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
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
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
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
The degradation characteristics of high-purity(HP)magnesium(Mg)orthopedic implants under static and cyclic compressive loads(SCL and CCL)remain inadequately understood.This study developed an in vivo loading device ca...The degradation characteristics of high-purity(HP)magnesium(Mg)orthopedic implants under static and cyclic compressive loads(SCL and CCL)remain inadequately understood.This study developed an in vivo loading device capable of applying single SCL and CCL while shielding against unpredictable host movements.In vitro degradation experiments of HP Mg implants were conducted to verify the experimental protocol,and in vivo experiments in rabbit tibiae to observe the degradation characteristics of the implants.Micro-computed tomography and scanning electron microscope were used for three-dimensional reconstruction and surface morphology analysis,respectively.Compared to in vitro specimens,in vivo specimens exhibited significantly higher corrosion rates and more extensive cracking.Cracks in the in vivo specimens gradually penetrated deeper from the loading surface,eventually leading to a rapid structural deterioration;whereas in vitro specimens exhibited more surface-localized cracking and a relatively uniform corrosion pattern.Compared to SCL,CCL accelerated both corrosion and cracking to some extent.These findings provide new insights into the in vivo degradation behavior of Mg-based implants under compressive loading conditions.展开更多
In the present study,we investigated the existence of arbitrary amplitude dust acoustic solitons by considering the Cairns distributed ions,negatively charged streaming dust grains along with(r,q)distributed electrons...In the present study,we investigated the existence of arbitrary amplitude dust acoustic solitons by considering the Cairns distributed ions,negatively charged streaming dust grains along with(r,q)distributed electrons in an un-magnetized dusty plasma.We used the pseudopotential technique to obtain the solitary wave solution.It is seen that the coexistence of rarefactive and compressive solitons is possible when ions and electrons are nonthermally distributed.We found that the soliton characteristics are strongly dependent on the choice of velocity distribution function through the nonthermal spectral indices r q,,a as well as on the ion and dust temperatures.For(r,q)distributed electrons,we found that the soliton amplitude increases(decreases)with smaller(higher)values of negative(positive)r.For Cairns distributed ions,we found a transition from negative to positive polarity solitary structures with the coexistence in between as the nonthermal parameter a increases.Our results gave a better explanation for the formation of dust acoustic solitary structures and their dependence on high and low energy particles in nonthermal distribution profiles in space environments.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
It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modu...It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.展开更多
Deep rock is under a complex geological environment with high geo-stress, high pore pressure, and strong dynamic disturbance. Understanding the dynamic response of rocks under coupled hydraulic-mechanical loading is t...Deep rock is under a complex geological environment with high geo-stress, high pore pressure, and strong dynamic disturbance. Understanding the dynamic response of rocks under coupled hydraulic-mechanical loading is thus essential in evaluating the stability and safety of subterranean engineering structures. Nevertheless, the constraints in experimental techniques have led to limited prior investigations into the dynamic compression behavior of rocks subjected to simultaneous high in-situ stress and pore pressure conditions. This study utilizes a triaxial split Hopkinson pressure bar (SHPB) system in conjunction with a pore pressure loading cell to conduct dynamic experiments on rocks subjected to hydraulic-mechanical loading. A porous green sandstone (GS) was adopted as the testing rock material. The findings reveal that the dynamic behavior of rock specimens is significantly influenced by multiple factors, including the loading rate, confining stress, and pore pressure. Specifically, the dynamic compressive strength of GS exhibits an increase with higher loading rates and greater confining pressures, while it decreases with elevated pore pressure. Moreover, the classical Ashby-Sammis micromechanical model was augmented to account for dynamic loading and pore pressure considerations. By deducing the connection between crack length and damage evolution, the resulting law of crack expansion rate is related to the strain rate. In addition, the influence of hydraulic factors on the stress intensity factor at the crack tip is introduced. Thereby, a dynamic constitutive model for deep rocks under coupled hydraulic-mechanical loading was established and then validated against the experimental results. Subsequently, the characteristics of introduced parameter for quantifying the water-induced effects were carefully discussed.展开更多
Strain effects have garnered significant attention in catalytic applications due to their ability to modulate the electronic structure and surface adsorption properties of catalysts.In this study,we propose a novel ap...Strain effects have garnered significant attention in catalytic applications due to their ability to modulate the electronic structure and surface adsorption properties of catalysts.In this study,we propose a novel approach called“similar stacking”for stress modulation,achieved through the loading of Co_(2)P on Ni_(2)P(Ni_(2)P/Co_(2)P).Theoretical simulations reveal that the compressive strain induced by Co_(2)P influences orbital overlap and electron transfer with hydrogen atoms.Furthermore,the number of stacked layers can be adjusted by varying the precursor soaking time,which further modulates the strain range and hydrogen adsorption.Under a 2-h soaking condition,the strain effect proves favorable for efficient hydrogen production.Experimental characterizations using X-ray diffraction,high-angel annular dark-field scanning transmission election microscope(HAADF-STEM),and X-ray absorption near-edge structure spectroscopy successfully demonstrate lattice contraction of Co_(2)P and bond length shortening of Co-P.Remarkably,our catalyst shows an ultrahigh current density of 1 A cm^(-2) at an overpotential of only 388 mV,surpassing that of commercial Pt/C,while maintaining long-term stability.This material design strategy of similar stacking opens up new avenues of strain modulation and the deeper development of electrocatalysts.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
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
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%.
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.
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.
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.
基金supported by National Natural Science Foundation of China[51975317].
文摘The degradation characteristics of high-purity(HP)magnesium(Mg)orthopedic implants under static and cyclic compressive loads(SCL and CCL)remain inadequately understood.This study developed an in vivo loading device capable of applying single SCL and CCL while shielding against unpredictable host movements.In vitro degradation experiments of HP Mg implants were conducted to verify the experimental protocol,and in vivo experiments in rabbit tibiae to observe the degradation characteristics of the implants.Micro-computed tomography and scanning electron microscope were used for three-dimensional reconstruction and surface morphology analysis,respectively.Compared to in vitro specimens,in vivo specimens exhibited significantly higher corrosion rates and more extensive cracking.Cracks in the in vivo specimens gradually penetrated deeper from the loading surface,eventually leading to a rapid structural deterioration;whereas in vitro specimens exhibited more surface-localized cracking and a relatively uniform corrosion pattern.Compared to SCL,CCL accelerated both corrosion and cracking to some extent.These findings provide new insights into the in vivo degradation behavior of Mg-based implants under compressive loading conditions.
文摘In the present study,we investigated the existence of arbitrary amplitude dust acoustic solitons by considering the Cairns distributed ions,negatively charged streaming dust grains along with(r,q)distributed electrons in an un-magnetized dusty plasma.We used the pseudopotential technique to obtain the solitary wave solution.It is seen that the coexistence of rarefactive and compressive solitons is possible when ions and electrons are nonthermally distributed.We found that the soliton characteristics are strongly dependent on the choice of velocity distribution function through the nonthermal spectral indices r q,,a as well as on the ion and dust temperatures.For(r,q)distributed electrons,we found that the soliton amplitude increases(decreases)with smaller(higher)values of negative(positive)r.For Cairns distributed ions,we found a transition from negative to positive polarity solitary structures with the coexistence in between as the nonthermal parameter a increases.Our results gave a better explanation for the formation of dust acoustic solitary structures and their dependence on high and low energy particles in nonthermal distribution profiles in space environments.
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supported by the National Natural Science Foundation of China(61931012,62171258,62088102,and 62271414)the Zhejiang Provincial Outstanding Youth Science Foundation(LR23F010001)the Key Project of Westlake Institute for Optoelectronics(2023GD007).
文摘It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.
基金the funding support from the National Natural Science Foundation of China(Grant Nos.52079091,42141010,and 42377147).
文摘Deep rock is under a complex geological environment with high geo-stress, high pore pressure, and strong dynamic disturbance. Understanding the dynamic response of rocks under coupled hydraulic-mechanical loading is thus essential in evaluating the stability and safety of subterranean engineering structures. Nevertheless, the constraints in experimental techniques have led to limited prior investigations into the dynamic compression behavior of rocks subjected to simultaneous high in-situ stress and pore pressure conditions. This study utilizes a triaxial split Hopkinson pressure bar (SHPB) system in conjunction with a pore pressure loading cell to conduct dynamic experiments on rocks subjected to hydraulic-mechanical loading. A porous green sandstone (GS) was adopted as the testing rock material. The findings reveal that the dynamic behavior of rock specimens is significantly influenced by multiple factors, including the loading rate, confining stress, and pore pressure. Specifically, the dynamic compressive strength of GS exhibits an increase with higher loading rates and greater confining pressures, while it decreases with elevated pore pressure. Moreover, the classical Ashby-Sammis micromechanical model was augmented to account for dynamic loading and pore pressure considerations. By deducing the connection between crack length and damage evolution, the resulting law of crack expansion rate is related to the strain rate. In addition, the influence of hydraulic factors on the stress intensity factor at the crack tip is introduced. Thereby, a dynamic constitutive model for deep rocks under coupled hydraulic-mechanical loading was established and then validated against the experimental results. Subsequently, the characteristics of introduced parameter for quantifying the water-induced effects were carefully discussed.
基金Self-innovation Capability Construction of Jilin Province Development and Reform Commission,Grant/Award Number:2021C026National Natural Science Foundation of China,Grant/Award Numbers:12034002,22202080,22279044,51872116Jilin Province Science and Technology Development Program,Grant/Award Number:20210301009GX。
文摘Strain effects have garnered significant attention in catalytic applications due to their ability to modulate the electronic structure and surface adsorption properties of catalysts.In this study,we propose a novel approach called“similar stacking”for stress modulation,achieved through the loading of Co_(2)P on Ni_(2)P(Ni_(2)P/Co_(2)P).Theoretical simulations reveal that the compressive strain induced by Co_(2)P influences orbital overlap and electron transfer with hydrogen atoms.Furthermore,the number of stacked layers can be adjusted by varying the precursor soaking time,which further modulates the strain range and hydrogen adsorption.Under a 2-h soaking condition,the strain effect proves favorable for efficient hydrogen production.Experimental characterizations using X-ray diffraction,high-angel annular dark-field scanning transmission election microscope(HAADF-STEM),and X-ray absorption near-edge structure spectroscopy successfully demonstrate lattice contraction of Co_(2)P and bond length shortening of Co-P.Remarkably,our catalyst shows an ultrahigh current density of 1 A cm^(-2) at an overpotential of only 388 mV,surpassing that of commercial Pt/C,while maintaining long-term stability.This material design strategy of similar stacking opens up new avenues of strain modulation and the deeper development of electrocatalysts.
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.
