Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventio...Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.展开更多
This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf o...This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.展开更多
Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the prope...Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.展开更多
River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study d...River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study deals with the properties of cement mortar containing different levels of manufactured sand(MS)based on quartzite,used to replace river sand.The river sand was replaced at 20%,40%,60%and 80%with MS(by weight or volume).The mechanical properties,transfer properties,and microstructure were examined and compared to a control group to study the impact of the replacement level.The results indicate that the compressive strength can be improved by increasing such a level.The strength was improved by 35.1%and 45.5%over that of the control mortar at replacement levels of 60%and 80%,respectively.Although there was a weak link between porosity and gas permeability in the mortars with manufactured sand,the gas permeability decreased with growing the replacement level.The microstructure of the MS mortar was denser,and the cement paste had fewer microcracks with increasing the replacement level.展开更多
Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining envir...Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.展开更多
Experimental investigations on dynamic in-plane compressive behavior of a plain weave composite were performed using the split Hopkinson pressure bar. A quantitative criterion for calculating the constant strain rate ...Experimental investigations on dynamic in-plane compressive behavior of a plain weave composite were performed using the split Hopkinson pressure bar. A quantitative criterion for calculating the constant strain rate of composites was established. Then the upper limit of strain rate, restricted by stress equilibrium and constant loading rate, was rationally estimated and confirmed by tests. Within the achievable range of 0.001/s-895/s, it was found that the strength increased first and subsequently decreased as the strain rate increased. This feature was also reflected by the turning point(579/s) of the bilinear model for strength prediction. The transition in failure mechanism, from local opening damage to completely splitting destruction, was mainly responsible for such strain rate effects. And three major failure modes were summarized under microscopic observations: fiber fracture, inter-fiber fracture, and interface delamination. Finally, by introducing a nonlinear damage variable, a simplified ZWT model was developed to characterize the dynamic mechanical response. Excellent agreement was shown between the experimental and simulated results.展开更多
Figuring out rock strength plays essential roles in the sub ground mining activities,such as oil and gas well drilling and hydraulic fracturing,coal mining,tunneling,and other civil engineering scenarios.To help under...Figuring out rock strength plays essential roles in the sub ground mining activities,such as oil and gas well drilling and hydraulic fracturing,coal mining,tunneling,and other civil engineering scenarios.To help understand the effects of the mineralogical composition on evaluating the rock strength,this research tries to establish indirect prediction models of rock strength by specific input mineral contents for common sedimentary rocks.Using rock samples collected from the outcrops in the Sichuan Basin,uniaxial compression tests have been conducted to sandstone,carbonate,and shale cores.Combining with statistical analysis,the experimental data prove it true that the mineralogical composition can be utilized to predict the rock strength under specific conditions but the effects of mineralogical composition on the rock strength highly depend on the rock lithologies.According to the statistical analysis results,the predicted values of rock strengths by the mineral contents can get high accuracies in sandstone and carbonate rocks while no evidences can be found in shale rocks.The best indicator for predicting rock strength should be the quartz content for the sandstone rocks and the dolomite content for the carbonate rocks.Especially,to improve the evaluation accuracy,the rock strengths of sandstones can be obtained by substituting the mineral contents of quartz and clays,and those of carbonates can be calculated by the mineral contents of dolomite and calcite.Noticeably,the research data point out a significant contrast of quartz content in evaluating the rock strength of the sandstone rocks and the carbonate rocks.Increasing quartz content helps increase the sandstone strength but decrease the carbonate strength.As for shale rocks,no relationship exists between the rock strength and the mineralogical composition(e.g.,the clay fractions).To provide more evidences,detailed discussion also provides the readers more glances into the framework of the rock matrix,which can be further studied in the future.These findings can help understand the effects of mineralogical composition on the rock strengths,explain the contrasts in the rock strength of the responses to the same mineral content(e.g.,the quartz content),and provide another indirect method for evaluating the rock strength of common sedimentary rocks.展开更多
Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice v...Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.展开更多
The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering.Five machine learning(ML)techniques were adopted in thi...The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering.Five machine learning(ML)techniques were adopted in this study,i.e.back propagation neural network(BPNN),AdaBoost-based classification and regression tree(AdaBoost-CART),support vector machine(SVM),K-nearest neighbor(KNN),and radial basis function neural network(RBFNN).A total of 351 data points with seven input parameters(i.e.diameter and height of specimen,density,temperature,confining pressure,crack damage stress and elastic modulus)and one output parameter(triaxial compressive strength)were utilized.The root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R)were used to evaluate the prediction performance of the five ML models.The results demonstrated that the BPNN shows a better prediction performance than the other models with RMSE,MAE and R values on the testing dataset of 15.4 MPa,11.03 MPa and 0.9921,respectively.The results indicated that the ML techniques are effective for accurately predicting the triaxial compressive strength of rocks after different high-temperature treatments.展开更多
Glass powder of various particle sizes(2,5,10 and 15μm)has been assessed as a possible cement substitute for mortars.Different replacement rates of cement(5%,10%,15%,and 20%)have been considered for all particle size...Glass powder of various particle sizes(2,5,10 and 15μm)has been assessed as a possible cement substitute for mortars.Different replacement rates of cement(5%,10%,15%,and 20%)have been considered for all particle sizes.The accessible porosity,compressive strength,gas permeability and microstructure have been investigated accordingly.The results have shown that adding glass powder up to 20%has a significantly negative effect on the porosity and compressive strength of mortar.The compressive strength initially rises with a 5%replacement and then decreases.Similarly,the gas permeability of the mortar displays a non-monotonic behavior;first,it decreases and then it grows with an increase in the glass powder content and particle size.The porosity and gas permeability attain a minimum for a 5%content and 10μm particle size.Application of a Nuclear magnetic resonance(NMR)technique has revealed that incorporating waste glass powder with a certainfineness can reduce the pore size and the number of pores of the mortar.Compared with the control mortar,the pore volume of the waste glass mortar with 5%and 10μm particle size is significantly reduced.When cement is partially replaced by glass powder with a particle size of 10μm and a 5%percentage,the penetration resistance and compressive strength of the mortar are significantly improved.展开更多
The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the rel...The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB.展开更多
The treatment of wheat straw is very difficult,and its utilization rate is very low;accumulation causes air pollution and even fire.To make full use of wheat straw resources,we examined how using different physical an...The treatment of wheat straw is very difficult,and its utilization rate is very low;accumulation causes air pollution and even fire.To make full use of wheat straw resources,we examined how using different physical and chemical methods to treat the wheat straw which can improve its strength abilities,or enhance the activity of wheat straw ash.In terms of concrete additives,it can reduce the amount of cement used.In this paper,we found that alkali treatment can significantly improve the tensile strength of wheat straw fiber,but polyvinyl alcohol treatment has no obvious effect on the strength of wheat straw fiber after alkali treatment.At the same time,we analyzed the wheat straw fiber microstructure through scanning electron microscopy,and we also studied the wheat straw ash chemical composition after 600℃ high-temperature treatment.Through the compressive strength test,we found that the strength of concrete decreases with increasing of wheat straw fiber and wheat straw powder content,and the compressive strength of concrete with wheat straw ash instead of 5%cement decreases little,and the strength of the concrete also decreases with the increasing of wheat straw ash.Through the macroscopic observation of the failure form of concrete,we found that the failure form of concrete with wheat straw ash is similar to that of common concrete,while the failure degree of concrete with wheat straw fiber and wheat straw powder is weakened.Through the scanning electron microscope test of the concrete,it was found that wheat straw fiber has an effect on the cracking of concrete and the inner compactness of concrete can also be affected by adding wheat straw ash and wheat straw powder.展开更多
The general goal of this research is to investigate whether steel fiber has a significant “positive” or “negative” influence on concrete compressive strength, as well as the optimal steel fiber ratio that delivers...The general goal of this research is to investigate whether steel fiber has a significant “positive” or “negative” influence on concrete compressive strength, as well as the optimal steel fiber ratio that delivers best result. Manually, cement, fine aggregates, coarse aggregates, steel fibers, and water were mixed together properly. A slump test was carried on the mixed concrete. After determining the workability, the mixed concrete was poured into cubes dimension 150 mm × 150 mm × 150 mm and left for 24 hours. After 24 hours, the samples were removed from the mold and placed in a water tank to cure for 7 to 28 days. The cube was tested for compressive and flexural strength in a universal testing machine after the samples had cured for the required 7 - 28 days. This study focuses on how to obtain high strength concrete using with steel fiber in the Conventional mix ratio to enhance concrete strength. Concrete reinforcement using steel fibers alters the characteristics of the concrete, allowing it to withstand fracture and hence improve its mechanical qualities. This study reports on an experimental study that reveals the effect of steel fiber on concrete compressive strength and the optimal steel fiber ratio that produces the best results. Steel fiber reinforcing improved the compressive strength of concrete. The average compressive strength of normal M25 concrete with 0% steel fibers and curing ages of 7 and 28 days was determined to be 22.97 N/mm<sup>2</sup> and 25.78 N/mm<sup>2</sup>, respectively. The steel fibers are then added in various concentrations, such as 1%, 2%, and 3%, with aspect ratios of 70. The compressive strength of concrete with 1%, 2%, and 3% steel fiber with an aspect ratio of 70 was examined at 7 days and found to be 23.96, 24.80, and 26.14 N/mm<sup>2</sup> correspondingly.展开更多
Tubular members subject to combined pitting corrosion and crack damage were numerically studied to clarify the reduction of ultimate strength and failure behavior,based on numerical models validated against available ...Tubular members subject to combined pitting corrosion and crack damage were numerically studied to clarify the reduction of ultimate strength and failure behavior,based on numerical models validated against available experi-ments.The effects of length,location and inclined angle of a crack under combined damage were studied to disclose the mechanism of interaction between the crack and corrosion pits.The methods,named as linear superposition directly accumulating the effects of solo crack and solo pitting damage,as well as crack projection transferring an inclined crack to a transverse one,were discussed and verified in the view of assessing ultimate strength of tubular members with combined damage.It was shown that the former is practical but complex while the next always over-estimates the residual strength.Besides,the location and inclined angle of a crack have a subtle effect on the reduction of ultimate strength under combined damage,especially at higher level of pitting damage,due to the synergistic effect between corrosion pits and cracks.Such effect can lead to early occurrence of plasticity and local buckling by inducing stress interaction between crack tips and pits,and causing more significant strength reduction compared with a solo type of damage.A practical method was proposed to determine the loss ratio of cross-sectional area on the equivalent weakest section of a damaged member.Based on the loss ratio,a formula was presented to predict the ultimate strength of damaged members with combined damage,showing good applicability.展开更多
Basic magnesium sulfate cement coral aggregate concrete(MCAC)is a new type of concrete consisting of basic magnesium sulfate cement,coarse coral aggregate,coral reef sand and seawater.The rebound hammer(RH),the ultras...Basic magnesium sulfate cement coral aggregate concrete(MCAC)is a new type of concrete consisting of basic magnesium sulfate cement,coarse coral aggregate,coral reef sand and seawater.The rebound hammer(RH),the ultrasonic pulse velocity(UPV)and the compressive strength(fcu)tests of 14 sets of cube specimens of the MCAC after 28 d of aging were conducted.The impact of the content and length of sisal fiber on the relationship between the fcu-RH and the fcu-UPV was determined.A mathematical model was established to predict the strength of the MCAC using the UPV,RH,and comprehensive UPV/RH methods and to obtain the curves of test strength.The applicability of the test strength curves of ordinary portland concrete(OPC),light-weight aggregate concrete(LAC),and coral aggregate concrete(CAC)to MCAC was assessed.The results showed that the test strength curves of OPC,LAC and CAC were inappropriate to determine the strength of MCAC using non-destructive method.The relative standard error of the curves of test strength of the RH method and the comprehensive method met the specifications,whereas that of the UPV method did not.展开更多
In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To pre...In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To predict the UCS,simple regression(SRA),multiple regression(MRA),artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and genetic expression programming(GEP)have been utilized.The obtained UCS values were compared with the actual UCS values with the help of various graphs.Datasets were modeled using different methods and compared with each other.In the study where the performance indice PIat was used to determine the best performing method,MRA method is the most successful method with a small difference.It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA,while these values are 2.44,2.33,and 2.22 for GEP,ANFIS,and ANN techniques,respectively.The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others.According to the performance index assessment,the weakest model among the nine model is P7,while the most successful models are P2,P9,and P8,respectively.展开更多
It is generally accepted that the uniaxial compressive strength(UCS)and P-wave velocity of rocks tend to decrease simultaneously with increasing temperature.However,based on a great number of statistical data and syst...It is generally accepted that the uniaxial compressive strength(UCS)and P-wave velocity of rocks tend to decrease simultaneously with increasing temperature.However,based on a great number of statistical data and systematic analysis of the microstructure variation of rocks with temperature rising and corresponding propagation mechanism of elastic wave,the results show that(1)There are three different trends for the changes of UCS and P-wave velocity of sandstone when heated from room temperature(20C or 25C)to 800C:(i)Both the UCS and P-wave velocity decrease simultaneously;(ii)The UCS increases initially and then decreases,while the P-wave velocity decreases continuously;and(iii)The UCS increases initially and then fluctuates,while the P-wave velocity continuously decreases.(2)The UCS changes at room temperaturee400C,400Ce600C,and 600Ce800C are mainly attributed to the discrepancy of microstructure characteristics and quartz content,the transformation plasticity of clay minerals,and the balance between the thermal cementation and thermal damage,respectively.(3)The inconsistency in the trends of UCS and P-wave velocity changes is caused by the change of quartz content,phase transition of water and certain minerals.展开更多
High temperature compressive strength is one of the most important performances of resin sand; its value directly concerns the quality of castings. In order to seek the best testing method of resin sand high temperatu...High temperature compressive strength is one of the most important performances of resin sand; its value directly concerns the quality of castings. In order to seek the best testing method of resin sand high temperature compressive strength, a self-developed instrument was used to carry out experiments, and the sample shape and size were designed and studied. The results show that a hollow cylinder sample can reflect the strength difference of different resin sands better than a solid cylinder sample, and its data is stable. The experiments selected Φ20/5×30 mm as the size of the hollow cylinder samples. The high temperature compressive strengths of phenol-formaldehyde resin coated sand, furan resin self-setting sand, and TEA resin sand were each tested. For the resin sand used for cast steel and cast iron, 1,000 oC was selected as the test temperature; for the resin sand used for cast non-ferrous al oy, 800 oC was selected as the test temperature; and for all the resin sand samples, 1 min was selected as the holding time. This testing method can truthfully reflect the high temperature performance of three kinds of resin sand; it is reproducible, and the variation coefficients of test values are under 10%.展开更多
Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However...Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.展开更多
The stability of cemented paste backfill(CPB)is threatened by dynamic disturbance,but the conventional low strain rate laboratory pressure test has difficulty achieving this research purpose.Therefore,a split Hopkinso...The stability of cemented paste backfill(CPB)is threatened by dynamic disturbance,but the conventional low strain rate laboratory pressure test has difficulty achieving this research purpose.Therefore,a split Hopkinson pressure bar(SHPB)was utilized to investigate the high strain rate compressive behavior of CPB with dynamic loads of 0.4,0.8,and 1.2 MPa.And the failure modes were determined by macro and micro analysis.CPB with different cement-to-tailings ratios,solid mass concentrations,and curing ages was prepared to conduct the SHPB test.The results showed that increasing the cement content,tailings content,and curing age can improve the dynamic compressive strength and elastic modulus.Under an impact load,a higher strain rate can lead to larger increasing times of the dynamic compressive strength when compared with static loading.And the dynamic compressive strength of CPB has an exponential correlation with the strain rate.The macroscopic failure modes indicated that CPB is more seriously damaged under dynamic loading.The local damage was enhanced,and fine cracks were formed in the interior of the CPB.This is because the CPB cannot dissipate the energy of the high strain rate stress wave in a short loading period.展开更多
基金funded by the Researchers Supporting Program at King Saud University(RSPD2023R809).
文摘Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.
基金supported via funding from Prince Sattam Bin Abdulaziz University Project Number(PSAU/2023/R/1445).
文摘This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.
基金This work has been supported by the Conselleria de Inno-vación,Universidades,Ciencia y Sociedad Digital de la Generalitat Valenciana(CIAICO/2021/335).
文摘Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.
基金supported by the National Natural Science Foundation of China(No.51709097).
文摘River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study deals with the properties of cement mortar containing different levels of manufactured sand(MS)based on quartzite,used to replace river sand.The river sand was replaced at 20%,40%,60%and 80%with MS(by weight or volume).The mechanical properties,transfer properties,and microstructure were examined and compared to a control group to study the impact of the replacement level.The results indicate that the compressive strength can be improved by increasing such a level.The strength was improved by 35.1%and 45.5%over that of the control mortar at replacement levels of 60%and 80%,respectively.Although there was a weak link between porosity and gas permeability in the mortars with manufactured sand,the gas permeability decreased with growing the replacement level.The microstructure of the MS mortar was denser,and the cement paste had fewer microcracks with increasing the replacement level.
基金supported by the National Natural Science Foundation of China(Grant No.52374153).
文摘Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.
基金the National Science and Technology Major Project(Grant No.2017-VII-0011-0106)Natural Science Foundation of Heilongjiang Province(Grant No.ZD2019A001).
文摘Experimental investigations on dynamic in-plane compressive behavior of a plain weave composite were performed using the split Hopkinson pressure bar. A quantitative criterion for calculating the constant strain rate of composites was established. Then the upper limit of strain rate, restricted by stress equilibrium and constant loading rate, was rationally estimated and confirmed by tests. Within the achievable range of 0.001/s-895/s, it was found that the strength increased first and subsequently decreased as the strain rate increased. This feature was also reflected by the turning point(579/s) of the bilinear model for strength prediction. The transition in failure mechanism, from local opening damage to completely splitting destruction, was mainly responsible for such strain rate effects. And three major failure modes were summarized under microscopic observations: fiber fracture, inter-fiber fracture, and interface delamination. Finally, by introducing a nonlinear damage variable, a simplified ZWT model was developed to characterize the dynamic mechanical response. Excellent agreement was shown between the experimental and simulated results.
基金supported by National Key Research and Development Program of China(2019YFA0708302)National Natural Science Foundation of China(Grant No.52004296,and Grant No.52274016)+1 种基金the Foundation of State Key Laboratory of Petroleum Resources and Prospecting(PRP/DX-2206)Science Foundation of China University of Petroleum-Beijing(No.2462022YXZZ007,No.2462022BJRC012).
文摘Figuring out rock strength plays essential roles in the sub ground mining activities,such as oil and gas well drilling and hydraulic fracturing,coal mining,tunneling,and other civil engineering scenarios.To help understand the effects of the mineralogical composition on evaluating the rock strength,this research tries to establish indirect prediction models of rock strength by specific input mineral contents for common sedimentary rocks.Using rock samples collected from the outcrops in the Sichuan Basin,uniaxial compression tests have been conducted to sandstone,carbonate,and shale cores.Combining with statistical analysis,the experimental data prove it true that the mineralogical composition can be utilized to predict the rock strength under specific conditions but the effects of mineralogical composition on the rock strength highly depend on the rock lithologies.According to the statistical analysis results,the predicted values of rock strengths by the mineral contents can get high accuracies in sandstone and carbonate rocks while no evidences can be found in shale rocks.The best indicator for predicting rock strength should be the quartz content for the sandstone rocks and the dolomite content for the carbonate rocks.Especially,to improve the evaluation accuracy,the rock strengths of sandstones can be obtained by substituting the mineral contents of quartz and clays,and those of carbonates can be calculated by the mineral contents of dolomite and calcite.Noticeably,the research data point out a significant contrast of quartz content in evaluating the rock strength of the sandstone rocks and the carbonate rocks.Increasing quartz content helps increase the sandstone strength but decrease the carbonate strength.As for shale rocks,no relationship exists between the rock strength and the mineralogical composition(e.g.,the clay fractions).To provide more evidences,detailed discussion also provides the readers more glances into the framework of the rock matrix,which can be further studied in the future.These findings can help understand the effects of mineralogical composition on the rock strengths,explain the contrasts in the rock strength of the responses to the same mineral content(e.g.,the quartz content),and provide another indirect method for evaluating the rock strength of common sedimentary rocks.
基金Supported by the National Natural Science Foundation of China(Nos.41630969,41941013,41806225)the Tianjin Municipal Natural Science Foundation(No.20JCQNJC01290)。
文摘Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.
基金We acknowledge the funding support from the National Natural Science Foundation of China(Grant No.51778575)Postdoctoral Science Foundation of China(Grant No.2021M692481)Fundamental Research Funds for the Central Universities of China(Grant No.2042021kf0055).The authors would like to thank the anonymous reviewers and editors for their constructive suggestions which greatly improve the quality of this paper.The authors are also grateful for the permission from Elsevier.
文摘The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering.Five machine learning(ML)techniques were adopted in this study,i.e.back propagation neural network(BPNN),AdaBoost-based classification and regression tree(AdaBoost-CART),support vector machine(SVM),K-nearest neighbor(KNN),and radial basis function neural network(RBFNN).A total of 351 data points with seven input parameters(i.e.diameter and height of specimen,density,temperature,confining pressure,crack damage stress and elastic modulus)and one output parameter(triaxial compressive strength)were utilized.The root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R)were used to evaluate the prediction performance of the five ML models.The results demonstrated that the BPNN shows a better prediction performance than the other models with RMSE,MAE and R values on the testing dataset of 15.4 MPa,11.03 MPa and 0.9921,respectively.The results indicated that the ML techniques are effective for accurately predicting the triaxial compressive strength of rocks after different high-temperature treatments.
基金This work is supported by the National Natural Science Foundation of China(No.51709097).
文摘Glass powder of various particle sizes(2,5,10 and 15μm)has been assessed as a possible cement substitute for mortars.Different replacement rates of cement(5%,10%,15%,and 20%)have been considered for all particle sizes.The accessible porosity,compressive strength,gas permeability and microstructure have been investigated accordingly.The results have shown that adding glass powder up to 20%has a significantly negative effect on the porosity and compressive strength of mortar.The compressive strength initially rises with a 5%replacement and then decreases.Similarly,the gas permeability of the mortar displays a non-monotonic behavior;first,it decreases and then it grows with an increase in the glass powder content and particle size.The porosity and gas permeability attain a minimum for a 5%content and 10μm particle size.Application of a Nuclear magnetic resonance(NMR)technique has revealed that incorporating waste glass powder with a certainfineness can reduce the pore size and the number of pores of the mortar.Compared with the control mortar,the pore volume of the waste glass mortar with 5%and 10μm particle size is significantly reduced.When cement is partially replaced by glass powder with a particle size of 10μm and a 5%percentage,the penetration resistance and compressive strength of the mortar are significantly improved.
基金funded by the Natural Sciences and Engineering Research Council of Canada(NSERC RGPIN-2017-05537).
文摘The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB.
基金Supported by the Opening Project of Tunnel and Underground Engineering Research Center of Jiangsu Province (TERC) (2021-SDJJ-08).
文摘The treatment of wheat straw is very difficult,and its utilization rate is very low;accumulation causes air pollution and even fire.To make full use of wheat straw resources,we examined how using different physical and chemical methods to treat the wheat straw which can improve its strength abilities,or enhance the activity of wheat straw ash.In terms of concrete additives,it can reduce the amount of cement used.In this paper,we found that alkali treatment can significantly improve the tensile strength of wheat straw fiber,but polyvinyl alcohol treatment has no obvious effect on the strength of wheat straw fiber after alkali treatment.At the same time,we analyzed the wheat straw fiber microstructure through scanning electron microscopy,and we also studied the wheat straw ash chemical composition after 600℃ high-temperature treatment.Through the compressive strength test,we found that the strength of concrete decreases with increasing of wheat straw fiber and wheat straw powder content,and the compressive strength of concrete with wheat straw ash instead of 5%cement decreases little,and the strength of the concrete also decreases with the increasing of wheat straw ash.Through the macroscopic observation of the failure form of concrete,we found that the failure form of concrete with wheat straw ash is similar to that of common concrete,while the failure degree of concrete with wheat straw fiber and wheat straw powder is weakened.Through the scanning electron microscope test of the concrete,it was found that wheat straw fiber has an effect on the cracking of concrete and the inner compactness of concrete can also be affected by adding wheat straw ash and wheat straw powder.
文摘The general goal of this research is to investigate whether steel fiber has a significant “positive” or “negative” influence on concrete compressive strength, as well as the optimal steel fiber ratio that delivers best result. Manually, cement, fine aggregates, coarse aggregates, steel fibers, and water were mixed together properly. A slump test was carried on the mixed concrete. After determining the workability, the mixed concrete was poured into cubes dimension 150 mm × 150 mm × 150 mm and left for 24 hours. After 24 hours, the samples were removed from the mold and placed in a water tank to cure for 7 to 28 days. The cube was tested for compressive and flexural strength in a universal testing machine after the samples had cured for the required 7 - 28 days. This study focuses on how to obtain high strength concrete using with steel fiber in the Conventional mix ratio to enhance concrete strength. Concrete reinforcement using steel fibers alters the characteristics of the concrete, allowing it to withstand fracture and hence improve its mechanical qualities. This study reports on an experimental study that reveals the effect of steel fiber on concrete compressive strength and the optimal steel fiber ratio that produces the best results. Steel fiber reinforcing improved the compressive strength of concrete. The average compressive strength of normal M25 concrete with 0% steel fibers and curing ages of 7 and 28 days was determined to be 22.97 N/mm<sup>2</sup> and 25.78 N/mm<sup>2</sup>, respectively. The steel fibers are then added in various concentrations, such as 1%, 2%, and 3%, with aspect ratios of 70. The compressive strength of concrete with 1%, 2%, and 3% steel fiber with an aspect ratio of 70 was examined at 7 days and found to be 23.96, 24.80, and 26.14 N/mm<sup>2</sup> correspondingly.
基金supported by the National Natural Science Foundation of China(Grant No.51879124)。
文摘Tubular members subject to combined pitting corrosion and crack damage were numerically studied to clarify the reduction of ultimate strength and failure behavior,based on numerical models validated against available experi-ments.The effects of length,location and inclined angle of a crack under combined damage were studied to disclose the mechanism of interaction between the crack and corrosion pits.The methods,named as linear superposition directly accumulating the effects of solo crack and solo pitting damage,as well as crack projection transferring an inclined crack to a transverse one,were discussed and verified in the view of assessing ultimate strength of tubular members with combined damage.It was shown that the former is practical but complex while the next always over-estimates the residual strength.Besides,the location and inclined angle of a crack have a subtle effect on the reduction of ultimate strength under combined damage,especially at higher level of pitting damage,due to the synergistic effect between corrosion pits and cracks.Such effect can lead to early occurrence of plasticity and local buckling by inducing stress interaction between crack tips and pits,and causing more significant strength reduction compared with a solo type of damage.A practical method was proposed to determine the loss ratio of cross-sectional area on the equivalent weakest section of a damaged member.Based on the loss ratio,a formula was presented to predict the ultimate strength of damaged members with combined damage,showing good applicability.
基金Funded by National Natural Science Foundation of China(Nos.51878350,11832013,52078250)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0236)。
文摘Basic magnesium sulfate cement coral aggregate concrete(MCAC)is a new type of concrete consisting of basic magnesium sulfate cement,coarse coral aggregate,coral reef sand and seawater.The rebound hammer(RH),the ultrasonic pulse velocity(UPV)and the compressive strength(fcu)tests of 14 sets of cube specimens of the MCAC after 28 d of aging were conducted.The impact of the content and length of sisal fiber on the relationship between the fcu-RH and the fcu-UPV was determined.A mathematical model was established to predict the strength of the MCAC using the UPV,RH,and comprehensive UPV/RH methods and to obtain the curves of test strength.The applicability of the test strength curves of ordinary portland concrete(OPC),light-weight aggregate concrete(LAC),and coral aggregate concrete(CAC)to MCAC was assessed.The results showed that the test strength curves of OPC,LAC and CAC were inappropriate to determine the strength of MCAC using non-destructive method.The relative standard error of the curves of test strength of the RH method and the comprehensive method met the specifications,whereas that of the UPV method did not.
文摘In this study,uniaxial compressive strength(UCS),unit weight(UW),Brazilian tensile strength(BTS),Schmidt hardness(SHH),Shore hardness(SSH),point load index(Is50)and P-wave velocity(Vp)properties were determined.To predict the UCS,simple regression(SRA),multiple regression(MRA),artificial neural network(ANN),adaptive neuro-fuzzy inference system(ANFIS)and genetic expression programming(GEP)have been utilized.The obtained UCS values were compared with the actual UCS values with the help of various graphs.Datasets were modeled using different methods and compared with each other.In the study where the performance indice PIat was used to determine the best performing method,MRA method is the most successful method with a small difference.It is concluded that the mean PIat equal to 2.46 for testing dataset suggests the superiority of the MRA,while these values are 2.44,2.33,and 2.22 for GEP,ANFIS,and ANN techniques,respectively.The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others.According to the performance index assessment,the weakest model among the nine model is P7,while the most successful models are P2,P9,and P8,respectively.
基金This work was supported by the National Natural Science Foundation of China(Grant No.41772333)the program of State Key Laboratory of Frozen Soil Engineering(Grant No.SKLFSE201713)the Shaanxi Province New-Star Talents Promotion Project of Science and Technology(Grant No.2019KJXX-049).
文摘It is generally accepted that the uniaxial compressive strength(UCS)and P-wave velocity of rocks tend to decrease simultaneously with increasing temperature.However,based on a great number of statistical data and systematic analysis of the microstructure variation of rocks with temperature rising and corresponding propagation mechanism of elastic wave,the results show that(1)There are three different trends for the changes of UCS and P-wave velocity of sandstone when heated from room temperature(20C or 25C)to 800C:(i)Both the UCS and P-wave velocity decrease simultaneously;(ii)The UCS increases initially and then decreases,while the P-wave velocity decreases continuously;and(iii)The UCS increases initially and then fluctuates,while the P-wave velocity continuously decreases.(2)The UCS changes at room temperaturee400C,400Ce600C,and 600Ce800C are mainly attributed to the discrepancy of microstructure characteristics and quartz content,the transformation plasticity of clay minerals,and the balance between the thermal cementation and thermal damage,respectively.(3)The inconsistency in the trends of UCS and P-wave velocity changes is caused by the change of quartz content,phase transition of water and certain minerals.
基金financially supported by the National Natural Science Foundation of China(No.51405002)
文摘High temperature compressive strength is one of the most important performances of resin sand; its value directly concerns the quality of castings. In order to seek the best testing method of resin sand high temperature compressive strength, a self-developed instrument was used to carry out experiments, and the sample shape and size were designed and studied. The results show that a hollow cylinder sample can reflect the strength difference of different resin sands better than a solid cylinder sample, and its data is stable. The experiments selected Φ20/5×30 mm as the size of the hollow cylinder samples. The high temperature compressive strengths of phenol-formaldehyde resin coated sand, furan resin self-setting sand, and TEA resin sand were each tested. For the resin sand used for cast steel and cast iron, 1,000 oC was selected as the test temperature; for the resin sand used for cast non-ferrous al oy, 800 oC was selected as the test temperature; and for all the resin sand samples, 1 min was selected as the holding time. This testing method can truthfully reflect the high temperature performance of three kinds of resin sand; it is reproducible, and the variation coefficients of test values are under 10%.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2018R1A2B6007333).
文摘Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.
基金supported by the National Key R&D Program of China(No.2017YFC0602902)the National Natural Scienceof China(Nos.41807259 and 51874350)+2 种基金the Fundamental Research Funds for the Central Universities of Central South University(No.2016zztx096)The support provided by the China Scholarship Council(CSC)during the visit of the first author toécole Polytechnique de Montréal(Student ID:201706370039)the materials supply by Fan Kou lead-zinc mine of Shenzhen Zhongjin Lingnan Non-ferrous metal Company Limited。
文摘The stability of cemented paste backfill(CPB)is threatened by dynamic disturbance,but the conventional low strain rate laboratory pressure test has difficulty achieving this research purpose.Therefore,a split Hopkinson pressure bar(SHPB)was utilized to investigate the high strain rate compressive behavior of CPB with dynamic loads of 0.4,0.8,and 1.2 MPa.And the failure modes were determined by macro and micro analysis.CPB with different cement-to-tailings ratios,solid mass concentrations,and curing ages was prepared to conduct the SHPB test.The results showed that increasing the cement content,tailings content,and curing age can improve the dynamic compressive strength and elastic modulus.Under an impact load,a higher strain rate can lead to larger increasing times of the dynamic compressive strength when compared with static loading.And the dynamic compressive strength of CPB has an exponential correlation with the strain rate.The macroscopic failure modes indicated that CPB is more seriously damaged under dynamic loading.The local damage was enhanced,and fine cracks were formed in the interior of the CPB.This is because the CPB cannot dissipate the energy of the high strain rate stress wave in a short loading period.
