Developments and Applications of Neutrosophic Theory in Civil Engineering Fields:A Review

Neutrosophic theory can effectively and reasonably express indeterminate,inconsistent,and incomplete information.Since Smarandache proposed the neutrosophic theory in 1998,neutrosophic theory and related research have been developed and applied to many important fields.Indeterminacy and fuzziness are one of the main research issues in the field of civil engineering.Therefore,the neutrosophic theory is very suitable for modeling and applications of civil engineering fields.This review paper mainly describes the recent developments and applications of neutrosophic theory in four important research areas of civil engineering:the neutrosophic decision-making theory and applied methods,the neutrosophic evaluation methods and applications of slope stability,the neutrosophic expressions and analyses of rock joint roughness coefficient,and the neutrosophic structural optimization methods and applications.In terms of these research achievements in the four areas of civil engineering,the neutrosophic theory demonstrates its advantages in dealing with the indeterminate and inconsistent issues in civil engineering and the effectiveness and practicability of existing applied methods.In the future work,the existing research results will be further improved and extended in civil engineering problems.In addition,the neutrosophic theory will also have better application prospects in other fields of civil engineering.

Fiber optic monitoring of an anti-slide pile in a retrogressive landslide

Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.

Unraveling the hydraulic properties of loess for landslide prediction:A study on variations in loess landslides in Lanzhou,Dingxi,and Tianshui,China

Loess has distinctive characteristics,leading to frequent landslide disasters and posing serious threats to the lives and properties of local re sidents.The involvement of water repre sents a critical factor in inducing loess landslides.This study focuses on three neighboring cities sequentially situated on the Loess Plateau along the direction of aeolian deposition of loess,namely Lanzhou,Dingxi,and Tianshui,which are densely populated and prone to landslide disasters.The variations in hydraulic properties,including water retention capacity and permeability,are investigated through Soil Water Characteristic Curve(SWCC)test and hydraulic conductivity test.The experimental findings revealed that Tianshui loess exhibited the highest water retention capacity,followed by Dingxi loess,while Lanzhou loess demonstrated the lowest water retention capacity.Contrastingly,the results for the saturated permeability coefficient were found to be the opposite:Tianshui loess showed the lowest permeability,whereas Lanzhou loess displayed the highest permeability.These results are supported and analyzed by scanning electron microscopy(SEM)observation.In addition,the water retention capacity is mathematically expressed using the van Genuchten model and extended to predict unsaturated hydraulic properties of loess.The experimental results exhibit a strong accordance with one another and align with the regional distribution patterns of disasters.

Dynamic Resistance and Energy Absorption of Sandwich Beam via a Micro-Topology Optimization

The current research of sandwich structures under dynamic loading mainly focus on the response characteristic of structure.The micro-topology of core layers would sufficiently influence the property of sandwich structure.However,the micro deformation and topology mechanism of structural deformation and energy absorption are unclear.In this paper,based on the bi-directional evolutionary structural optimization method and periodic base cell(PBC)technology,a topology optimization frame work is proposed to optimize the core layer of sandwich beams.The objective of the present optimization problem is to maximize shear stiffness of PBC with a volume constraint.The effects of the volume fraction,filter radius,and initial PBC aspect ratio on the micro-topology of the core were discussed.The dynamic response process,core compression,and energy absorption capacity of the sandwich beams under blast impact loading were analyzed by the finite element method.The results demonstrated that the overpressure action stage was coupled with the core compression stage.Under the same loading and mass per unit area,the sandwich beam with a 20%volume fraction core layer had the best blast resistance.The filter radius has a slight effect on the shear stiffness and blast resistances of the sandwich beams.But increasing the filter radius could slightly improve the bending stiffness.Upon changing the initial PBC aspect ratio,there are three ways for PBC evolution:The first is to change the angle between the adjacent bars,the second is to further form holes in the bars,and the third is to combine the first two ways.However,not all three ways can improve the energy absorption capacity of the structure.Changing the aspect ratio of the PBC arbitrarily may lead to worse results.More studies are necessary for further detailed optimization.This research proposes a new topology sandwich beam structure by micro-topology optimization,which has sufficient shear stiffness.The micro mechanism of structural energy absorption is clarified,it is significant for structural energy absorption design.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

Nonlinear constitutive models of rock structural plane and their applications

Structural planes play an important role in controlling the stability of rock engineering,and the influence of structural planes should be considered in the design and construction process of rock engineering.In this paper,mechanical properties,constitutive theory,and numerical application of structural plane are studied by a combination method of laboratory tests,theoretical derivation,and program development.The test results reveal the change laws of various mechanical parameters under different roughness and normal stress.At the pre-peak stage,a non-stationary model of shear stiffness is established,and threedimensional empirical prediction models for initial shear stiffness and residual stage roughness are proposed.The nonlinear constitutive models are established based on elasto-plastic mechanics,and the algorithms of the models are developed based on the return mapping algorithm.According to a large number of statistical analysis results,empirical prediction models are proposed for model parameters expressed by structural plane characteristic parameters.Finally,the discrete element method(DEM)is chosen to embed the constitutive models for practical application.The running programs of the constitutive models have been compiled into the discrete element model library.The comparison results between the proposed model and the Mohr-Coulomb slip model show that the proposed model can better describe nonlinear changes at different stages,and the predicted shear strength,peak strain and shear stiffness are closer to the test results.The research results of the paper are conducive to the accurate evaluation of structural plane in rock engineering.

Effects of data smoothing and recurrent neural network(RNN)algorithms for real-time forecasting of tunnel boring machine(TBM)performance

Tunnel boring machines(TBMs)have been widely utilised in tunnel construction due to their high efficiency and reliability.Accurately predicting TBM performance can improve project time management,cost control,and risk management.This study aims to use deep learning to develop real-time models for predicting the penetration rate(PR).The models are built using data from the Changsha metro project,and their performances are evaluated using unseen data from the Zhengzhou Metro project.In one-step forecast,the predicted penetration rate follows the trend of the measured penetration rate in both training and testing.The autoregressive integrated moving average(ARIMA)model is compared with the recurrent neural network(RNN)model.The results show that univariate models,which only consider historical penetration rate itself,perform better than multivariate models that take into account multiple geological and operational parameters(GEO and OP).Next,an RNN variant combining time series of penetration rate with the last-step geological and operational parameters is developed,and it performs better than other models.A sensitivity analysis shows that the penetration rate is the most important parameter,while other parameters have a smaller impact on time series forecasting.It is also found that smoothed data are easier to predict with high accuracy.Nevertheless,over-simplified data can lose real characteristics in time series.In conclusion,the RNN variant can accurately predict the next-step penetration rate,and data smoothing is crucial in time series forecasting.This study provides practical guidance for TBM performance forecasting in practical engineering.

Cloud-Model-Based Feature Engineering to Analyze the Energy-Water Nexus of a Full-Scale Wastewater Treatment Plant

Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.

Biomineralization and mineralization using microfluidics:A comparison study

Biomineralization through microbial process has attracted great attention in the field of geotechnical engineering due to its ability to bind granular materials,clog pores,and seal fractures.Although minerals formed by biomineralization are generally the same as that by mineralization,their mechanical behaviors show a significant discrepancy.This study aims to figure out the differences between biomineralization and mineralization processes by visualizing and tracking the formation of minerals using microfluidics.Both biomineralization and mineralization processes occurred in the Y-shaped sandcontaining microchip that mimics the underground sand layers.Images from different areas in the reaction microchannel of microchips were captured to directly compare the distribution of minerals.Crystal size and numbers from different reaction times were measured to quantify the differences between biomineralization and mineralization processes in terms of crystal kinetics.Results showed that the crystals were precipitated in a faster and more uncontrollable manner in the mineralization process than that in the biomineralization process,given that those two processes presented similar precipitation stages.In addition,a more heterogeneous distribution of crystals was observed during the biomineralization process.The precipitation behaviors were further explained by the classical nucleation crystal growth theory.The present microfluidic tests could advance the understanding of biomineralization and provide new insight into the optimization of biocementation technology.

Recycling arsenic-containing bio-leaching residue after thermal treatment in cemented paste backfill:Structure modification,binder properties and environmental assessment

The substantial arsenic(As)content present in arsenic-containing bio-leaching residue(ABR)presents noteworthy environ-mental challenges attributable to its inherent instability and susceptibility to leaching.Given its elevated calcium sulfate content,ABR exhibits considerable promise for industrial applications.This study delved into the feasibility of utilizing ABR as a source of sulfates for producing super sulfated cement(SSC),offering an innovative binder for cemented paste backfill(CPB).Thermal treatment at varying temperatures of 150,350,600,and 800℃ was employed to modify ABR’s performance.The investigation encompassed the examination of phase transformations and alterations in the chemical composition of As within ABR.Subsequently,the hydration characteristics of SSC utilizing ABR,with or without thermal treatment,were studied,encompassing reaction kinetics,setting time,strength development,and microstructure.The findings revealed that thermal treatment changed the calcium sulfate structure in ABR,consequently impacting the resultant sample performance.Notably,calcination at 600℃ demonstrated optimal modification effects on both early and long-term strength attributes.This enhanced performance can be attributed to the augmented formation of reaction products and a densified micro-structure.Furthermore,the thermal treatment elicited modifications in the chemical As fractions within ABR,with limited impact on the As immobilization capacity of the prepared binders.

Digital Twins for Engineering Asset Management:Synthesis,Analytical Framework,and Future Directions

Effective engineering asset management(EAM)is critical to economic development and improving livability in society,but its complexity often impedes optimal asset functionalities.Digital twins(DTs)could revolutionize the EAM paradigm by bidirectionally linking the physical and digital worlds in real time.There is great industrial and academic interest in DTs for EAM.However,previous review studies have predominately focused on technical aspects using limited life-cycle perspectives,failing to holistically synthesize DTs for EAM from the managerial point of view.Based on a systematic literature review,we introduce an analytical framework for describing DTs for EAM,which encompasses three levels:DT 1.0 for technical EAM,DT 2.0 for technical-human EAM,and DT 3.0 for technical-environmental EAM.Using this framework,we identify what is known,what is unknown,and future directions at each level.DT 1.0 addresses issues of asset quality,progress,and cost management,generating technical value.It lacks multi-objective self-adaptive EAM,however,and suffers from high application cost.It is imperative to enable closed-loop EAM in order to provide various functional services with affordable DT 1.0.DT 2.0 accommodates issues of human-machine symbiosis,safety,and flexibility management,generating managerial value beyond the technical performance improvement of engineering assets.However,DT 2.0 currently lacks the automation and security of human-machine interactions and the managerial value related to humans is not prominent enough.Future research needs to align technical and managerial value with highly automated and secure DT 2.0.DT 3.0 covers issues of participatory governance,organization management,sustainable development,and resilience enhancement,generating macro social value.Yet it suffers from organizational fragmentation and can only address limited social governance issues.Numerous research opportunities exist to coordinate different stakeholders.Similarly,future research opportunities exist to develop DT 3.0 in a more open and complex system.

Application of Optical Hydrogels in Environmental Sensing

The ever-increasing complexity of environmental pollutants urgently warrants the development of new detection technologies.Sensors based on the optical properties of hydrogels enabling fast and easy in situ detection are attracting increasing attention.In this paper,the data from 138 papers about different optical hydrogels(OHs)are extracted for statistical analysis.The detection performance and potential of various types of OHs in different environmental pollutant detection scenarios were evaluated and compared to those obtained using the standard detection method.Based on this analysis,the target recognition and sensing mechanisms of two main types of OHs are reviewed and discussed:photonic crystal hydrogels(PCHs)and fluorescent hydrogels(FHs).For PCHs,the environmental stimulus response,target receptors,inverse opal structures,and molecular imprinting techniques related to PCHs are reviewed and summarized.Furthermore,the different types of fluorophores(i.e.,compound probes,biomacromolecules,quantum dots,and luminescent microbes)of FHs are discussed.Finally,the potential academic research directions to address the challenges of applying and developing OHs in environmental sensing are proposed,including the fusion of various OHs,introduction of the latest technologies in various fields to the construction of OHs,and development of multifunctional sensor arrays.

Direct recycling of Li-ion batteries from cell to pack level:Challenges and prospects on technology,scalability,sustainability,and economics

Direct recycling is a novel approach to overcoming the drawbacks of conventional lithium-ion battery(LIB)recycling processes and has gained considerable attention from the academic and industrial sectors in recent years.The primary objective of directly recycling LIBs is to efficiently recover and restore the active electrode materials and other components in the solid phase while retaining electrochemical performance.This technology's advantages over traditional pyrometallurgy and hydrometallurgy are costeffectiveness,energy efficiency,and sustainability,and it preserves the material structure and morphology and can shorten the overall recycling path.This review extensively discusses the advancements in the direct recycling of LIBs,including battery sorting,pretreatment processes,separation of cathode and anode materials,and regeneration and quality enhancement of electrode materials.It encompasses various approaches to successfully regenerate high-value electrode materials and streamlining the recovery process without compromising their electrochemical properties.Furthermore,we highlight key challenges in direct recycling when scaled from lab to industries in four perspectives:(1)battery design,(2)disassembling,(3)electrode delamination,and(4)commercialization and sustainability.Based on these challenges and changing market trends,a few strategies are discussed to aid direct recycling efforts,such as binders,electrolyte selection,and alternative battery designs;and recent transitions and technological advancements in the battery industry are presented.

Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world problems.This comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational techniques.The history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning techniques.Through a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering landscapes.This compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

Infection of leukemia in humans causes many complications in its later stages.It impairs bone marrow’s ability to produce blood.Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case.The binary classification is employed to distinguish between normal and leukemiainfected cells.In addition,various subtypes of leukemia require different treatments.These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia.This entails using multi-class classification to determine the leukemia subtype.This is usually done using a microscopic examination of these blood cells.Due to the requirement of a trained pathologist,the decision process is critical,which leads to the development of an automated software framework for diagnosis.Researchers utilized state-of-the-art machine learning approaches,such as Support Vector Machine(SVM),Random Forest(RF),Na飗e Bayes,K-Nearest Neighbor(KNN),and others,to provide limited accuracies of classification.More advanced deep-learning methods are also utilized.Due to constrained dataset sizes,these approaches result in over-fitting,reducing their outstanding performances.This study introduces a deep learning-machine learning combined approach for leukemia diagnosis.It uses deep transfer learning frameworks to extract and classify features using state-of-the-artmachine learning classifiers.The transfer learning frameworks such as VGGNet,Xception,InceptionResV2,Densenet,and ResNet are employed as feature extractors.The extracted features are given to RF and XGBoost classifiers for the binary and multi-class classification of leukemia cells.For the experimentation,a very popular ALL-IDB dataset is used,approaching a maximum accuracy of 100%.A private real images dataset with three subclasses of leukemia images,including Acute Myloid Leukemia(AML),Chronic Lymphocytic Leukemia(CLL),and Chronic Myloid Leukemia(CML),is also employed to generalize the system.This dataset achieves an impressive multi-class classification accuracy of 97.08%.The proposed approach is robust and generalized by a standardized dataset and the real image dataset with a limited sample size(520 images).Hence,this method can be explored further for leukemia diagnosis having a limited number of dataset samples.

Mechanical properties and damage constitutive model of sandstone after acid corrosion and high temperature treatments

Aiming at the problem of temperature-mechanics-chemical(T-M-C)action encountered by rocks in underground engineering,sandstone was selected as the sample for acid corrosion treatment at pH 1,3,5 and 7,the acid corrosion treated samples were then subjected to high-temperature experiments at 25,300,600,and 900℃,and triaxial compression experiments were conducted in the laboratory.The experimental results show that the superposition of chemical damage and thermal damage has a significant impact on the quality,wave velocity,porosity and compression failure characteristics of the rock.Based on the Lemaitre strain equivalent hypothesis theory,the damage degree of rock material was described by introducing damage variables,and the spatial mobilized plane(SMP)criterion was adopted.The damage constitutive model can well reflect the stress-strain characteristics of the rock triaxial compression process,which verified the rationality and reliability of the model parameters.The experiment and constitutive model analyzed the change law of mechanical properties of rock after chemical corrosion and high temperature thermal damage,which had certain practical significance for rock engineering construction.

Assessment of cyclic deformation and critical stress amplitude of jointed rocks via cyclic triaxial testing

Jointed rock specimens with a natural replicated joint surface oriented at a mean dip angle of 60were prepared,and a series of cyclic triaxial tests was performed at different confining pressures and cyclic deviatoric stress amplitudes.The samples were subjected to 10,000 loading-unloading cycles with a frequency of 8 Hz.At each level of confining pressure,the applied cyclic deviatoric stress amplitude was increased incrementally until excessive deformation of the jointed rock specimen was observed.Analysis of the test results indicated that there existed a critical cyclic deviatoric stress amplitude(i.e.critical dynamic deviatoric stress)beyond which the jointed rock specimens yielded.The measured critical dynamic deviatoric stress was less than the corresponding static deviatoric stress.At cyclic deviatoric stress amplitudes less than the critical dynamic deviatoric stress,minor cumulative residual axial strains were observed,resulting in hysteretic damping.However,for cyclic deviatoric stresses beyond the critical dynamic deviatoric stress,the plastic strains increased promptly,and the resilient moduli degraded rapidly during the initial loading cycles.Cyclic triaxial test results showed that at higher confining pressures,the ultimate residual axial strain attained by the jointed rock specimen decreased,the steadystate dissipated energy density and steady-state damping ratio per load cycle decreased,while steadystate resilient moduli increased.

Method for visualizing the shear process of rock joints using 3D laser scanning and 3D printing techniques

This study presents a visualized approach for tracking joint surface morphology.Three-dimensional laser scanning(3DLS)and 3D printing(3DP)techniques are adopted to record progressive failure during rock joint shearing.The 3DP resin is used to create transparent specimens to reproduce the surface morphology of a natural joint precisely.The freezing method is employed to enhance the mechanical properties of the 3DP specimens to reproduce the properties of hard rock more accurately.A video camera containing a charge-coupled device(CCD)camera is utilized to record the evolution of damaged area of joint surface during the direct shear test.The optimal shooting distance and shooting angle are recommended to be 800 mm and 40?,respectively.The images captured by the CCD camera are corrected to quantitatively describe the damaged area on the joint surface.Verification indicates that this method can accurately describe the total sheared areas at different shear stages.These findings may contribute to elucidating the shear behavior of rock joints.

Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

Particle aggregation and breakage kinetics in cemented paste backfill

The macroscopic flow behavior and rheological properties of cemented paste backfill(CPB)are highly impacted by the inherent structure of the paste matrix.In this study,the effects of shear-induced forces and proportioning parameters on the microstructure of fresh CPB were studied.The size evolution and distribution of floc/agglomerate/particles of paste were monitored by focused beam reflection measuring(FBRM)technique,and the influencing factors of aggregation and breakage kinetics of CPB were discussed.The results indicate that influenced by both internal and external factors,the paste kinetics evolution covers the dynamic phase and the stable phase.Increasing the mass content or the cement-tailings ratio can accelerate aggregation kinetics,which is advantageous for the rise of average floc size.Besides,the admixture and high shear can improve breaking kinetics,which is beneficial to reduce the average floc size.The chord length resembles a normal distribution somewhat,with a peak value of approximate 20μm.The particle disaggregation con-stant(k_(2))is positively correlated with the agitation rate,and k_(2) is five orders of magnitude greater than the particle aggregation constant(k1).The kinetics model depicts the evolution law of particles over time quantitatively and provides a theoretical foundation for the micromechanics of complicated rheological behavior of paste.