio from 2%to 8%,the thermal compressive strength of pellet under reduction for 60 min remarkably decreases from 41.47 to 8.94 N.Furthermore,in the high-temperature reduction process(600-1150℃),the thermal compressive strength of CIPWB firstly increases and then reduces with increasing the temperature,while it as well as the temperature corresponding to the maximum strength decreases with increasing the anthracite ratio.With adding 18%anthracite,the thermal compressive strength of pellet reaches the maximum value at 800℃,namely 35.00 N,and obtains the minimum value at 1050℃,namely 8.60 N.The thermal compressive strength of CIPWB significantly depends on the temperature,reducing agent dosage,and pellet size.
基金Funded by the Natural Science Foundation of China(No.52109168)。
文摘In order to study the characteristics of pure fly ash-based geopolymer concrete(PFGC)conveniently,we used a machine learning method that can quantify the perception of characteristics to predict its compressive strength.In this study,505 groups of data were collected,and a new database of compressive strength of PFGC was constructed.In order to establish an accurate prediction model of compressive strength,five different types of machine learning networks were used for comparative analysis.The five machine learning models all showed good compressive strength prediction performance on PFGC.Among them,R2,MSE,RMSE and MAE of decision tree model(DT)are 0.99,1.58,1.25,and 0.25,respectively.While R2,MSE,RMSE and MAE of random forest model(RF)are 0.97,5.17,2.27 and 1.38,respectively.The two models have high prediction accuracy and outstanding generalization ability.In order to enhance the interpretability of model decision-making,we used importance ranking to obtain the perception of machine learning model to 13 variables.These 13 variables include chemical composition of fly ash(SiO_(2)/Al_(2)O_(3),Si/Al),the ratio of alkaline liquid to the binder,curing temperature,curing durations inside oven,fly ash dosage,fine aggregate dosage,coarse aggregate dosage,extra water dosage and sodium hydroxide dosage.Curing temperature,specimen ages and curing durations inside oven have the greatest influence on the prediction results,indicating that curing conditions have more prominent influence on the compressive strength of PFGC than ordinary Portland cement concrete.The importance of curing conditions of PFGC even exceeds that of the concrete mix proportion,due to the low reactivity of pure fly ash.
基金Funded by China National Key Research and Development Program for Application and Verification of Typical Groundwater Contaminated Sites(No.2019YFC1804805)Shenyang Key Laboratory of Safety Evaluation and Disaster Prevention of Engineering Structures(No.S230184)the Funding Project of Northeast Geological S&T Innovation Center of China Geological Survey(No.QCJJ2023-39)。
文摘Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed concrete.To address this issue,we introduce two optimized hybrid models:the Bayesian optimization model(B-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed concrete.Furthermore,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional models.Moreover,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by water.Therefore,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.
文摘Introduction It is necessary for an ideal bioceramic scaffold to have a suitable structure.The structure can affect the mechanical properties of the scaffold(i.e.,elastic modulus and compressive strength)and the biological properties of the scaffold(i.e.,degradability and cell growth rate).Lattice structure is a kind of periodic porous structure,which has some advantages of light weight and high strength,and is widely used in the preparation of bioceramic scaffolders.For the structure of the scaffold,high porosity and large pore size are important for bone growth,bone integration and promoting good mechanical interlocking between neighboring bones and the scaffold.However,scaffolds with a high porosity often lack mechanical strength.In addition,different parts of the bone have different structural requirements.In this paper,scaffolds with a non-uniform structure or a hierarchical structure were designed,with loose and porous exterior to facilitate cell adhesion,osteogenic differentiation and vascularization as well as relatively dense interior to provide sufficient mechanical support for bone repair.Methods In this work,composite ceramics scaffolds with 10%akermanite content were prepared by DLP technology.The scaffold had a high porosity outside to promote the growth of bone tissue,and a low porosity inside to withstand external forces.The compressive strength,fracture form,in-vitro degradation performance and bioactivity of graded bioceramic scaffolds were investigated.The models of scaffolds were imported into the DLP printer with a 405 nm light.The samples were printed with the intensity of 8 mJ/cm^(2)and a layer thickness of 50μm.Finally,the ceramic samples were sintered at 1100℃.The degradability of the hierarchical gyroid bioceramic scaffolds was evaluated through immersion in Tris-HCl solution and SBF solution at a ratio of 200 mL/g.The bioactivity of bioceramic was obtained via immersing them in SBF solution for two weeks.The concentrations of calcium,phosphate,silicon,and magnesium ions in the soaking solution were determined by an inductively coupled plasma optical emission spectrometer.Results and discussion In this work,a hierarchical Gyroid structure HA-AK10 scaffold(sintered at 1100℃)with a radial internal porosity of 50%and an external porosity of 70%is prepared,and the influence of structural form on the compressive strength and degradation performance of the scaffold is investigated.The biological activity of the bioceramics in vitro is also verified.The mechanical simulation results show that the stress distribution corresponds to the porosity distribution of the structure,and the low porosity is larger and the overall stress concentration phenomenon does not appear.After soaking in SBF solution,Si—OH is firstly formed on the surface of bioceramics,and then silicon gel layer is produced due to the presence of calcium and silicon ions.The silicon gel layer is dissociated into negatively charged groups under alkaline environment secondary adsorption of calcium ions and phosphate ions,forming amorphous calcium phosphate,and finally amorphous calcium phosphate crystals and adsorption of carbonate ions,forming carbonate hydroxyapatite.This indicates that the composite bioceramics have a good biological activity in-vitro and can provide a good environment for the growth of bone cells.A hierarchical Gyroid ceramic scaffold with a bone geometry is prepared via applying the hierarchical structure to the bone contour scaffold.The maximum load capacity of the hierarchical Gyroid ceramic scaffold is 8 times that of the uniform structure.Conclusions The hierarchical structure scaffold designed had good overall compressive performance,good degradation performance,and still maintained a good mechanical stability during degradation.In addition,in-vitro biological experimental results showed that the surface graded composite scaffold could have a good in-vitro biological activity and provide a good environment for bone cells.Compared to the heterosexual structure,the graded scaffold had greater mechanical properties.
基金supported by the National Key Research and Development Program of China(No.2023YFC3707002).
文摘The compressive strength of the pellets is a key indicator that determines the production efficiency in straight grate.It usually relies on manual sampling and testing,which is cumbersome and inefficient.To address this,a time series prediction model for pellet compressive strength was developed,combining a gradient boosting decision tree with a temporal convolutional network(GBDT-TCN).Firstly,the key physical characteristics of the pellet production process were established through the feature construction method,and then the multicollinear features were eliminated based on the Spearman correlation coefficient.The final selection of feature parameters,amounting to 9,was determined using recursive feature elimination(RFE)method.Finally,the GBDT algorithm was used to establish the nonlinear relationship between these features and the compressive strength.The GBDT prediction results and process data were constructed into a time series dataset,which was input into the TCN unit cascade model.The time series information was captured through the distribution coefficient of the loss function in the time series.Results illustrate that the GBDT-TCN method proposed performs well in the task of predicting the compressive strength of pellets.Compared with the prediction model using only GBDT,the accuracy within±100 N is increased from 83.33%to 90.00%.
基金The support of Prince Sultan University for paying the Article Processing Charge(APC)of this publication and their support.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R300).
文摘Soilcrete is a composite material of soil and cement that is highly valued in the construction industry.Accurate measurement of its mechanical properties is essential,but laboratory testing methods are expensive,timeconsuming,and include inaccuracies.Machine learning(ML)algorithms provide a more efficient alternative for this purpose,so after assessment with a statistical extraction method,ML algorithms including back-propagation neural network(BPNN),K-nearest neighbor(KNN),radial basis function(RBF),feed-forward neural networks(FFNN),and support vector regression(SVR)for predicting the uniaxial compressive strength(UCS)of soilcrete,were proposed in this study.The developed models in this study were optimized using an optimization technique,gradient descent(GD),throughout the analysis(direct optimization for neural networks and indirect optimization for other models corresponding to their hyperparameters).After doing laboratory analysis,data pre-preprocessing,and data-processing analysis,a database including 600 soilcrete specimens was gathered,which includes two different soil types(clay and limestone)and metakaolin as a mineral additive.80%of the database was used for the training set and 20%for testing,considering eight input parameters,including metakaolin content,soil type,superplasticizer content,water-to-binder ratio,shrinkage,binder,density,and ultrasonic velocity.The analysis showed that most algorithms performed well in the prediction,with BPNN,KNN,and RBF having higher accuracy compared to others(R^(2)=0.95,0.95,0.92,respectively).Based on this evaluation,it was observed that all models show an acceptable accuracy rate in prediction(RMSE:BPNN=0.11,FFNN=0.24,KNN=0.05,SVR=0.06,RBF=0.05,MAD:BPNN=0.006,FFNN=0.012,KNN=0.008,SVR=0.006,RBF=0.009).The ML importance ranking-sensitivity analysis indicated that all input parameters influence theUCS of soilcrete,especially the water-to-binder ratio and density,which have themost impact.
基金funded by the Natural Science Foundation of China(Grant No.52090084)was partially supported by the Sand Hazards and Opportunities for Resilience,Energy,and Sustainability(SHORES)Center,funded by Tamkeen under the NYUAD Research Institute Award CG013.
文摘This study focuses on empirical modeling of the strength characteristics of urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization with ordinary Portland cement(OPC).For dataset collection,an extensive experimental program was designed to estimate the unconfined compressive strength(Qu)of heavy metal-contaminated soils collected from awide range of land use pattern,i.e.residential,industrial and roadside soils.Accordingly,a robust comparison of predictive performances of four data-driven models including extreme learning machines(ELMs),gene expression programming(GEP),random forests(RFs),and multiple linear regression(MLR)has been presented.For completeness,a comprehensive experimental database has been established and partitioned into 80%for training and 20%for testing the developed models.Inputs included varying levels of heavy metals like Cd,Cu,Cr,Pb and Zn,along with OPC.The results revealed that the GEP model outperformed its counterparts:explaining approximately 96%of the variability in both training(R2=0.964)and testing phases(R^(2)=0.961),and thus achieving the lowest RMSE and MAE values.ELM performed commendably but was slightly less accurate than GEP whereas MLR had the lowest performance metrics.GEP also provided the benefit of traceable mathematical equation,enhancing its applicability not just as a predictive but also as an explanatory tool.Despite its insights,the study is limited by its focus on a specific set of heavy metals and urban soil samples of a particular region,which may affect the generalizability of the findings to different contamination profiles or environmental conditions.The study recommends GEP for predicting Qu in heavy metal-contaminated soils,and suggests further research to adapt these models to different environmental conditions.
基金supported by the Postgraduate Innovation Program of Chongqing University of Science and Technology(Grant No.YKJCX2420605)Research Foundation of Chongqing University of Science and Technology(Grant No.ckrc20241225)+1 种基金Opening Projects of State Key Laboratory of Solid Waste Reuse for Building Materials(Grant No.SWR-2021-005)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202401510)。
文摘Foam concrete is widely used in engineering due to its lightweight and high porosity.Its compressive strength,a key performance indicator,is influenced by multiple factors,showing nonlinear variation.As compressive strength tests for foam concrete take a long time,a fast and accurate prediction method is needed.In recent years,machine learning has become a powerful tool for predicting the compressive strength of cement-based materials.However,existing studies often use a limited number of input parameters,and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.This study selects foam concrete density,water-to-cement ratio(W/C),supplementary cementitious material replacement rate(SCM),fine aggregate to binder ratio(FA/Binder),superplasticizer content(SP),and age of the concrete(Age)as input parameters,with compressive strength as the output.Five different machine learning models were compared,and sensitivity analysis,based on Shapley Additive Explanations(SHAP),was used to assess the contribution of each input parameter.The results show that Gaussian Process Regression(GPR)outperforms the other models,with R2,RMSE,MAE,and MAPE values of 0.95,1.6,0.81,and 0.2,respectively.It is because GPR,optimized through Bayesian methods,better fits complex nonlinear relationships,especially considering a large number of input parameters.Sensitivity analysis indicates that the influence of input parameters on compressive strength decreases in the following order:foam concrete density,W/C,Age,FA/Binder,SP,and SCM.
基金supported by the National Natural Science Foundation of China(Nos.51874068 and 52074062)the Open Funds from the Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines,Northeastern University,China(No.DM2023B03).
文摘During rock drilling and blasting activities,stemming blast holes is to prevent high-pressure explosive gases from the holes,thereby enhancing the overall blasting effectiveness.Hence,it is imperative to investigate the dynamic mechanical properties of the stem-ming materials.In this study,impact compression tests were conducted on self-swelling cartridges(SSCs)using a split Hopkinson pres-sure bar(SHPB),aiming to evaluate dynamic performances across strain rate range of 20 to 65 s^(−1).Test results indicate that the dynamic compressive strength of SSCs exhibits the following trends:it increases with increasing density of SSC,decreases with an increase in insertion gap,and follows an initial rise and subsequent fall trend with an increase in water absorption.The order of significance among these factors is density>water absorption>insertion gaps.SSCs exhibit a pronounced strain-rate strengthening dependence in dynamic compressive strength.Furthermore,both the compressive peak stress and peak strain of SSCs follow a well-defined quadratic upward trend with increasing strain rates.As the strain rate increases,the degree of fragmentation,absorbed energy,and dynamic increase factor exhibit an upward trend.Model experimental results indicate that,compared to cementitious stemming materials,SSCs can prolong the duration of gas explosion action.Therefore,SSCs are more suitable for high strain-rate applications such as blasting stemming and rock burst control.
文摘This study investigates the effect of different in situ conditions like flaw infill,heat-treatment temperatures,and sample porosities on the anisotropic compressive response of jointed samples with an impersistent flaw.Jointed samples of different porosities are prepared by mixing Plaster of Paris(POP)with different water contents,i.e.60%(i.e.for lower porosity)and 80%(i.e.for higher porosity).These samples are grouted with different infill materials,i.e.un-grouted,cement and sand-cement(3:1)-bio-concrete(SCB)mix and subsequently subjected to different temperatures,i.e.100℃,200℃ and 300℃.The results reveal the distinct stages in the stress-strain responses of samples characterized by initial micro-cracks closure,elastic transition,and non-linear response till peak followed by a post-peak behaviour.The un-grouted samples exhibit their lowest strength at 30°joint orientation.The ratios of maximum to minimum strength are 3.11 and 3.22 with varying joint orientations for lower and higher porosity samples,respectively.Strengths of cement and SCB mix grouted samples are increased for all joint orientations ranging between 16.13%-69.83%and 18.04%-73%at low porosity and 22%-48.66%and 27.77%-51.57%at high porosity,respectively as compared to the un-grouted samples.However,the strength of the grouted samples is decreased by 66.94%-75.47%and 77.17%-81.05%at lower porosity,and 79.37%-82.86%and 81.29%-95.55%at higher porosity for cement and for SCB grouts with an increase in the heating temperature from 30℃ to 300℃,respectively.These observations could be due to the suppression of favourable crack initiation locations,i.e.flaw tips along the samples due to the filling of the crack by grouting and generation of thermal cracks with temperature.The mechanism of strength behaviour is elucidated in detail based on fracture propagation analysis and the anisotropic response of with or,without grouted samples.
基金financiallysupported by the National Natural Science Foundation of China(Grant No.42577209)the Natural Science Foundation of Jiangsu Province(Grant No.BK20241489)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering,Institute of Rock and Soil Mechanics,Chinese Academy of Sciences(Grant No.SKLGME023009).
文摘This study systematically investigated the coupling effects of confiningpressure and strain rate on the dynamic strength of granite through dynamic triaxial compression tests.A dynamic strength criterion was developed to incorporate these coupling effects for further analysis.Moreover,the research thoroughly revealed the underlying mechanism by which these coupling effects influencethe rock strength.The results revealed that both confiningpressure and strain rate significantly enhanced the dynamic strength of rock;however,a mutual inhibition effect emerged under their coupling.Specifically,as the confiningpressure increased,the strengthening effect of strain rate gradually diminished.Conversely,increasing the strain rate weakened the strengthening effect of confiningpressure.The proposed strength criterion successfully predicted rock strength under various confiningpressures(0-225 MPa)and strain rates(10^(-6)-600 s^(-1)).It achieved an average prediction error of only 8.3%,which represents a 65%improvement in accuracy compared to models that consider confiningpressure and strain rate effects independently.At the micro-mechanism level,increasing confiningpressure and strain rate promoted crack propagation in a transgranular(TG)mode,thereby enhancing the overall rock strength.However,under the coupling effects,the interference and interaction of TG cracks weakened the overall strengthening effect.This indicated that the competitive interaction between confiningpressure and strain rate during crack propagation constitutes the intrinsic mechanism underlying their mutual inhibitory effect on rock strength.This study provides a more accurate theoretical basis for understanding the dynamic responses of rocks and contributes valuable insights for disaster prevention and control in deep rock engineering projects.
基金Project(GZB202405561)supported by the China Postdoctoral Fellowship ProgramProject(42377154)supported by the National Natural Science Foundation of China。
文摘The determination of discontinuity shear strength is an important concern in rock engineering.Previous research mainly focused on the shear behavior of discontinuities with identical joint wall compressive strengths(DIJCS).However,the shear behavior of discontinuities with different joint wall compressive strengths(DDJCS)and 3D surface morphology had been rarely reported.In this study,matched mortar DDJCSs were prepared using 3D printed photosensitive resin molds.Direct shear tests were carried out under three kinds of normal stress(ranging from 0.5 to 3.0 MPa)to analyze the shear strength and contact zones of DDJCS during shearing.The results show that the contact zones of DDJCS during shearing are scattered in the steep zones facing the shear direction.It is verified that Grasselli and Develi’s directional surface roughness characterization method can be used to predict the shear-induced potential contact zones of DDJCS.When the critical apparent dip angle is equal to the peak dilation angle,the predicted contact area agrees well with the actual contact area.A 3D directional roughness parameter with clear physical meaning was introduced to characterize discontinuity surface roughness.A 3D modified joint roughness coefficient-joint wall compressive strength(JRC-JCS)criterion that can both predict the shear strength of DDJCS and DIJCS was proposed based on the newly defined roughness parameter.The proposed criterion was validated by 77 direct shear tests presented by this study and 163 direct shear tests presented by other investigators.The results show that the proposed criterion was generally reliable for the peak shear strength prediction of DDJCS and DIJCS(within 16%).It is also found that the new criterion can capture the anisotropy of the peak shear strength of DDJCS.The anisotropy of DDJCS decreases with increasing normal stress.It should be noted that the anisotropy of the shear strength of DDJCS was not investigated experimentally,and further experiments should be conducted to verify it.
基金the financial support provided by Tianfu Yongxing Laboratory Organized Research Project Funding(No.2023CXXM01)the ARC linkage program(No.LP200100420).
文摘Ceramic spheres,typically with a particle diameter of less than 0.8 mm,are frequently utilized as a critical proppant material in hydraulic fracturing for petroleum and natural gas extraction.Porous ceramic spheres with artificial inherent pores are an important type of lightweight proppant,enabling their transport to distant fracture extremities and enhancing fracture conductivity.However,the focus frequently gravitates towards the low-density advantage,often overlooking the pore geometry impacts on compressive strength by traditional strength evaluation.This paper numerically bypasses such limitations by using a combined finite and discrete element method(FDEM)considering experimental results.The mesh size of the model undergoes validation,followed by the calibration of cohesive element parameters via the single particle compression test.The stimulation elucidates that proppants with a smaller pore size(40μm)manifest crack propagation evolution at a more rapid pace in comparison to their larger-pore counterparts,though the influence of pore diameter on overall strength is subtle.The inception of pores not only alters the trajectory of crack progression but also,with an increase in porosity,leads to a discernible decline in proppant compressive strength.Intriguingly,upon crossing a porosity threshold of 10%,the decrement in strength becomes more gradual.A denser congregation of pores accelerates crack propagation,undermining proppant robustness,suggesting that under analogous conditions,hollow proppants might not match the strength of their porous counterparts.This exploration elucidates the underlying mechanisms of proppant failure from a microstructural perspective,furnishing pivotal insights that may guide future refinements in the architectural design of porous proppant.
基金the National Natural Science Foundation of China(Grant Nos.52308403 and 52079068)the Yunlong Lake Laboratory of Deep Underground Science and Engineering(No.104023005)the China Postdoctoral Science Foundation(Grant No.2023M731998)for funding provided to this work.
文摘The uniaxial compressive strength(UCS)of rocks is a vital geomechanical parameter widely used for rock mass classification,stability analysis,and engineering design in rock engineering.Various UCS testing methods and apparatuses have been proposed over the past few decades.The objective of the present study is to summarize the status and development in theories,test apparatuses,data processing of the existing testing methods for UCS measurement.It starts with elaborating the theories of these test methods.Then the test apparatus and development trends for UCS measurement are summarized,followed by a discussion on rock specimens for test apparatus,and data processing methods.Next,the method selection for UCS measurement is recommended.It reveals that the rock failure mechanism in the UCS testing methods can be divided into compression-shear,compression-tension,composite failure mode,and no obvious failure mode.The trends of these apparatuses are towards automation,digitization,precision,and multi-modal test.Two size correction methods are commonly used.One is to develop empirical correlation between the measured indices and the specimen size.The other is to use a standard specimen to calculate the size correction factor.Three to five input parameters are commonly utilized in soft computation models to predict the UCS of rocks.The selection of the test methods for the UCS measurement can be carried out according to the testing scenario and the specimen size.The engineers can gain a comprehensive understanding of the UCS testing methods and its potential developments in various rock engineering endeavors.
文摘Uniaxial compressive strength(UCS)is a significant mechanical measure in rock engineering,key for classifying rock masses,conducting stability assessments,and guiding design processes.Over the past five decades,numerous techniques and devices have emerged for UCS measurement.This paper presents a comprehensive literature review of existing methodologies and advancements in UCS testing,examining the theoretical foundations,testing equipment,data processing techniques,and criteria for selecting appropriate UCS testing methods.Additionally,the study highlights a shift toward automated,precise,and computational approaches with multiple inputs(i.e.multiple regression,machine learning,ML)for UCS prediction.Approximately 221 articles published by various researchers between 2000 and 2024 related to ML were reviewed,focusing on the application of ML models,including artificial neural networks(ANNs),adaptive-network-based fuzzy inference system(ANFIS),random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGBoost),in UCS prediction.The review also observed the growing use of hybrid models integrating ML with optimization techniques,significantly improving UCS estimation.Numerous empirical correlations,both direct and indirect,have been established in the past several years based on the physical properties of rocks.However,utilizing these proposed equations to reliably estimate UCS remains challenging due to the variability in lithology,rock origin,and other factors.This study systematically presents the established correlation expressions,considering their lithology,the number of samples used to establish the expressions,the coefficient of determination(R2),and the locations.This allows geologists and engineers to easily apply these established expressions in situations where direct estimation is not possible,enabling them to approximate UCS results.
基金The National Basic Research Program of China (973Program)(No2000CB610703)
文摘The pozzolanic activity of coal gangue, which is calcining at 500 to 1 000 ℃, differs distinctly. The simplex-centroid design with upper and lower bounds of component proportion is adopted to study the compressive strength of mortars made with ternary blends of cement, activated coal gangue and fly ash. Based on the results of a minimum of seven design points, three special cubic polynomial models are used to establish the strength predicating equations at different ages for mortars. Five experimental checkpoints were also designed to verify the precision of the equations. The most frequent errors of the predicted values are within 3%. A simple and practical way is provided for determining the optimal proportion of two admixtures when they are used in concrete.
基金Project(51408213)supported by the National Natural Science Foundation of China
文摘A series of compression tests were conducted on 150 groups of cement paste specimens with side lengths ranging from 40 mm to 200 mm. The specimens include cube specimens and prism specimens with height to width ratio of 2. The experiment results show that size effect exists in the cubic compressive strength and prismatic compressive strength of the cement paste, and larger specimens resist less in terms of strength than smaller ones. The cubic compressive strength and the prismatic compressive strength of the specimens with side length of 200 mm are respectively about 91% and 89% of the compressive strength of the specimens with the side length of 40 mm. Water to binder ratio has a significant influence on the size effect of the compressive strengths of the cement paste. With a decrease in the water to binder ratio, the size effect is significantly enhanced. When the water to binder ratio is 0.2, the size effects of the cubic compressive strength and the prismatic compressive strength of the cement paste are 1.6 and 1.4 times stronger than those of a water to binder ratio of 0.6. Furthermore, a series of formulas are proposed to calculate the size effect of the cubic compressive strength and the prismatic compressive strength of cement paste, and the results of the size effect predicted by the formulas are in good agreement with the experiment results.
文摘A theoretical calculation method of the axial compressive strength of a high strength concrete with fibre reinforced plastics (FRP) constraint is proposed. It is shown by test verification that the FRP strength devotion factor used for this method is in accordance with actual conditions. FRP is not up to the ultimate strength when the concrete reaches the ultimate strength, whose strength devotion factor is in the range of 0.28 to 0.59, which is related to an elastic modulus. The method can be used to estimate axial compressive strength of the concrete strengthened with FRP. The theoretical strength is 10% to 30% higher than the measured one. The deviation comes mainly from a non-ideal bonding condition of FRP-concrete interfaces and discrete property of the testing data of compressive strength.
文摘To test the influence of binder strength, porous concretes with 4 binder strengths between 30.0-135.0 MPa and 5 void ratios between 15%-35% were tested. The results indicated that for the same aggregate, the rates of strength reduction due to the increases in void ratio were the same for binders with different strengths. To study the influence of aggregate size, 3 single size aggregates with nominal sizes of 5.0, 13.0 and 20.0 mm （Nos. 7, 6 and 5 according to JIS A 5001） were used to make porous concrete. The strengths of porous concrete are found to be dependent on aggregate size. The rate of strength reduction of porous concrete with small aggregate size is found to be higher than that with larger aggregate size. At the same void ratio, the strength of porous concrete with large aggregate is larger than that with small aggregate. The general equations for porous concrete are related to compressive strength and void ratio for different binder strengths and aggregate sizes.
基金Funded by the National Basic Research Program of China (No.2009CB623105)
文摘Surfaces of grade III fly ashes were modified through mixing with carbide slag and calcining at 850 ℃ for 1 h. Mineralogical compositions and surface morphology of fly ashes before and after modification were characterized by X-ray diffraction （XRD） and scanning electron microscopy （SEM）, respectively. Effect of surface-modified fly ashes on compressive strength and autogenous shrinkage of blended cement pastes was investigated. Microstructures of cement pastes were examined by backscattered electron （BSE） imaging and mercury intrusion porosimetry （MIP）. The experimental results showed that β-C2S was formed on the surfaces of fly ashes after modification. Hydration ofβ-C2S on the surface-modified fly ashes densified interface zone and enhanced bond strength between particles of fly ashes and hydrated clinkers. In addition, surface modification of fly ashes tended to decrease total porosity and 10-50 nm pores of cement pastes. Surface modification of fly ashes increased compressive strength and reduced autogenous shrinkage of cement pastes.
