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
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
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
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
In cold regions,the dynamic compressive strength(DCS)of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering.Nevertheless,testing the dynamic strength under freeze-thaw wea...In cold regions,the dynamic compressive strength(DCS)of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering.Nevertheless,testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive.Therefore,this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques.Initially,a database of DCS for frozen-thawed rocks,comprising 216 rock specimens,was compiled.Three external load parameters(freeze-thaw cycle number,confining pressure,and impact pressure)and two rock parameters(characteristic impedance and porosity)were selected as input variables,with DCS as the predicted target.This research optimized the kernel scale,penalty factor,and insensitive loss coefficient of the support vector regression(SVR)model using five swarm intelligent optimization algorithms,leading to the development of five hybrid models.In addition,a statistical DCS prediction equation using multiple linear regression techniques was developed.The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes.A sensitivity analysis based on the cosine amplitude method has also been conducted.The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions.Among these models,the SVR model optimized with the chameleon swarm algorithm exhibited the best performance,with metrics indicating its effectiveness,including root mean square error(RMSE)﹦3.9675,mean absolute error(MAE)﹦2.9673,coefficient of determination(R^(2))﹦0.98631,and variance accounted for(VAF)﹦98.634.This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models.Notably,impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction.This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.展开更多
Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the prope...Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.展开更多
Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern const...Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.展开更多
Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventio...Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.展开更多
This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf o...This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.展开更多
The high-performance Basalt Fiber Reinforced Polymer(BFRP)composites have been prepared by guiding Micro/Nano Short Aramid Fiber(MNSAF)into the interlayer to improve the resin-rich region and the interfacial transitio...The high-performance Basalt Fiber Reinforced Polymer(BFRP)composites have been prepared by guiding Micro/Nano Short Aramid Fiber(MNSAF)into the interlayer to improve the resin-rich region and the interfacial transition region,and the flexible fiber bridging claws of MNSAF were constructed to grasp the adjacent layers for stronger interlaminar bond.The lowvelocity impact results show that the MNSAF could improve the impact resistance of BFRP composites.The compression test results demonstrate that the compressive strength and the residual compressive strength after impact of MNSAF-reinforced BFRP composites were greater than those of unreinforced one,exhibiting the greatest 56.2% and 73.3% increments respectively for BFRP composites improved by 4wt%MNSAF.X-ray micro-computed tomography scanning results indicate that the“fiber bridging claws”contributed to better mechanical interlocking to inhibit the crack generation and propagation under impact and compression load,and the original delamination-dominated failure of unreinforced BFRP composites was altered into sheardominated failure of MNSAF-reinforced BFRP composites.Overall,the MNSAF interleaving might be an effective method in manufacturing high-performance laminated fiber in industrial production.展开更多
Phosphate tailings are usually used as backfill material in order to recycle tailings resources.This study considers the effect of the mix proportions of clinker-free binders on the fluidity,compressive strength and o...Phosphate tailings are usually used as backfill material in order to recycle tailings resources.This study considers the effect of the mix proportions of clinker-free binders on the fluidity,compressive strength and other key performances of cementitious backfill materials based on phosphate tailings.In particular,three solid wastes,phosphogypsum(PG),semi-aqueous phosphogypsum(HPG)and calcium carbide slag(CS),were selected to activate wet ground granulated blast furnace slag(WGGBS)and three different phosphate tailings backfill materials were prepared.Fluidity,rheology,settling ratio,compressive strength,water resistance and ion leaching behavior of backfill materials were determined.According to the results,when either PG or HPG is used as the sole activator,the fluidity properties of the materials are enhanced.Phosphate tailings backfill material activated with PG present the largest fluidity and the lowest yield stress.Furthermore,the backfill material’s compressive strength is considerably increased to 2.9 MPa at 28 days after WGGBS activation using a mix of HPG and CS,all with a settling ratio of only 1.15 percent.Additionally,all the three ratios of binder have obvious solidification effects on heavy metal ions Cu and Zn,and P in phosphate tailings.展开更多
River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study d...River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study deals with the properties of cement mortar containing different levels of manufactured sand(MS)based on quartzite,used to replace river sand.The river sand was replaced at 20%,40%,60%and 80%with MS(by weight or volume).The mechanical properties,transfer properties,and microstructure were examined and compared to a control group to study the impact of the replacement level.The results indicate that the compressive strength can be improved by increasing such a level.The strength was improved by 35.1%and 45.5%over that of the control mortar at replacement levels of 60%and 80%,respectively.Although there was a weak link between porosity and gas permeability in the mortars with manufactured sand,the gas permeability decreased with growing the replacement level.The microstructure of the MS mortar was denser,and the cement paste had fewer microcracks with increasing the replacement level.展开更多
Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests...Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.展开更多
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.
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.
基金supported by the National Natural Science Foundation of China(Grant No.42072309)the Knowledge Innovation Program of Wuhan-Basic Research(Grant No.2022020801010199)the Fundamental Research Funds for National University,China University of Geosciences(Wuhan)(Grant No.CUGDCJJ202217).
文摘In cold regions,the dynamic compressive strength(DCS)of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering.Nevertheless,testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive.Therefore,this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques.Initially,a database of DCS for frozen-thawed rocks,comprising 216 rock specimens,was compiled.Three external load parameters(freeze-thaw cycle number,confining pressure,and impact pressure)and two rock parameters(characteristic impedance and porosity)were selected as input variables,with DCS as the predicted target.This research optimized the kernel scale,penalty factor,and insensitive loss coefficient of the support vector regression(SVR)model using five swarm intelligent optimization algorithms,leading to the development of five hybrid models.In addition,a statistical DCS prediction equation using multiple linear regression techniques was developed.The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes.A sensitivity analysis based on the cosine amplitude method has also been conducted.The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions.Among these models,the SVR model optimized with the chameleon swarm algorithm exhibited the best performance,with metrics indicating its effectiveness,including root mean square error(RMSE)﹦3.9675,mean absolute error(MAE)﹦2.9673,coefficient of determination(R^(2))﹦0.98631,and variance accounted for(VAF)﹦98.634.This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models.Notably,impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction.This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.
基金This work has been supported by the Conselleria de Inno-vación,Universidades,Ciencia y Sociedad Digital de la Generalitat Valenciana(CIAICO/2021/335).
文摘Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.
文摘Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2023R809).
文摘Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.
基金supported via funding from Prince Sattam Bin Abdulaziz University Project Number(PSAU/2023/R/1445).
文摘This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.
基金supported financially by the National Natural Science Foundation of China(No.52102115)the High-end Foreign Expert Recruitment Plan of China(No.G2023036002L)+1 种基金the Basalt Fiber and Composite Key Laboratory of Sichuan Province,China(No.XXKFJJ202308)Shock and Vibration of Engineering Materials and Structures Key Lab of Sichuan Province,China(No.23kfgk06).
文摘The high-performance Basalt Fiber Reinforced Polymer(BFRP)composites have been prepared by guiding Micro/Nano Short Aramid Fiber(MNSAF)into the interlayer to improve the resin-rich region and the interfacial transition region,and the flexible fiber bridging claws of MNSAF were constructed to grasp the adjacent layers for stronger interlaminar bond.The lowvelocity impact results show that the MNSAF could improve the impact resistance of BFRP composites.The compression test results demonstrate that the compressive strength and the residual compressive strength after impact of MNSAF-reinforced BFRP composites were greater than those of unreinforced one,exhibiting the greatest 56.2% and 73.3% increments respectively for BFRP composites improved by 4wt%MNSAF.X-ray micro-computed tomography scanning results indicate that the“fiber bridging claws”contributed to better mechanical interlocking to inhibit the crack generation and propagation under impact and compression load,and the original delamination-dominated failure of unreinforced BFRP composites was altered into sheardominated failure of MNSAF-reinforced BFRP composites.Overall,the MNSAF interleaving might be an effective method in manufacturing high-performance laminated fiber in industrial production.
基金the Key Research and Development Program of Hubei Province(2022BCA071)the Wuhan Science and Technology Bureau(2022020801020269).
文摘Phosphate tailings are usually used as backfill material in order to recycle tailings resources.This study considers the effect of the mix proportions of clinker-free binders on the fluidity,compressive strength and other key performances of cementitious backfill materials based on phosphate tailings.In particular,three solid wastes,phosphogypsum(PG),semi-aqueous phosphogypsum(HPG)and calcium carbide slag(CS),were selected to activate wet ground granulated blast furnace slag(WGGBS)and three different phosphate tailings backfill materials were prepared.Fluidity,rheology,settling ratio,compressive strength,water resistance and ion leaching behavior of backfill materials were determined.According to the results,when either PG or HPG is used as the sole activator,the fluidity properties of the materials are enhanced.Phosphate tailings backfill material activated with PG present the largest fluidity and the lowest yield stress.Furthermore,the backfill material’s compressive strength is considerably increased to 2.9 MPa at 28 days after WGGBS activation using a mix of HPG and CS,all with a settling ratio of only 1.15 percent.Additionally,all the three ratios of binder have obvious solidification effects on heavy metal ions Cu and Zn,and P in phosphate tailings.
基金supported by the National Natural Science Foundation of China(No.51709097).
文摘River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study deals with the properties of cement mortar containing different levels of manufactured sand(MS)based on quartzite,used to replace river sand.The river sand was replaced at 20%,40%,60%and 80%with MS(by weight or volume).The mechanical properties,transfer properties,and microstructure were examined and compared to a control group to study the impact of the replacement level.The results indicate that the compressive strength can be improved by increasing such a level.The strength was improved by 35.1%and 45.5%over that of the control mortar at replacement levels of 60%and 80%,respectively.Although there was a weak link between porosity and gas permeability in the mortars with manufactured sand,the gas permeability decreased with growing the replacement level.The microstructure of the MS mortar was denser,and the cement paste had fewer microcracks with increasing the replacement level.
基金supported by the National Natural Science Foundation of China,NSFC(No.42202318).
文摘Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.
