Concrete Design Research Articles

Determination of Optimized Mix Design of Reactive Powder Concrete

There are different types of reactive powder concrete (RPC) and researchers continue to develop better-quality mix designs. This research presents an integrated approach for RPC mix design. For this purpose, 13 RPC mix designs were collected according to expert opinion and laboratory samples and were tested in this study for validation of the characteristics of compressive strength and water absorption. The samples were then ranked using the simple additive weighting (SAW) method, and three highest-quality RPCs were selected for the Taguchi method. These RPCs were used to prepare 27 experimental RPC mix designs applying the Taguchi method. From the experimental results, compressive strength with 0.38–0.76% and water absorption with 0.50–0.99% differences were more appropriate in compliance with the collected data. Also, results from the 27 mix designs investigated by the Taguchi method revealed the optimized mix design for the maximum compressive strength with 146.7 MPa and the optimized mix design for the minimum water absorption with 0.89%. The results showed that our approach was consistent with the results of classic methods that require a large number of samples. This suggests that integrating the SAW and Taguchi methods is an appropriate approach for screening and optimizing RPC mix design.

Densified mixture design algorithm: A novel mix design concept and its application for green concretes incorporating industrial by‐products

AbstractDespite the fact that densified mixture design algorithm (DMDA) has been identified as a promising tool for green concrete mix design, its use in real practice has still been minimal worldwide. Due to the lack of precise information on DMDA computation in earlier studies, this work proposes DMDA including fly ash and ground granulated blast‐furnace slag as a viable way to improve the long‐term performance of green concrete. To encourage the use of DMDA, this study offers a detailed description of the DMDA mix design, as well as a step‐by‐step standard operating procedure and an empirical example of the computation. Assessments of the long‐term performance of green mixtures (self‐consolidating concrete and high‐performance concrete) and ordinary Portland cement concrete (designed using the conventional method of American Concrete Institute, ACI) were made. Performances of the ACI (O6000 mixture) and DMDA concretes (S6000 and H6000 mixtures) were then compared in terms of compressive strength, elastic modulus, shrinkage, and creep strain. As a result, all of the concrete samples attained the target strength of above 41 MPa (6000 psi) after only 28 days. Moreover, compressive strength, elastic modulus, shrinkage, and creep strain increased significantly in all of the concretes over curing time. Importantly, the experimental results confirmed that both types of DMDA concrete exhibited higher compressive strength and better long‐term performance than the ACI concrete under elastic modulus, creep, and shrinkage aspects.

An atomistic based continuum level 3D mode-I meso-fracture criterion for cement-based concrete

This paper establishes an atomistic based continuum level 3D mode-I meso-fracture criterion for cement-based concrete from a multiscale perspective. Firstly, on the basis of the 3D self-consistent finite stress model and the 3D mesoscale crack path model, a meso-fracture criterion is theoretically deduced by considering both the crack tip stress state and the crack path energy consumption conditions. Secondly, with the help of MD simulation technique, a systematic atomistic level modelling approach for the study of interaction behaviors between mortar and aggregate in cement-based concrete is developed, and based on which, a new determination method for the critical mesoscopic fracture index is proposed. Thirdly, using mesoscale fracture test results as experimental benchmark for numerical models, the proposed meso-fracture criterion is verified through FE simulations based on a tri-phase numerical model. It is expected this work could lay a foundation for the following investigations on concrete performance design.

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms.

The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C3A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R2 values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

Improving building thermal comfort through passive design – An experimental analysis of phase change material 3D printed concrete

As the effects of climate change are felt around the world, it is important to consider more sustainable practices in every industry to reduce greenhouse gas emissions and preserve resources. Sustainable practices in the building sector and construction industry include incorporating passive design solutions into the building design and constructing with recycled materials. In this research the main passive design solution considered is thermal energy storage by latent heat capacity of phase change materials (PCMs). Construction with bricks is widely used in the developing world, allowing much potential for the use of recycled brick aggregate in construction composites. This research is the first of its kind on PCM inclusion by aggregate impregnation of recycled brick aggregate in 3D printed concrete (3DPC). Two cavity façade sections are 3D printed using two concrete mix designs - the first, in which 64% of the natural aggregate in the mix is replaced with recycled brick aggregate, and the second, adjusted from the first by an addition of PCM to the pores of the recycled brick aggregate by vacuum impregnation, creating concrete with a total average latent heat capacity of 7360 J/kg. These façade sections are used in four sets of thermal tests in Stellenbosch, South Africa. It is concluded that by latent heat storage, PCM delays heat transfer through the PCM-3DPC passive design façade section, significantly increasing the time that internal ambient temperatures remain within standardised ranges of thermal comfort, and reducing the maximum internal ambient temperatures by up to 3.9 °C. Minimal leakage of PCM is proven by effectiveness of the PCM in the PCM-3DPC façade section after five months of exposure to outdoor ambient spring and summer conditions.

BIM-Based Timber Formwork Design and Modeling

Building information modeling (BIM) is one of the most promising new technological developments that can be used to facilitate design and construction automation. Prior BIM research has primarily focused on permanent building components; temporary structures such as scaffolding and concrete formwork have received little attention. The design and planning of concrete formwork systems, however, would substantially benefit from the capabilities of BIM. Formwork design is repetitive and can be very time-consuming due to the need to analyze multiple parameters of individual permanent components, the latest formwork design guidelines, methods, and procedures, and the available materials. In this study, a BIM-based tool is developed and proposed to assist designers and builders in planning, designing, and generating concrete formwork design models for planning purposes. By integrating the design procedures recommended by the American Concrete Institute (ACI), the streamlining tool automates the design and modeling process for timber concrete formwork through an application programming interface (API) in Revit. The workability of the proposed tool was demonstrated through a case study. The study also employed the survey method to investigate the effectiveness of the proposed tool and its potential for improving worker safety and health from the perspectives of formwork professionals. Using the tool, form designers and planners will be able to determine the most applicable formwork design based on the permanent facility structure along with the availability of construction materials, site conditions present, and worker safety and health considerations. The study also provides contractors with a new tool for planning concrete operations and expands the scope of BIM design.

An In-Depth Evaluation of Externally Amplified Coupling (EAC) Attacks—A Concrete Threat for Masked Cryptographic Implementations

Masking is a systematic countermeasure to achieve side-channel security for cryptographic algorithms. However, its secure implementation relies on an independence assumption that can be violated by signal coupling. It has been established that coupling induced within a device can be detrimental. It was demonstrated on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^{st}$ </tex-math></inline-formula> -order secure design (i.e., with two shares) that an adversary who can manipulate the design’s power-measurement setup can externally induce significant coupling. It can thus concretely reduce the “effective-security-order”, i.e., make <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1^{st}$ </tex-math></inline-formula> -order leakages as significant as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{nd}$ </tex-math></inline-formula> -order ones with fewer measurements. This paper explores the impact of such external amplification phenomena on fabricated hardware test cases for the first time. We designed a dedicated ASIC to extend the empirical results for demonstrating impact up to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4^{th}$ </tex-math></inline-formula> order. We have systematically evaluate d factors related to adversarial control, e.g., the external measurement resistance. We also investigate d their relative influence compared to intra-design ones, i.e., internal power-grid resistance and transistors’ inherent resistance. Our study demonstrates that externally amplified coupling scale s up to concrete masked hardware designs with various amounts of shares and is not very sensitive to intra-design parameters. Therefore, providing experimental evidence that such coupling should be considered during masking validation.

An innovative design of mega shaft boring machine (SBM) cutterhead based on TRIZ and AD theory

Cutterhead is the key component of the mega shaft boring machine (SBM), whose complex structure and huge size lead to high manufacturing difficulty and cost, and its reliability is difficult to guarantee under huge loads. It is difficult to design the cutterhead only referring to the existing small horizontal tunnel boring machine cutterhead. In order to brainstorm innovative ideas, the paper incorporates the TRIZ into AD to address the following issues. Firstly, engineering conflicts in designing process are eliminated by TRIZ tools to generating innovative ideas. Then, the decomposition of functional requirements and design parameters are employed based on independence axiom of AD, transferring the abstract ideas into concrete design solutions. Moreover, fuzzy comprehensive evaluation is adopted to choose a better design scheme from alternatives of critical component. Finally, the feasibility and practicality of the proposed approach has been demonstrated through case study of designing a novel mega SBM cutterhead. The cutterhead structure is significantly improved with embedded movable cutter boxes, which can excavate step by step to reduce the driving torque, and the manufacturing and transportation cost has been greatly reduced. The method can help designer to innovative design the key components of large-scale underground tunneling equipment.

Mix-Proportion Design Methods and Sustainable Use Evaluation of Recycled Aggregate Concrete Used in Freeze–Thaw Environment

Recycled aggregate concrete (RAC) has been gradually used in practical engineering owing to the maturity of waste-concrete recycling technology. However, the disposal of waste RAC in different environments remains challenging. Second-generation recycled concrete aggregates (second RCAs) prepared by crushing waste RAC can further promote the sustainable development of the construction industry, in which the mix-proportion design method of RAC used in different environments is key in determining the feasibility of recycling second RCA. In this study, to investigate the usability of second RCA, five typical concrete mix-design methods were selected to prepare RAC with a service life of 50 years for freeze-thaw (F–T) environments in cold regions: the ordinary concrete design method, high-performance method, saturated surface-dry aggregate design method, equal mortar volume method (RAC-E), and overall calculation method (RAC-A). The frost resistance of RACs and the physical properties of second RCAs were investigated, and the economy of recycling waste RAC with different mix-proportion design methods was evaluated via value engineering analysis. The results showed that after 350 F–T cycles, the mass loss and relative dynamic modulus of elasticity of RAC-E and RAC-A were better than those of specimens prepared via other methods. Compared with the original RCA, the second RCAs obtained using waste RAC in the F–T environment performed better, and their water absorption was reduced by 23%. A value analysis of the RAC–waste RAC–second RCA circulation system showed that RAC-A exhibited the highest value coefficient. Both the equal mortar volume and overall calculation methods can be used for the mix design of RAC in the F–T environment; however, the latter is a better method owing to its higher value coefficient and lower calculation error.

Experimental Study on Concrete by Using Polymer and Partial Repalcement of M.Sand

Abstract: Polymer concrete (pc) is a composite material in which the binder consists entirely of a synthetic organic polymer. It is increasing popularity as a new construction material due to its high compressive, tensile and flexural strengths, short curing time and impact resistance. This paper explores a research study which has been initiated to improve fundamental understanding of this material and to provide the knowledge required for its broad utilization. Hence a comparison has made between the conventional concrete and polymer concrete along with Partial Repalcement of sand By M.Sand. The mix design of M25 grade concrete is done. In experimental methods such as compressive strength test and Flexural strength test is performed. Polymer resin concrete with resin percentage 0.1% to 0.3% and replacement of M.sand with 10% to 30% is performed and Compared the results with conventional concrete.

The Impact of Density and Porosity on Thermal Properties of Kernelrazzo Concrete Floor Finish

Understanding the impact of density and porosity on the thermal characteristics of kernelrazzo concrete floor finish was the study's main goal. Kernelrazzo is a combination of palm kernel shell, marble chippings in place of terrazzo, quarry dust, cement, palm oil fuel ash and water. In this study, it was determined which factors normally have the biggest influence on the thermal development of kernelrazzo concrete by conducting a scan of the literature and specifications. In this study, experiments in the laboratory were carried out at USM's HBP Concrete Lab. The components were mechanically combined in a concrete mixer with the calculated water using the Department of Environment (DoE) method. Concrete mix design for M20 grade is included in the scope of laboratory investigations. Ordinary Portland Cement (OPC), palm oil fuel ash (POFA) was used as an added pozzolana element, and quarry dust, aggregates made from palm kernel shell (PKS), and marble chippings were utilized to create the mixture. By examining kernelrazzo concrete cube samples after proper curing, the study involved estimating density, specific heat, thermal diffusivity, and thermal conductivity. Density is important to control the material's thermal characteristics of kernelrazzo concrete. Lower-density kernelrazzo concrete is a sign of more porosity. In lower-density kernelrazzo concrete, specific heat capacity, thermal diffusivity, and thermal conductivity are all decreased. There is strong relationship between thermal properties, temperature, density, water, and porosity.

A comprehensive study for physical and chemical properties of road dust to utilize in concrete mix design, collected from diversified locations of Delhi NCR

Rising volume of road dust is a serious concern in global as well as Indian scenario. To find out the possible application, chemical and physical characterizations of road dust of six diversified sites of Delhi, National Capital Region, India were carried out prior to utilize in concrete mix design. The chemical characterizations suggest major and minor components are oxides of silicon, aluminium, iron, sodium, calcium, potassium, sulphur, phosphorous, manganese etc. X-ray diffraction studies depicted major phases of silica, muscovite/ illite, K-Feldspar and albite minerals. Scanning Electron Microscopy studies depicted flacks, fibrous, spherical, irregular, voids and hexagonal morphologies. Further detailed studies of road dust of two sites were carried out in concrete mix design following IS and ASTM test methods to know the effects on compressive strength, flexural strength, water and rapid chloride permeability test after replacing upto 50% of stone sand by road dust. The 7th and 28th days compressive strength test results of two sites concrete showed 16.51%, 8.25% and 4.67%, 2.34 % lesser strength in comparison to control concrete respectively. Similarly, 7th and 28th days flexural strength studies of same sites concrete showed 19.67%, 14.75% and 6.85%, 1.37% lesser strength in comparison to control concrete, respectively. The depth of water penetration test results of two selected sites of concrete showed 13.14% and 10.22% lesser extent of water penetration under 5 bars hydrostatic pressure, when compared to control concrete. The RCPT results of same sites concrete showed 33.47% and 9.48% greater extent of chloride ion permeability, in comparison to control concrete. The results obtained after 7th and 28th days of conventional stone sand concrete and road dust concrete, showed quite comparable results. However, in case of water permeability test the road dust concrete showed lesser extent of water percolation in comparison to conventional concrete, this may be due to presence of more fines in road dust, which may have caused better packing and lesser voids for water to ingress.

Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms

Concrete is the most utilized material (i.e., average production of 2 billion tons per year) for the construction of buildings, bridges, roads, dams, and several other important infrastructures. The strength and durability of these structures largely depend on the compressive strength of the concrete. The compressive strength of concrete depends on the proportionality of the key constituents (i.e., fine aggregate, coarse aggregate, cement, and water). However, the optimization of the constituent proportions (i.e., matrix design) to achieve high-strength concrete is a challenging task. Furthermore, it is essential to reduce the carbon footprint of the cementitious composites through the optimization of the matrix. In this research, machine learning algorithms including regression models, tree regression models, support vector regression (SVR), ensemble regression (ER), and gaussian process regression (GPR) were utilized to predict the compressive and tensile concrete strength. Also, the model performance was characterized based on the number of input variables utilized. The dataset used in this research was compiled from journal publications. The results showed that the exponential GPR had the highest performance and accuracy. The model had an impressive performance during the training phase, with a R2 of 0.98, RMSE of 2.412 MPa, and MAE of 1.6249 MPa when using 8 input variables to predict the compressive strength of concrete. In the testing phase, the model maintained its accuracy with a R2 of 0.99, RMSE of 0.0025134 MPa, and MAE of 0.0016367 MPa. In the training and testing phases, the exponential GPR also demonstrated high accuracy in predicting the tensile strength with an R2, RMSE, and MAE of 0.99, 0.00049247 MPa, and 0.00036929 MPa, respectively. Furthermore, in the prediction of tensile strength the number of variables utilized had an insignificant effect on the performance of the models. However, in predicting the compressive strength, an increase in the number of input variables lead to an enhancement in the performance metrics. The results of this research can allow for the quick and accurate prediction of the strength of a given concrete mixture design.

Enhancement of conventional concrete mix designs for sensible thermal energy storage applications

The study examines twenty-nine different concrete mix designs with varying constituents to improve thermal performance for energy storage at elevated temperatures up to 420 °C. Effects of variables including the type and volumetric percentage of coarse and fine aggregates, the type and replacement content of supplemental cementitious materials, water-to-cement ratio, the type and quantity of steel fiber reinforcement, and the inclusion of iron oxide powder are experimentally investigated by measuring the mechanical and thermal properties of each mix, with the overall goal of enhancing thermal performance and mitigating mechanical degradation for thermal energy storage (TES) applications. It is found that the use of siliceous aggregate with a high volumetric percentage produces the highest improvement (23 %–37 % at elevated temperatures) of concrete thermal conductivity, while silica fume replacement of Type I cement provides the highest improvement (4 %–8 % at elevated temperatures) of concrete specific heat. To simulate the operation of a TES system, thermal cycling was conducted to examine the change in concrete properties as a function of repetitive heating (during and after 50 total cycles to 420 °C). Based on the results of testing, concrete mix designs with high aggregate percentages (up to 72 % by volume), siliceous coarse aggregate, silica fume replacement of Type I cement (15 % by weight), and steel fiber reinforcement (up to 2 % by volume) facilitate enhanced TES performance by providing consistent thermal conductivity of ~2.2 W/m·K and specific heat of ~3.2 MJ/m3·K under cyclic high temperature exposure.

Probabilistic error assessment and correction of design code-based shear strength prediction models for reliability analysis of prestressed concrete girders

Aiming at probabilistic error assessment of design code models for shear strength prediction of prestressed concrete (PC) girders, this study compiled an experimental database containing 369 PC girders that failed in shear. Using the experimental database, this paper first assessed seven well-received shear strength models from five concrete structure and bridge design codes, including ACI 318-19, AASHTO LRFD 2017, CSA A23.3:19, CSA S6:19 and fib MC 2010. In view of the fact that systematic error exists in those models, polynomial correction terms were calibrated for each model together with the remaining error quantified based on the compiled experimental database and Bayesian updating. The resulted models can be used for shear strength predictions with better accuracy and, more importantly, with the model uncertainty quantified probabilistically. In the end, a case study of fragility analysis was conducted to show the benefits of the developed probabilistic models.

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Due to the intrinsic complexity, there has been no widely accepted mechanics-based estimation model of the shear performance of Fiber-Reinforced Polymer (FRP)-reinforced concrete beams. Capitalizing on a large amount of previous experimental data, data-driven machine learning (ML) models could be potentially suitable for addressing this problem. In this paper, four existing shear design provisions are reviewed and four typical ML models are analyzed. The accuracy of codified methods and ML models are compared and analyzed based on our established extensive database of FRP-reinforced concrete beams with rectangular cross sections. A series of artificially selected features considering the shear-carrying mechanisms of FRP-reinforced beams are incorporated into the proposed ML models to show their influence on the model validity. Bayesian optimization is utilized to automatically tune the hyperparameters of different ML models. Compared to the most satisfying codified predictions from CSA S806, the best ML model, XGBoost, can provide more accurate and consistent predictions for the database, with R2 enhanced by 15% and the MAE and RMSE reduced by 59% and 52%, respectively. With the selected features based on domain knowledge, the performance of ML models is further enhanced, shown by the most important features being the added ones. With outstanding performance on a large database and singular test, the ML approaches have great potential in guiding the shear design of FRP-reinforced concrete.

Development of unified elastic modulus model of natural and recycled aggregate concrete for structural applications

The development of design guidelines for structural recycled aggregate concrete (RAC) is hindered by the absence of a unified elastic modulus model focusing on the properties of diversely-sourced coarse recycled aggregates (CRA). This study develops a unified elastic modulus model of RAC and natural aggregate concrete (NAC) considering four inputs: CRA dosage, coarse aggregates/cement content, effective water/cement content, and coarse aggregates water absorption. Ten machine-learning algorithms are evaluated through various statistical approaches to explore the most suitable algorithm by establishing a database from 72 studies (i.e., 633 test results). Based on the results, CRA properties are significant for the elastic modulus prediction of RAC. All machine learning models perform better than existing models, and GBA, BRA, and XGBA models are the most suitable algorithms. The pioneering unified design equations proposed in this work can efficiently predict the elastic modulus of RAC and NAC, leading toward sustainable and cleaner concrete design.

State of the art on the hydraulic properties of pervious concrete

Pervious concrete is a type of concrete with high porosity that allows water to pass through it. It has a range of environmental benefits, including stormwater management, noise reduction, and temperature regulation. However, testing the permeability and strength of pervious concrete is costly and time-consuming, due to the many variables that can influence these properties. This paper presents research on the factors that influence the porosity and permeability of pervious concrete, including the type and size of aggregates, the water-cement ratio, and the cement-paste content. The paper also compares different methods of measuring permeability, and finds that a constant-head permeameter is more accurate than a falling-head permeameter. Additionally, the study investigates the clogging of pervious concrete and strategies to prevent it. The results of this research aim to improve the design of pervious concrete for use in the urban built environment. Highlights This paper presents the composition and method of formulation of pervious concrete based on cement and natural aggregates. Factors affecting the porosity of pervious concrete are identified. Correlations are established between the permeability and open porosity of pervious concrete. Factors influencing the permeability of pervious concrete are studied. Methods of measuring permeability are presented, and the results obtained with these different methods are compared. Different models for predicting the permeability of pervious concrete are presented.

Performance studies on quaternary blended Geopolymer concrete

This experimental work studied the mix design, mechanical properties and durability properties for G40 and G60 Quaternary Blended Geopolymer Concrete. Quaternary means fourth in sequence; hence Geopolymer Concrete is composed of four binder materials: fly ash, ground granulated blast furnace slag (GGBS), rice husk ash, and Metakaolin. For the Binary mixed geopolymer mix, the ratio of flyash to ground granulated blast furnace slag was 70:30 for G40 grades and 60:40 for G60 rates. For the Ternary geopolymer mixture, Flyash: Ground granulated blast furnace slag: Metakaolinratio was taken as 50:30:20 and 40:40:20 for G40 and G60 grades, respectively. For quaternary blended geopolymer mix, Fly ash: Ground granulated blast furnace slag: Metakaolin: Ricehuskash ratio was taken as 30:30:20:20 and 20:40:20:20 for G40 and G60 grades, respectively. As an alkaline solution, sodium with silicate and hydroxide actuated the binder components. The ratio of alkaline activator (NaOH/Na2SiO3) was set at 2.5, and the concentration of NaOH was kept at eight molalities. The total ratio was calculated to be 5.18 for G40 and 4.18 for G60 grades. In this experimental investigation, flexural strength, split tensile strength tests, compressive strength (CS), Rapid Chloride Penetration Test (RCPT), sorptivity, and chloride permeability, were conducted. On geopolymer mortar, tests for Normal Consistency (mm), Initial and Final Setting Time (min), and CS(MPa) were undertaken. Results had shown that ternary blended geopolymer mortar had optimum values in CS. Quaternary composite geopolymer mortar had shown low CS. In quaternary blended geopolymer mortar, Initial and Final Setting Times were delayed. It had not performed well in the flexural strength test, compression strength test and split tensile strength test. It also showed low resistance for sorptivity, Rapid chloride permeability, and acid attack when compared with ternary and binary blended Geopolymer Concrete. Ternary mixed Geopolymer Concrete had optimal results in CS, flexural strength, and split tensile strength tests, as well as superior sorptivity, fast chloride permeability and acid attack resistance.

Case study on performance of pumping concrete with super-fine river-sand and manufactured-sand

Natural ecological protection changes the traditional practice for concrete production which faces a situation of the source exhaustion of natural materials, and the sufficient utilization of waste local materials becomes a strategy to reduce the carbon emission in infrastructure construction. This promotes a possible economic production of concrete with mixed-sand composited by coarse manufactured-sand and super-fine river-sand. Due to lack of study on the mixed-sand used for pumping concrete, an experimental study was carried out in this paper. The proportion of super-fine river-sand and manufactured-sand was determined at a target fineness modulus with a gradation in zone-II, the sand ratio of concrete was determined by the closed packing test of aggregates. Twelve mix proportions of mixed-sand concrete were designed by the absolute volume method, considering the content of super-fine river-sand and the sand ratio. The target cubic compressive strength of mixed-sand concrete was 48.2 MPa, while the target pumpability aimed at pumping height of 300 m. The pumpability of fresh mixed-sand concrete was evaluated by multi-indices including slump, slump flow, static segregation rate, pressure bleeding rate and air content, and explained by the rheological property including the indices of yield stress and plastic viscosity. The mechanical property was evaluated by the cubic and axial compressive strengths, the modulus of elasticity and the splitting tensile strength at curing age from 7 days to 56 days. Based on the test results, the absolute volume method for mix proportion design of mixed-sand concrete is rational to keep a consistent unit weight of fresh concrete, the optimal content of super-fine river-sand in mixed-sand is 25%− 35% with a rational sand ratio of 45%− 51%. The developments of compressive strength and tensile strength of mixed-sand concrete are identical to those of conventional concrete. The mixed-sand concrete presents the same relationships to the conventional concrete for the axial compressive strength, the splitting tensile strength and the modulus of elasticity with the cubic compressive strength. However, the splitting tensile strength of mixed-sand concrete is about 90% that of conventional concrete at the same strength grade. Further studies can be conducted to improve the flowability by adjusting superplasticizer and to enhance the tensile strength of mixed-sand concrete.