Conglomerate rock's complex and heterogeneous microstructure significantly affects its mechanical properties,especially under dynamic loading.However,research on their dynamic behavior and fracture mechanisms is l...Conglomerate rock's complex and heterogeneous microstructure significantly affects its mechanical properties,especially under dynamic loading.However,research on their dynamic behavior and fracture mechanisms is limited.Through uniaxial compression tests and split Hopkinson pressure bar(SHPB)impact tests,the dynamic compressive mechanical properties and fracture mechanisms of conglomerate rock were studied.Nanoindentation and high-resolution X-ray computed tomography were employed to analyze the micro-mechanical behavior and internal structure of the conglomerate rock.Results indicate significant differences in mechanical properties between different gravel particles and cementing materials,with initial fractures primarily distributed at the gravel-cement interfaces.The dynamic mechanical properties of conglomerate rocks exhibit a clear strain rate dependency.Based on the stress−strain curves and failure characteristics,the dynamic compressive mechanical behavior can be categorized into two types using a critical strain rate.The dynamic compressive strength,peak strain,and toughness of conglomerate rock increased with the strain rate,with the strength at 54 s−1 being 2.6 times that at 6 s−1.The dynamic compressive fracture mechanism of conglomerate rock is related to the strain rate and microstructure;at low strain rates,gravel distribution is the key factor,whereas at high strain rates,gravel content becomes critical.展开更多
This study proposes to use the unconfined compressive strength(UCS)and the bender element(BE)tests for determining the strength and the initial small-strain shear modulus of Bangkok soft marine clay improved by cement...This study proposes to use the unconfined compressive strength(UCS)and the bender element(BE)tests for determining the strength and the initial small-strain shear modulus of Bangkok soft marine clay improved by cement and polyester fibers.This study varies the content of admixed cement(1%–20%)and polyester fibers(0–20%),including the curing time(3–28 d)for preparing 360 samples.Moreover,this study uses the Michaelis-Menten kinetics concept to model cement hydration saturation.From the study,it is concluded as follows.The modelled results reveals that at least 10%cement and 1%polyester fiber are recommended to attain the 28-d UCS standards(294 kPa)for highway subgrade materials in Thailand.This also fulfils sustainable construction due to reducing normal-use cement from 20%to 10%.Unfortunately,the addition of polyester fibers into the Bangkok clay with at least 5%cement reduces shear modulus by 1.12–1.32 times.The Abram's relationship between shear modulus and the mixing-water-to-cement ratio is found time-dependent.From the composite theory,the BE detects the polyester fiber zone as a defect in the Bangkok clay(matrix)with 5%–20%cement.So,the 28-d shear modulus in the polyester fiber zone is negative(up to0.034 MPa for 20%fiber),similar to softening phenomenon in concrete cracking(negative stiffness).For the 28-d shear modulus of fiber zone,the optimum cement content is around 2%for the positive influences of polyester fibers.Experimentally,the timedependent normalized UCS for 10%and 20%cement is compatible with other studies,and its development rate increases with the cement content as 0.3017,0.3172 and 0.3204 for 5%,10%and 20%cement,respectively.The 28-d relationship between shear modulus and UCS shows that low-cement soft clay requires high polyester fiber content(5%–20%)to activate UCS improvement.However,the soft clay with enough cement(20%)causes the uniformly distributed UCS improvement.展开更多
High-performance fiber fabrics and composites experienced transverse compression deformation at ultrahigh strain rates near the impact point when subjected to high-velocity impacts,which significantly affected their b...High-performance fiber fabrics and composites experienced transverse compression deformation at ultrahigh strain rates near the impact point when subjected to high-velocity impacts,which significantly affected their ballistic limits.In this paper,a fiber-scale experimental method for characterizing ultrahigh strain-rate transverse compression behavior was proposed.To begin with,in order to measure the extremely low stress and strain in small specimens,the conventional Hopkinson bar was reduced to the hundred-micron scale,thereby achieving wave impedance matching with single fibers.In addition,tangential and normal laser Doppler velocimetry(LDV)methods were employed to realize non-contact,high-precision,and high-speed axial velocity measurements of micron-scale incident and transmission bars,respectively.Meanwhile,a microscopic observation system was used to facilitate the installation of miniature fiber samples.The experimental setup and procedures were introduced,and the system accuracy was verified through sample-free loading tests based on one-dimensional stress wave propagation theory.Dynamic compression experiments on Graphene-UHMWPE fibers were carried out,followed by post-compression microstructural characterization via scanning electron microscopy(SEM).Results demonstrated that successful mechanical characterization was achieved at strain rates exceeding 105,an order of magnitude higher than the previously reported maximum rates.Furthermore,during the loading process,the fibers underwent uniform compression deformation while exhibiting pronounced strain-rate effects.This method offers a novel approach for dynamic mechanical characterization of microscale single fibers,enabling the development of comprehensive strain-ratedependent material models to guide the design of advanced composites and high-performance fibers.展开更多
The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)bi...The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)biopolymer to ameliorate residual granitic soil(RGS)in arid and semiarid regions can significantly mitigate soil erosion.This study investigates the enhancement of RGS by cross-linking the PBS and XG biopolymers.Employing a multitude of geotechnical tests(liquid limit,linear shrinkage,specific gravity,compaction,and UCS tests)at 3 d,28 d,and 90 d of steam-curing at a controlled temperature of 16℃,the outcomes were validated through scanning electron microscopy(SEM),thermogravimetric analysis(TGA),Fourier transform infrared spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET)analyses.In addition,a comprehensive experimental database of 150 tests and nine parameters from the current study was utilized to model the UCS90-d(i.e.unconfined compressive strength after 90 d of curing)of the PBS-XG-treated RGS mixtures by deploying the random forest(RF)and eXtreme Gradient Boost(XGBoost)methods.The results found that the two biopolymers significantly improve the mechanical properties of RGS,with optimal UCS achieved at specific dosages(0.4PBS,1.5XG,and 0.2PBS+1.5XG dosage levels)and curing times.The UCS of PBS-XG-treated RGS showed up to a 57%increase after 90 d of curing.Furthermore,SEM and FTIR analyses revealed the formation of stronger microstructures and chemical bonds,respectively,whereas BET analysis indicated that pore volume and diameter are critical in affecting UCS.The proposed RF model outperformed XGBoost in predictive accuracy and generalization,demonstrating robustness and versatility.Moreover,SHAP values highlighted the significant impact of input parameters on UCS90-d,with curing time and specific material properties being key determinants.The study concludes with the proposal of a novel PyCharm intuitive graphical user interface as a"UCS Prediction App"for engineers and practitioners to forecast the UCS90-d of granitic residual soil.展开更多
Ultrahigh-performance concrete(UHPC)is a groundbreaking kind of concrete that distinguishes itself from conventional concrete through its unique material properties.Understanding and managing the time-dependent charac...Ultrahigh-performance concrete(UHPC)is a groundbreaking kind of concrete that distinguishes itself from conventional concrete through its unique material properties.Understanding and managing the time-dependent characteristics of these materials is essential for their effective use in various construction applications.This study presents an experimental evaluation of the compressive and bending properties of the UHPC incorporating polypropylene,steel,and glass fibers.Based on ACI-211 guidelines,the UHPC mix was designed by using three types of aggregates:limestone,andesite,and quartzite,along with 5%fiber content(at varying percentages of 0,5%,10%,15%,and 20%)relative to the cementitious materials,and three different water-to-cement(w/c)ratios(0.24,0.3,and 0.4)were used.In this research,the compressive and flexural strength tests were conducted.The results show that increasing the values of the fibers significantly enhances the compressive strength of the studied samples.Furthermore,the utilization of fibers markedly improves the bending strength of the samples,demonstrating a strong correlation with the yield resistance of the material.Also,findings show that using steel fibers increases the compressive and bending strength of the tested samples more than polypropylene and glass fibers.For instance,in UHPC samples with 0.4 w/c,the average compressive strength values are 82.2 MPa,70.3 MPa,and 67.1 MPa for steel,polypropylene,and glass fibers,respectively.Also,in the flexural strength test,the modulus of rupture is obtained as an average of 6.24 MPa,5.24 MPa and 4.83 MPa for UHPC samples with steel,polypropylene and glass fibers,respectively.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
To meet the increased demand for light-weight and high-performance special-shaped load bearing parts in automotive industry,the short carbon fiber reinforced magnesium matrix composite(C_(sf)/Mg)part with complex conf...To meet the increased demand for light-weight and high-performance special-shaped load bearing parts in automotive industry,the short carbon fiber reinforced magnesium matrix composite(C_(sf)/Mg)part with complex configuration features and abrupt cross-sectional transitions was fabricated by liquid-solid extrusion following vacuum pressure infiltration process(LSEVI).Near-net forming schemes of both the special-shaped fiber preform and composite part were proposed.The effect of process parameters on the forming quality of the composite part was discussed.Meanwhile,the microstructures and compressive properties in different regions of the part were analyzed.The results show that the forward forming scheme provides the special-shaped fiber preform with no surface defects.For the C_(sf)/AZ91D part,its internal microstructures show that the infiltration of liquid magnesium is sufficient and uniform.The compressive strength of the composite part can reach up to 487 MPa,corresponding to~40%increase compared to 335 MPa of the AZ91D alloy.The average compressive strain of composites is less than 10%,which is about 50%of that of the AZ91D alloy.When the fiber orientation is parallel to the shear direction on the shear plane,the load-bearing capacity of the fiber is much higher than that of the fiber perpendicular to the shear direction.This work not only provides a convenient approach to fabricate special-shaped preform with high fiber volume fraction,but also gives a demonstration for the near-net forming of C_(sf)/Mg parts with excellent material isotropy and compressive properties.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.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
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
Incoherent digital holography has attracted significant attention due to its advantages in threedimensional(3D)imaging under low spatial coherence conditions,such as easy access to light sources and reduced speckle no...Incoherent digital holography has attracted significant attention due to its advantages in threedimensional(3D)imaging under low spatial coherence conditions,such as easy access to light sources and reduced speckle noise.However,interlayer crosstalk during the reconstruction process leads to a substantial reduction in reconstruction fidelity.Furthermore,existing deconvolutionand deep-learning-based reconstruction algorithms face limitations in terms of effectiveness and generalization.To address these challenges,we propose a compressive incoherent digital holography(CIDH)approach for 3D imaging.In CIDH,a point spread hologram sequence with a high signal-to-noise ratio is initially obtained using a customized computergenerated holography method for dual-channel forward data acquisition.For scene reconstruction,a compressed sensing-based two-step iterative shrinkage/thresholding algorithm is employed to achieve high-fidelity 3D scene retrieval.The combined optimization demonstrates exceptional performance in suppressing interlayer crosstalk and enhancing reconstruction fidelity.In simulations,crosstalk was effectively suppressed across 10 depth layers.In experiments,successful suppression was achieved for both a five-layer transmissive object and a two-layer reflective 3D object,resulting in significantly improved reconstruction accuracy.The proposed framework shows great potential for applications in various incoherent source-illuminated and fluorescent 3D imaging.展开更多
Exploring alternative aggregates or recycled aggregates to substitute traditional concrete aggregates,particularly sand aggregates,which are becoming more limited and must comply with environmental protection standard...Exploring alternative aggregates or recycled aggregates to substitute traditional concrete aggregates,particularly sand aggregates,which are becoming more limited and must comply with environmental protection standards,is essential.Research has explored various alternative materials to sand in concrete,including concrete from demolished buildings,and broken glass from projects,among others.Investigating the use of recycled broken glass to substitute sand aggregates and implementing this research in compression columns is crucial.This paper examines the compressive behavior of reinforced concrete columns that utilize recycled glass particles as a substitute for sand in concrete.The research findings establish the relationships:load and vertical displacement,load and deformation at the column head,mid-column,and column base;the formation and propagation of cracks in the column,while considering factors such as the percentage of recycled glass,the arrangement of stirrups,and the amount of load-bearing steel influencing the performance of square reinforced concrete columns under compression.The feasibility of using recycled glass as a substitute for sand in column structures subjected to compression has been demonstrated,with the ideal replacement content for sand aggregate in reinforced concrete columns in this study ranging from 0%to 10%.The column’s load-bearing ability dropped from 250 kN to 150 kN when 100%recycled glass was used instead of sand.This is a 40%drop,and cracks started to show up sooner.The research will support recycling broken glass instead of using sand in building,improving the environment and reducing natural sand use.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
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
Accurately predicting the compressive strength of recycled aggregate concrete(RAC)incorporating supplementary cementitious materials(SCMs)remains a critical challenge due to the heterogeneous nature of recycled aggreg...Accurately predicting the compressive strength of recycled aggregate concrete(RAC)incorporating supplementary cementitious materials(SCMs)remains a critical challenge due to the heterogeneous nature of recycled aggregates(RA)and the complex interactions among multiple binder constituents.This study advances the field by developing the most extensive and rigorously preprocessed database to date,which comprises 1243 RAC mixtures containing silica fume,fly ash,and ground-granulated blast-furnace slag.A hybrid,domain-informed machine-learning framework was then proposed,coupling optimized Extreme Gradient Boosting(XGBost)with civil engineering expertise to capture the complex chemical and microstructural mechanisms that govern RAC performance.Systematic grid-search optimization(n_estimators=50,learning_rate=0.2,max_depth=7)produced superior predictive accuracy(training R^(2)=0.9923,testing R^(2)=0.937;MAE=2.378 MPa;RMSE=3.591 MPa),which outperformed Extra Trees,Light Gradient Boosting,and traditional regressors.Beyond prediction,model interpretability was achieved using Shapley additive explanations and partial dependence analyses,which revealed curing age as the dominant strength driver,while water-to-binder ratio and recycled aggregate water absorption exhibited strong negative influences.Three-dimensional interaction plots further demonstrated how optimal superplasticizer dosages reduce the strength loss associated with high recycled aggregate content.In summary,this work provides a novel,explainable,and data-driven framework that achieves high predictive accuracy with mechanistic transparency and offers a powerful,interpretable tool for the design and optimization of sustainable RAC mixtures.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
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
基金Project(51978674)supported by the National Natural Science Foundation of China。
文摘Conglomerate rock's complex and heterogeneous microstructure significantly affects its mechanical properties,especially under dynamic loading.However,research on their dynamic behavior and fracture mechanisms is limited.Through uniaxial compression tests and split Hopkinson pressure bar(SHPB)impact tests,the dynamic compressive mechanical properties and fracture mechanisms of conglomerate rock were studied.Nanoindentation and high-resolution X-ray computed tomography were employed to analyze the micro-mechanical behavior and internal structure of the conglomerate rock.Results indicate significant differences in mechanical properties between different gravel particles and cementing materials,with initial fractures primarily distributed at the gravel-cement interfaces.The dynamic mechanical properties of conglomerate rocks exhibit a clear strain rate dependency.Based on the stress−strain curves and failure characteristics,the dynamic compressive mechanical behavior can be categorized into two types using a critical strain rate.The dynamic compressive strength,peak strain,and toughness of conglomerate rock increased with the strain rate,with the strength at 54 s−1 being 2.6 times that at 6 s−1.The dynamic compressive fracture mechanism of conglomerate rock is related to the strain rate and microstructure;at low strain rates,gravel distribution is the key factor,whereas at high strain rates,gravel content becomes critical.
基金allocated by National Science,Research and Innovation Fund(NSRF)King Mongkut's University of Technology North Bangkok(project no.KMUTNB-FF-67-B-44 and KMUTNB-FF-67-B-45)supported by the NSRF through the Program Management Unit for Human Resources&Institutional Development,Research and Innovation(grant no.B40G660036).
文摘This study proposes to use the unconfined compressive strength(UCS)and the bender element(BE)tests for determining the strength and the initial small-strain shear modulus of Bangkok soft marine clay improved by cement and polyester fibers.This study varies the content of admixed cement(1%–20%)and polyester fibers(0–20%),including the curing time(3–28 d)for preparing 360 samples.Moreover,this study uses the Michaelis-Menten kinetics concept to model cement hydration saturation.From the study,it is concluded as follows.The modelled results reveals that at least 10%cement and 1%polyester fiber are recommended to attain the 28-d UCS standards(294 kPa)for highway subgrade materials in Thailand.This also fulfils sustainable construction due to reducing normal-use cement from 20%to 10%.Unfortunately,the addition of polyester fibers into the Bangkok clay with at least 5%cement reduces shear modulus by 1.12–1.32 times.The Abram's relationship between shear modulus and the mixing-water-to-cement ratio is found time-dependent.From the composite theory,the BE detects the polyester fiber zone as a defect in the Bangkok clay(matrix)with 5%–20%cement.So,the 28-d shear modulus in the polyester fiber zone is negative(up to0.034 MPa for 20%fiber),similar to softening phenomenon in concrete cracking(negative stiffness).For the 28-d shear modulus of fiber zone,the optimum cement content is around 2%for the positive influences of polyester fibers.Experimentally,the timedependent normalized UCS for 10%and 20%cement is compatible with other studies,and its development rate increases with the cement content as 0.3017,0.3172 and 0.3204 for 5%,10%and 20%cement,respectively.The 28-d relationship between shear modulus and UCS shows that low-cement soft clay requires high polyester fiber content(5%–20%)to activate UCS improvement.However,the soft clay with enough cement(20%)causes the uniformly distributed UCS improvement.
基金financial support provided by the National Natural Science Foundation of China(Grant No.12302472)the Science and Technology Support Program of Jiangsu Province(Grant No.BK20230874)+2 种基金the Aeronautical Science Fund(ASF)(Grant No.2023Z057052005)the Research Fund of State Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCAS-I-0124G02)the funding received from Jiangsu Hanvo Safety Product Co.,Ltd。
文摘High-performance fiber fabrics and composites experienced transverse compression deformation at ultrahigh strain rates near the impact point when subjected to high-velocity impacts,which significantly affected their ballistic limits.In this paper,a fiber-scale experimental method for characterizing ultrahigh strain-rate transverse compression behavior was proposed.To begin with,in order to measure the extremely low stress and strain in small specimens,the conventional Hopkinson bar was reduced to the hundred-micron scale,thereby achieving wave impedance matching with single fibers.In addition,tangential and normal laser Doppler velocimetry(LDV)methods were employed to realize non-contact,high-precision,and high-speed axial velocity measurements of micron-scale incident and transmission bars,respectively.Meanwhile,a microscopic observation system was used to facilitate the installation of miniature fiber samples.The experimental setup and procedures were introduced,and the system accuracy was verified through sample-free loading tests based on one-dimensional stress wave propagation theory.Dynamic compression experiments on Graphene-UHMWPE fibers were carried out,followed by post-compression microstructural characterization via scanning electron microscopy(SEM).Results demonstrated that successful mechanical characterization was achieved at strain rates exceeding 105,an order of magnitude higher than the previously reported maximum rates.Furthermore,during the loading process,the fibers underwent uniform compression deformation while exhibiting pronounced strain-rate effects.This method offers a novel approach for dynamic mechanical characterization of microscale single fibers,enabling the development of comprehensive strain-ratedependent material models to guide the design of advanced composites and high-performance fibers.
基金supported by the National Natural Science Foundation of China(Grant Nos.52379104 and 52090084).
文摘The biodegradable polybutylene succinate(PBS)material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste.Its cross-linkage with non-biodegradable xanthan gum(XG)biopolymer to ameliorate residual granitic soil(RGS)in arid and semiarid regions can significantly mitigate soil erosion.This study investigates the enhancement of RGS by cross-linking the PBS and XG biopolymers.Employing a multitude of geotechnical tests(liquid limit,linear shrinkage,specific gravity,compaction,and UCS tests)at 3 d,28 d,and 90 d of steam-curing at a controlled temperature of 16℃,the outcomes were validated through scanning electron microscopy(SEM),thermogravimetric analysis(TGA),Fourier transform infrared spectroscopy(FTIR),and Brunauer-Emmett-Teller(BET)analyses.In addition,a comprehensive experimental database of 150 tests and nine parameters from the current study was utilized to model the UCS90-d(i.e.unconfined compressive strength after 90 d of curing)of the PBS-XG-treated RGS mixtures by deploying the random forest(RF)and eXtreme Gradient Boost(XGBoost)methods.The results found that the two biopolymers significantly improve the mechanical properties of RGS,with optimal UCS achieved at specific dosages(0.4PBS,1.5XG,and 0.2PBS+1.5XG dosage levels)and curing times.The UCS of PBS-XG-treated RGS showed up to a 57%increase after 90 d of curing.Furthermore,SEM and FTIR analyses revealed the formation of stronger microstructures and chemical bonds,respectively,whereas BET analysis indicated that pore volume and diameter are critical in affecting UCS.The proposed RF model outperformed XGBoost in predictive accuracy and generalization,demonstrating robustness and versatility.Moreover,SHAP values highlighted the significant impact of input parameters on UCS90-d,with curing time and specific material properties being key determinants.The study concludes with the proposal of a novel PyCharm intuitive graphical user interface as a"UCS Prediction App"for engineers and practitioners to forecast the UCS90-d of granitic residual soil.
文摘Ultrahigh-performance concrete(UHPC)is a groundbreaking kind of concrete that distinguishes itself from conventional concrete through its unique material properties.Understanding and managing the time-dependent characteristics of these materials is essential for their effective use in various construction applications.This study presents an experimental evaluation of the compressive and bending properties of the UHPC incorporating polypropylene,steel,and glass fibers.Based on ACI-211 guidelines,the UHPC mix was designed by using three types of aggregates:limestone,andesite,and quartzite,along with 5%fiber content(at varying percentages of 0,5%,10%,15%,and 20%)relative to the cementitious materials,and three different water-to-cement(w/c)ratios(0.24,0.3,and 0.4)were used.In this research,the compressive and flexural strength tests were conducted.The results show that increasing the values of the fibers significantly enhances the compressive strength of the studied samples.Furthermore,the utilization of fibers markedly improves the bending strength of the samples,demonstrating a strong correlation with the yield resistance of the material.Also,findings show that using steel fibers increases the compressive and bending strength of the tested samples more than polypropylene and glass fibers.For instance,in UHPC samples with 0.4 w/c,the average compressive strength values are 82.2 MPa,70.3 MPa,and 67.1 MPa for steel,polypropylene,and glass fibers,respectively.Also,in the flexural strength test,the modulus of rupture is obtained as an average of 6.24 MPa,5.24 MPa and 4.83 MPa for UHPC samples with steel,polypropylene and glass fibers,respectively.
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support from the National Natural Science Foundation of China (No.52231004,52175365,51972271)Dr.Jiawei Fu appreciates the support from The Young Talents Plan in Shaanxi Province of China (No.00121)。
文摘To meet the increased demand for light-weight and high-performance special-shaped load bearing parts in automotive industry,the short carbon fiber reinforced magnesium matrix composite(C_(sf)/Mg)part with complex configuration features and abrupt cross-sectional transitions was fabricated by liquid-solid extrusion following vacuum pressure infiltration process(LSEVI).Near-net forming schemes of both the special-shaped fiber preform and composite part were proposed.The effect of process parameters on the forming quality of the composite part was discussed.Meanwhile,the microstructures and compressive properties in different regions of the part were analyzed.The results show that the forward forming scheme provides the special-shaped fiber preform with no surface defects.For the C_(sf)/AZ91D part,its internal microstructures show that the infiltration of liquid magnesium is sufficient and uniform.The compressive strength of the composite part can reach up to 487 MPa,corresponding to~40%increase compared to 335 MPa of the AZ91D alloy.The average compressive strain of composites is less than 10%,which is about 50%of that of the AZ91D alloy.When the fiber orientation is parallel to the shear direction on the shear plane,the load-bearing capacity of the fiber is much higher than that of the fiber perpendicular to the shear direction.This work not only provides a convenient approach to fabricate special-shaped preform with high fiber volume fraction,but also gives a demonstration for the near-net forming of C_(sf)/Mg parts with excellent material isotropy and compressive properties.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
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supported by the National Natural Science Foundation of China(Grant Nos.12325408,12274129,12374274,12274139,62175066,92150102,62475070,12474404,12471368,and 12304338)the Shanghai Municipal Education Commission(Grant No.2024AI01007).
文摘Incoherent digital holography has attracted significant attention due to its advantages in threedimensional(3D)imaging under low spatial coherence conditions,such as easy access to light sources and reduced speckle noise.However,interlayer crosstalk during the reconstruction process leads to a substantial reduction in reconstruction fidelity.Furthermore,existing deconvolutionand deep-learning-based reconstruction algorithms face limitations in terms of effectiveness and generalization.To address these challenges,we propose a compressive incoherent digital holography(CIDH)approach for 3D imaging.In CIDH,a point spread hologram sequence with a high signal-to-noise ratio is initially obtained using a customized computergenerated holography method for dual-channel forward data acquisition.For scene reconstruction,a compressed sensing-based two-step iterative shrinkage/thresholding algorithm is employed to achieve high-fidelity 3D scene retrieval.The combined optimization demonstrates exceptional performance in suppressing interlayer crosstalk and enhancing reconstruction fidelity.In simulations,crosstalk was effectively suppressed across 10 depth layers.In experiments,successful suppression was achieved for both a five-layer transmissive object and a two-layer reflective 3D object,resulting in significantly improved reconstruction accuracy.The proposed framework shows great potential for applications in various incoherent source-illuminated and fluorescent 3D imaging.
文摘Exploring alternative aggregates or recycled aggregates to substitute traditional concrete aggregates,particularly sand aggregates,which are becoming more limited and must comply with environmental protection standards,is essential.Research has explored various alternative materials to sand in concrete,including concrete from demolished buildings,and broken glass from projects,among others.Investigating the use of recycled broken glass to substitute sand aggregates and implementing this research in compression columns is crucial.This paper examines the compressive behavior of reinforced concrete columns that utilize recycled glass particles as a substitute for sand in concrete.The research findings establish the relationships:load and vertical displacement,load and deformation at the column head,mid-column,and column base;the formation and propagation of cracks in the column,while considering factors such as the percentage of recycled glass,the arrangement of stirrups,and the amount of load-bearing steel influencing the performance of square reinforced concrete columns under compression.The feasibility of using recycled glass as a substitute for sand in column structures subjected to compression has been demonstrated,with the ideal replacement content for sand aggregate in reinforced concrete columns in this study ranging from 0%to 10%.The column’s load-bearing ability dropped from 250 kN to 150 kN when 100%recycled glass was used instead of sand.This is a 40%drop,and cracks started to show up sooner.The research will support recycling broken glass instead of using sand in building,improving the environment and reducing natural sand use.
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supported by the Ongoing Research Funding Program(Grant No.ORFT-2025-025-6)at King Saud University,Riyadh,Saudi Arabia.
文摘Accurately predicting the compressive strength of recycled aggregate concrete(RAC)incorporating supplementary cementitious materials(SCMs)remains a critical challenge due to the heterogeneous nature of recycled aggregates(RA)and the complex interactions among multiple binder constituents.This study advances the field by developing the most extensive and rigorously preprocessed database to date,which comprises 1243 RAC mixtures containing silica fume,fly ash,and ground-granulated blast-furnace slag.A hybrid,domain-informed machine-learning framework was then proposed,coupling optimized Extreme Gradient Boosting(XGBost)with civil engineering expertise to capture the complex chemical and microstructural mechanisms that govern RAC performance.Systematic grid-search optimization(n_estimators=50,learning_rate=0.2,max_depth=7)produced superior predictive accuracy(training R^(2)=0.9923,testing R^(2)=0.937;MAE=2.378 MPa;RMSE=3.591 MPa),which outperformed Extra Trees,Light Gradient Boosting,and traditional regressors.Beyond prediction,model interpretability was achieved using Shapley additive explanations and partial dependence analyses,which revealed curing age as the dominant strength driver,while water-to-binder ratio and recycled aggregate water absorption exhibited strong negative influences.Three-dimensional interaction plots further demonstrated how optimal superplasticizer dosages reduce the strength loss associated with high recycled aggregate content.In summary,this work provides a novel,explainable,and data-driven framework that achieves high predictive accuracy with mechanistic transparency and offers a powerful,interpretable tool for the design and optimization of sustainable RAC mixtures.
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.
