Assessment of compressive strength of jet grouting by machine learning

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.

Predicting uniaxial compressive strength of tuff after accelerated freeze-thaw testing: Comparative analysis of regression models and artificial neural networks

Ignimbrites have been widely used as building materials in many historical and touristic structures in the Kayseri region of Türkiye. Their diverse colours and textures make them a popular choice for modern construction as well. However, ignimbrites are particularly vulnerable to atmospheric conditions, such as freeze-thaw cycles, due to their high porosity, which is a result of their formation process. When water enters the pores of the ignimbrites, it can freeze during cold weather. As the water freezes and expands, it generates internal stress within the stone, causing micro-cracks to develop. Over time, repeated freeze-thaw (F-T) cycles lead to the growth of these micro-cracks into larger cracks, compromising the structural integrity of the ignimbrites and eventually making them unsuitable for use as building materials. The determination of the long-term F-T performance of ignimbrites can be established after long F-T experimental processes. Determining the long-term F-T performance of ignimbrites typically requires extensive experimental testing over prolonged freeze-thaw cycles. To streamline this process, developing accurate predictive equations becomes crucial. In this study, such equations were formulated using classical regression analyses and artificial neural networks (ANN) based on data obtained from these experiments, allowing for the prediction of the F-T performance of ignimbrites and other similar building stones without the need for lengthy testing. In this study, uniaxial compressive strength, ultrasonic propagation velocity, apparent porosity and mass loss of ignimbrites after long-term F-T were determined. Following the F-T cycles, the disintegration rate was evaluated using decay function approaches, while uniaxial compressive strength (UCS) values were predicted with minimal input parameters through both regression and ANN analyses. The ANN and regression models created for this purpose were first started with a single input value and then developed with two and three combinations. The predictive performance of the models was assessed by comparing them to regression models using the coefficient of determination (R2) as the evaluation criterion. As a result of the study, higher R2 values (0.87) were obtained in models built with artificial neural network. The results of the study indicate that ANN usage can produce results close to experimental outcomes in predicting the long-term F-T performance of ignimbrite samples.

Bending Failure Mode and Prediction Method of the Compressive Strain Capacity of A Submarine Pipeline with Dent Defects

A dent is a common type of defects for submarine pipeline.For submarine pipelines,high hydrostatic pressure and internal pressure are the main loads.Once pipelines bend due to complex subsea conditions,the compression strain capacity may be exceeded.Research into the local buckling failure and accurate prediction of the compressive strain capacity are important.A finite element model of a pipeline with a dent is established.Local buckling failure under a bending moment is investigated,and the compressive strain capacity is calculated.The effects of different parameters on pipeline local buckling are analyzed.The results show that the dent depth,external pressure and internal pressure lead to different local buckling failure modes of the pipeline.A higher internal pressure indicates a larger compressive strain capacity,and the opposite is true for external pressure.When the ratio of external pressure to collapse pressure of intact pipeline is greater than 0.1,the deeper the dent,the greater the compressive strain capacity of the pipeline.And as the ratio is less than 0.1,the opposite is true.On the basis of these results,a regression equation for predicting the compressive strain capacity of a dented submarine pipeline is proposed,which can be referred to during the integrity assessment of a submarine pipeline.

The deteriorated degradation resistance of Mg alloy microtubes for vascular stent under the coupling effect of radial compressive stress and dynamic medium

The degradation of Mg alloys relates to the service performance of Mg alloy biodegradable implants.In order to investigate the degradation behavior of Mg alloys as vascular stent materials in the near service environment,the hot-extruded fine-grained Mg-Zn-Y-Nd alloy microtubes,which are employed to manufacture vascular stents,were tested under radial compressive stress in the dynamic Hanks'Balanced Salt Solution(HBSS).The results revealed that the high flow rate accelerates the degradation of Mg alloy microtubes and its degradation is sensitive to radial compressive stress.These results contribute to understanding the service performance of Mg alloys as vascular stent materials.

Compressive property and energy absorption characteristic of interconnected porous Mg-Zn-Y alloys with adjusting Y addition

In this study,interconnected porous Mg-2Zn-xY alloys with different phase compositions were prepared by various Y additions(x=0.4,3,and 6 wt.%)to adjust the compressive properties and energy absorption characteristics.Several characterization methods were then applied to identify the microstructure of the porous Mg-Zn-Y and describe the details of the second phase.Compressive tests were performed at room temperature(RT),200℃,and 300℃to study the impact of the Y addition and testing temperature on the compressive properties of the porous Mg-Zn-Y.The experimental results showed that a high Y content promotes a microstructure refinement and increases the volume fraction of the second phase.When the Y content increases,different Mg-Zn-Y ternary phases appear:I-phase(Mg_(3)Zn_(6)Y),W-phase(Mg_(3)Zn_(3)Y_(2)),and LPSO phase(Mg_(12)ZnY).When the Y content ranges between 0.4%and 6%,the compressive strength increases from 6.30MPa to 9.23 MPa,and the energy absorption capacity increases from 7.33 MJ/m^(3)to 10.97 MJ/m^(3)at RT,which is mainly attributed to the phase composition and volume fraction of the second phase.However,the average energy absorption efficiency is independent of the Y content.In addition,the compressive deformation behaviors of the porous Mg-Zn-Y are altered by the testing temperature.The compressive strength and energy absorption capacity of the porous Mg-Zn-Y decrease due to the softening effect of the high temperature on the struts.The deformation behaviors at different temperatures are finally observed to reflect the failure mechanisms of the struts.

Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods

In cold regions,the dynamic compressive strength(DCS)of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering.Nevertheless,testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive.Therefore,this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques.Initially,a database of DCS for frozen-thawed rocks,comprising 216 rock specimens,was compiled.Three external load parameters(freeze-thaw cycle number,confining pressure,and impact pressure)and two rock parameters(characteristic impedance and porosity)were selected as input variables,with DCS as the predicted target.This research optimized the kernel scale,penalty factor,and insensitive loss coefficient of the support vector regression(SVR)model using five swarm intelligent optimization algorithms,leading to the development of five hybrid models.In addition,a statistical DCS prediction equation using multiple linear regression techniques was developed.The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes.A sensitivity analysis based on the cosine amplitude method has also been conducted.The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions.Among these models,the SVR model optimized with the chameleon swarm algorithm exhibited the best performance,with metrics indicating its effectiveness,including root mean square error(RMSE)﹦3.9675,mean absolute error(MAE)﹦2.9673,coefficient of determination(R^(2))﹦0.98631,and variance accounted for(VAF)﹦98.634.This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models.Notably,impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction.This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Determination of Material Parameters of EVA Foam under Uniaxial Compressive Testing Using Hyperelastic Models

The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of EVA foam was also evaluated by scanning electron microscopy(SEM).The results show that Blatz-Ko and Neo-Hookean model can fit the curve at 5%and 8%strain,respectively.The Mooney model can fit the curve at 50%strain.The modulus of rigidity evaluated from Mooney model is 0.0814±0.0027 MPa.The structure of EVA foam from SEM image shows that EVA structure is a closed cell with homogeneous porous structure.From the result,it is found that Mooney model can adjust the data better than other models.This model can be applied for mechanical response prediction of EVA foam and also for reference value in engineering application.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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.

Chaotic CS Encryption:An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.

Fluid-Related Performances and Compressive Strength of Clinker-Free Cementitious Backfill Material Based on Phosphate Tailings

Phosphate tailings are usually used as backfill material in order to recycle tailings resources.This study considers the effect of the mix proportions of clinker-free binders on the fluidity,compressive strength and other key performances of cementitious backfill materials based on phosphate tailings.In particular,three solid wastes,phosphogypsum(PG),semi-aqueous phosphogypsum(HPG)and calcium carbide slag(CS),were selected to activate wet ground granulated blast furnace slag(WGGBS)and three different phosphate tailings backfill materials were prepared.Fluidity,rheology,settling ratio,compressive strength,water resistance and ion leaching behavior of backfill materials were determined.According to the results,when either PG or HPG is used as the sole activator,the fluidity properties of the materials are enhanced.Phosphate tailings backfill material activated with PG present the largest fluidity and the lowest yield stress.Furthermore,the backfill material’s compressive strength is considerably increased to 2.9 MPa at 28 days after WGGBS activation using a mix of HPG and CS,all with a settling ratio of only 1.15 percent.Additionally,all the three ratios of binder have obvious solidification effects on heavy metal ions Cu and Zn,and P in phosphate tailings.

An Investigation into the Compressive Strength,Permeability and Microstructure of Quartzite-Rock-Sand Mortar

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.

An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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.

Compressive performance of innovative reinforced pillars in closed/abandoned mines

Pillar is closely related to the stability and reliability of underground spaces in closed/abandoned mines.The present research introduced a new technique to strengthen square cement mortar columns via fiber-reinforced polymer(FRP)strips to verify the strengthening effect of FRP on pillars.Compared to a fully wrapped FRP jacket,the advantages of FRP strip are cost-effective and easy-to-construct.A series of compression tests as well as theoretical analysis were carried out to explore the mechanical behavior of square cement mortar specimens partially strengthened with FRP strips.The results verified the effectiveness of FRP strips in enhancing the stress and strain of cement mortar.Different from unconfined cement mortar specimens,these FRP-strengthened cement mortar specimens are featured with the double-peaked behaviors,mainly attributed to the stress state transformation from a one-dimensional to a three-dimensional stress state.It also indicated that the enhancement of stress increased with the FRP strip width.Moreover,the brittle-ductile transition ductile failure characteristics were also observed in FRP-confined cement mortar specimens.The ultimate ductility of the cement mortar specimen decreases gradually with the growth of the FRP strip width.The main contribution of this research is to enrich the strengthening techniques for residual pillars.

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and Optimization Algorithms

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.

Unconfined compressive strength and failure behaviour of completely weathered granite from a fault zone

Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.

Acetic acid-assisted mild dealloying of fine CuPd nanoalloys achieving compressive strain toward high-efficiency oxygen reduction and ethanol oxidation electrocatalysis

Dealloying by which the transition metal is partially or completely leached from an alloy precursor is an effective way to optimize the fundamental effects for further enhancing the electrocatalysis of a catalyst.Herein,to address the deficiencies associated with the commonly used dealloying methods,for example,electrochemical and sulfuric acid/nitric acid treatment,we report an acetic acid-assisted mild strategy to dealloy Cu atoms from the outer surface layers of CuPd alloy nanoparticles to achieve high-efficiency electrocatalysis for oxygen reduction and ethanol oxidation in an alkaline electrolyte.The leaching of Cu atoms by acetic acid exerts an additional compressive strain effect on the surface layers and exposes more active Pd atoms,which is beneficial for boosting the catalytic performance of a dealloyed catalyst for the oxygen reduction reaction(ORR)and the ethanol oxidation reaction(EOR).In particular,for ORR,the CuPd nanoparticles with a Pd/Cu molar ratio of 2:1 after acetic dealloying show a half-wave potential of 0.912 V(vs.RHE)and a mass activity of 0.213 AmgPd^(-1) at 0.9 V,respectively,while for EOR,the same dealloyed sample has a mass activity and a specific activity of 8.4 Amg^(-1) and 8.23 mA cm^(-2),respectively,much better than their dealloyed counterparts at other temperatures and commercial Pd/C as well as a Pt/C catalyst.

Effect of compressive strength on the performance of the NEMO-LIM model in Arctic Sea ice simulation

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.

Effect of ball milling time on the microstructure and compressive properties of the Fe–Mn–Al porous steel

In the present work,Fe–Mn–Al–C powder mixtures were manufactured by elemental powders with different ball milling time,and the porous high-Mn and high-Al steel was fabricated by powder sintering.The results indicated that the powder size significantly decreased,and the morphology of the Fe powder tended to be increasingly flat as the milling time increased.However,the prolonged milling duration had limited impact on the phase transition of the powder mixture.The main phases of all the samples sintered at 640℃ were α-Fe,α-Mn and Al,and a small amount of Fe2Al5 and Al8Mn5.When the sintering temperature increased to 1200℃,the phase composition was mainly comprised of γ-Fe and α-Fe.The weight loss fraction of the sintered sample decreased with milling time,i.e.,8.3wt% after 20 h milling compared to15.3wt% for 10 h.The Mn depletion region(MDR) for the 10,15,and 20 h milled samples was about 780,600,and 370 μm,respectively.The total porosity of samples sintered at 640℃ decreased from ~46.6vol% for the 10 h milled powder to ~44.2vol% for 20 h milled powder.After sintering at 1200℃,the total porosity of sintered samples prepared by 10 and 20 h milled powder was ~58.3vol% and ~51.3vol%,respectively.The compressive strength and ductility of the 1200℃ sintered porous steel increased as the milling time increased.

A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill

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.