Mix Proportions Research Articles

Performance Evaluation and Microstructure Study of Pervious Concrete Prepared from Various Solid Waste Admixtures

Solid waste materials (SWM) are commonly used in the preparation of building materials due to their structural characteristics and chemical composition. Pervious concrete (PC) is a green infrastructure material that offers advantages such as reducing surface runoff and purifying water quality, making it an important component of sponge cities. This study aims to investigate the physical properties and micro-structure of PC prepared from various SWM and determine the optimal mix proportion. In this study, three common SWM, including muck, steel slag (SS) and fly ash (FA), are used as raw materials. The chemical composition and physical properties of SWM are analyzed. A five-level and five-factor test scheme is developed using the orthogonal test method. This scheme considers the target porosity, water–cement ratio, muck content, SS content, and FA content as variables. The mechanical properties and permeability of PC, including compressive strength, porosity and permeability coefficient are evaluated. The internal structure of PC is observed using a scanning electron microscope (SEM). The results indicate that the optimal mix proportion for preparing PC is determined through efficiency coefficient method analysis: target porosity of 25%, water–cement ratio of 0.36, muck content of 10%, SS content of 10%, and FA content of 12.5%. The corresponding performance indicators of the PC sample are measured as follows: porosity of 24.67%, compressive strength of 15.78 MPa, and permeability coefficient of 2.23 mm/s. This study provides valuable insights for the rapid and flexible batching and performance optimization research of PC based on SWM.

Optimizing mix proportioning of high-performance concrete using genetic algorithm

Optimizing mix proportioning of high-performance concrete using genetic algorithm

Optimizing concrete mix proportions with zeolite, GGBS, and CDW: a data-driven approach integrating experimental analysis and machine learning models

Abstract The depletion of natural sand resources and the environmental impact of cement production necessitate sustainable alternatives in concrete manufacturing. This study evaluates the potential of zeolite, ground granulated blast-furnace slag (GGBS), and construction and demolition waste (CDW) as partial replacements for sand in concrete mix proportions. Experimental investigations revealed that the optimal mix proportion, identified as Mix Batch M4 (60% Sand, 20% Zeolite, 10% GGBS, and 10% CDW), achieved a compressive strength (CS) of 67.37 MPa, flexural strength (FS) of 6.80 MPa, split tensile strength (ST) of 5.61 MPa, and notable reductions in water absorption (WA) to 4.00% and drying shrinkage (DS) to 4.02%. Additionally, durability improvements included a 30% reduction in rapid chloride permeability and enhanced ultrasonic pulse velocity (UPV) and rebound hammer (RH) values. Advanced machine learning models were utilized to analyze and optimize the mix designs, integrating the Sparrow Search Algorithm (SSA) with models such as Extreme Gradient Boosting (XGB), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and LightGBM. The XGB model demonstrated the highest predictive accuracy with an R2 of 1.000. Multi-objective optimization techniques, including Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Genetic Algorithm with Fuzzy models, were employed to refine mix proportions further, balancing mechanical properties, material sustainability, and environmental benefits. This study highlights significant reductions in natural sand consumption and waste generation while enhancing concrete performance. Practical implications include reduced environmental impact, improved resource efficiency, and the promotion of circular economy principles. These findings provide a pathway toward innovative and sustainable concrete solutions, aligning with global sustainability goals in the construction industry.

An Experimental Study on Foam Concrete By Partial Replacement of Cement using Rice Husk Ash and Silica Fumes

A type of lightweight aerated concrete called foam concrete lacks coarse particles, making it comparable to aerated mortar. Foam concrete is created when slurry and pre-formed foam are combined. The foam's job is to create air pockets in the slurry made of cement. The foam is made separately with a foam generator by diluting the foaming ingredient with water and aerating it. The slurry forms a shell surrounding the bubbles of foam, and as the foam begins to break down, the paste's strength keeps it in place around the air holes. The ratio of water to cement (w/c) typically varies from 0.4 to 1.25. As the quantity decreases, the foam concrete mixture becomes excessively rigid, which breaks the bubbles, However, when the combination's water content increases, it becomes too thin to support the bubbles, which causes the bubbles to separate from the mixture. You can make foam concrete with any dry density between 300 and 1850 kg/m3. In this experiment, there are two foam concrete mixtures created using silica fumes and rice husk ash, and efforts have been produced to choose the foam concrete mix's proportions. A collection of 36 cube specimens is created and subjected to mixture testing. Following this, their actual state (density) and particular structural (compressive strength) qualities are examined, with the findings being reported. Important terms: Foam Concrete, Foaming Agent, Rice Husk Ash, Silica Fumes, Density.

Modeling Mechanical Properties of Concrete with Bida Natural Stones as Coarse Aggregate

This paper presents the results of statistical models for the mechanical properties of concrete made using Bida natural stones (pebbles) as coarse aggregate. Fresh and hardened concrete properties were analyzed using response surface methodology (RSM). The research employed central composite design using three (3) independent variables with equivalent ranges as Water to Cement ratio (W/C) = 0.4, 0.5, 0.6, Coarse Aggregate to Total Aggregate ratio (CA/TA) = 0.55, 0.615, 0.68 and Total Aggregate to Cement ratio (TA/C) = 3.0, 4.5 and 6.0. Minitab 14 (2004) selected 20 possible mix proportions for the experiment. The concrete specimens that were used for this research work include 150mm x 150mm x 150mm concrete cube, Ø150mm x 300mm concrete cylinder and 100mm x 100mm x 500mm concrete prism for compressive strength, elastic modulus/splitting tensile strength and flexural strength respectively. For each mix, five numbers of specimens were cast and cured for 7, 14, 21 and 28days. The twenty-eight days measured responses were used to develop models for the mechanical properties. Statistical and experimental model validations were employed. The p-value for all individual terms were less than 0.05, p-values for lack of fit were also greater than 0.05 in all cases tested, coefficient of determination (R2), adjusted coefficient of determination (adjR2) were in the range of 89.7% to 100% for compressive strength, splitting tensile strength, elastic modulus, flexural strength, and slump respectively which indicated adequate model validity. The predicted responses closely agree with experimental values, this further confirms the validity of the models developed. The study therefore suggested interaction, pure quadratic, reduced full quadratic, interaction models for the mechanical properties and slump respectively for the concrete made using Bida natural stones as coarse aggregate. The concrete with mix constituent mentioned above can be used for structural application such as reinforced concrete slabs, beams, columns and foundations.

Enhancement of Air-Entrained Grout-Enriched Vibrated Cemented Sand, Gravel and Rock (GECSGR) for Improving Frost and Thawing Resistance in CSGR Dams.

Cemented Sand, Gravel, and Rock (CSGR) dams have traditionally used either Conventional Vibrated Concrete (CVC) or Grout-Enriched Roller Compacted Concrete (GERCC) for protective and seepage control layers in low- to medium-height dams. However, these methods are complex, prone to interference, and uneconomical due to significant differences in the expansion coefficient, elastic modulus, and hydration heat parameters among CSGR, CVC, and GERCC. This complexity complicates quality control during construction, leading to the development of Grout-Enriched Vibrated Cemented Sand, Gravel, and Rock (GECSGR) as an alternative. Despite its potential, GECSGR has limited use due to concerns about freeze-thaw resistance. This project addresses these concerns by developing an air-entrained GECSGR grout formulation and construction technique. The study follows a five-phase approach: mix proportioning of C1806 CSGR; optimization of the grout formulation; determination of grout addition rate; evaluation of small-scale lab samples of GECSGR; and field application. The results indicate that combining 8-12% of 223 kg/m3 cement grout with 2-2.23 kg/m3 of admixtures, mud content of 15%, a marsh time of 26-31 s. and a water/cement ratio of 0.5-0.6 with the C1806 parent CSGR mixture achieved a post-vibration in situ air content of 4-6%, excellent freeze-thaw resistance (F300: mass loss <5% or initial dynamic modulus ≥60%), and permeability resistance (W12: permeability coefficient of 0.13 × 10-10 m/s). The development of a 2-in-1 slurry addition and vibration equipment eliminated performance risks and enhanced efficiency in field applications, such as the conversion of the C1804 CSGR mixture into air-entrained GECSGR grade C9015W6F50 for the 2.76 km Qianwei protection dam. Economic analysis revealed that the unit cost of GECSGR production is 18.3% and 6.33% less than CVC and GERCC, respectively, marking a significant advancement in sustainable cement-based composite materials in the dam industry.

Prediction of Compressive Strength of Fly Ash-Recycled Mortar Based on Grey Wolf Optimizer-Backpropagation Neural Network.

The evaluation of the mechanical performance of fly ash-recycled mortar (FARM) is a necessary condition to ensure the efficient utilization of recycled fine aggregates. This article describes the design of nine mix proportions of FARMs with a low water/cement ratio and screens six mix proportions with reasonable flowability. The compressive strengths of FARMs were tested, and the influence of the water/cement ratio (w/c) and age on the compressive strength was analyzed. Meanwhile, a backpropagation neural network (BPNN) model optimized by the grey wolf optimizer (GWO), namely the GWO-BPNN model, was established to predict the compressive strength of FARM. The input layer of the model consisted of w/c, a cement/sand ratio, water reducer, age, and fly ash content, while the output layer was the compressive strength. The data set consisted of 150 sets from this article and existing research in the literature, of which 70% is used for model training and 30% for model validation. The results show that compared with the traditional BPNN, the coefficient of determination (R2) of GWO-BPNN increases from 0.85 to 0.93, and the mean squared error (MSE) of model training decreases from 0.018 to 0.015. Meanwhile, the convergence iterations of model validation decrease from 108 to 65. This indicates that GWO improved the prediction accuracy and computational efficiency of BPNN. The model results of characteristic heat, kernel density estimation, scatter matrix, and the SHAP value all indicated that the w/c was strongly negatively correlated with compressive strength, while the sand/cement ratio and age were strongly positively correlated with compressive strength. However, the relationship between the contents of fly ash, the water reducer, and the compressive strength was not obvious.

A data-driven method for the design and calculation of recycled concrete mix proportions based on carbon footprint

A data-driven method for the design and calculation of recycled concrete mix proportions based on carbon footprint

Preparation of vegetative concrete with recycled aggregate: Mix proportion orthogonal test, curing methods, and microstructure

Preparation of vegetative concrete with recycled aggregate: Mix proportion orthogonal test, curing methods, and microstructure

Producing 100+ MPa Field Concrete in Developing Countries: Requirements and Challenges

Over the past three decades, there has been a paradigm shift in the concrete industry in which high strength and high performance concrete became more widely in use. However, producing ultra-high strength concrete surpassing 100 MPa compressive strength in the field remains a challenging task. This is primarily due to the various factors involved in such concrete and its sensitivity to many of these factors. This study aims at producing field concrete surpassing 100 MPa compressive strength using readily available materials worldwide. The study also addresses the requirements and challenges of 100+ MPa concrete in the field in order to possess similar properties to conjugate mixtures produced in the laboratory having same mix proportions. Concrete mixtures were prepared with different water-to-cement ratios and incorporated variety of chemical and mineral admixtures. Tests included fresh concrete, self-consolidation as well as hardened concrete properties in order to determine the properties of the concrete produced. The impact of other vital factors such as mixing process, ambient temperature, curing process and pumping are addressed taking field conditions into consideration. Several field visits were conducted to monitor field concrete that was produced using the designated mixtures. The study herein revealed that reaching 100 plus MPa concrete is doable using variety of readily-available constituents and mix proportion. However, the study pinpoints the importance of other crucial factors and field practices. Recommendations are provided to concrete users and practitioners to exercise better quality control and ensure high rate of success in producing ultra-high strength concrete in the field.

Engineering of alkali-activated permeable pavement composites with agro-industrial wastes

ABSTRACT This study investigates the development of alkali-activated pervious concrete (PC) composites using industrial and agro-waste as primary constituents. The binder was activated with sodium silicate and sodium hydroxide at an activator modulus (Ms) of 1.25, maintaining a sodium oxide dosage of 4.0% relative to the total binder at a water-to-binder ratio of 0.40. Slag and bagasse ash were used as binders, recycled coarse aggregates (from construction and demolition waste) partially replaced crushed granite and waste-foundry sand served as fine aggregates. Mix proportions were optimised for hydraulic conductivity and compressive strength after 28 days of air curing. Four selected mixes were further evaluated for total porosity, splitting-tensile strength, static flexural strength and flexural fatigue performance. Optimal mechanical properties were observed for the corresponding PC mixes at 10% bagasse ash and 50% recycled aggregates. Statistical analyses of flexural fatigue, including S-N curves and two-parameter Weibull distribution, were conducted, along with Kolmogorov-Smirnov analysis and survival probabilities (95%, 50% and 5%). The findings contribute to understanding the engineering behaviour of alkali-activated PCs, supporting the development of permeable concrete pavement systems through effective utilisation of agro-industrial wastes, thereby promoting sustainability and environmental conservation.

The design and performance evaluation of low-noise rubber-fibre micro-surfacing pavement

ABSTRACT Owing to its good performance and quick construction, micro-surfacing is frequently employed in the prevention and treatment of pavement diseases. However, the shortcomings of the current design methods and the noise problems have not been resolved. Two ways of optimising gradation and adding additives were used to reduce the noise and enhance the performance of micro-surfacing. The gradation of the micro-surfacing was investigated based on fractal theory and the optimum gradation range was determined. The results showed that adding more fibre to micro-surfacing improves its resistance to rutting, wear, water damage and cracking. The anti-skid performance of rubber powder-fibre micro-surfacing (RFMS) falls as the asphalt aggregate ratio rises, but wear resistance and water damage resistance rise and rutting resistance and fracture resistance increase initially before declining. The best mix proportion of RFMS is determined based on the overall mass of aggregate and each component is as follows: 1.5% cement (PO42.5), 3% rubber powder (40 mesh) and 0.2–0.25% polypropylene monofilament fibre (6 mm). RFMS can reduce pavement noise while maintaining good anti-skid performance. The study is an important supplement to the research on micro-surfacing and its noise reduction.

Prediction of Constituents of Concrete Mixtures Containing Fly Ash and Blast Furnace Slag Using Machine Learning Techniques

The prediction of concrete mix proportions is of the utmost importance to civil engineers to complete the design process of structures. This process is usually done through a trial-and-error process which involves simple regression techniques and is usually done to achieve a specific strength at a specific age. The incorporation of supplementary cementitious materials into concrete mixtures for environmental purposes has deemed the prediction process more complex and created a need to come up with more advanced techniques. Furthermore, the ability to predict the constituents of concrete mixtures given multiple inputs is still limited. Hence, in this work several machine learning algorithms were utilized to make a prediction regarding mix proportions of concrete mixtures based on concrete compressive strength, concrete age, and density as inputs. Random forest, decision tree, and K-neighbors regressors were used to achieve this objective. Mean squared error as well as root squared error were used to measure the accuracy of the constructed models. Random Forest algorithm obtained the highest accuracy with 98.5%.

Experimental study of mono and hybrid nanofluids on square pyramid solar still.

The lower productivity of square pyramid solar still is the prime impediment to its worldwide applicability. In the present study, efforts have been made to improve the productivity of square pyramid solar still using mono and hybrid nanofluid. The experiments were carried out with two similar square pyramid solar stills at a 1cm depth of basin fluid (saline water, mono, and hybrid nanofluid) under the climate of location (20.61°N 72.91°E). Copper (Cu) and aluminum oxide (Al2O3) nanoparticles were chosen to prepare mono and hybrid nanofluid. The 0.1% weight concentration of mono and hybrid nanofluid was prepared for experimentation. The hybrid nanofluid was prepared for the different mixing proportions of Al2O3-Cu such as 80:20, 60:40, 50:50, 40:60, and 20:80. For the case of use of mono-nanofluid, the performance of square pyramid solar still with Cu nanofluid was higher than Al2O3 nanofluid. The total productivity of 1640 mL/m2 and diurnal average efficiency of 17.00% were achieved for square pyramid solar still with Cu mono-nanofluid than saline water. A higher distillate yield was achieved for the square pyramid solar still with hybrid nanofluid than the mono-nanofluid and saline water. The diurnal average efficiency of 34.50% and maximum distillate output of 2644 mL/m2 were obtained for the square pyramid solar still with Al2O3-Cu hybrid nanofluid for the mixing ratio of 20:80. The efficiency and distillate output were higher by 27.41% and 25.19% for the square pyramid solar still with Al2O3-Cu (20:80) hybrid nanofluid than the square pyramid solar still with saline water. The distillate yield decreases with a decrease in the proportion of Cu nanoparticles.

Enhanced Gaussian Process Model for Predicting Compressive Strength of Ultra-High-Performance Concrete (UHPC).

Ultra-high-performance concrete (UHPC) is widely used in engineering due to its exceptional mechanical properties, particularly compressive strength. Accurate prediction of the compressive strength is critical for optimizing mix proportions but remains challenging due to data dispersion, limited data availability, and complex material interactions. This study enhances the Gaussian Process (GP) model to address these challenges by incorporating Singular Value Decomposition (SVD) and Kalman Filtering and Smoothing (KF/KS). SVD improves data quality by extracting critical features, while KF/KS reduces data dispersion and align prediction with physical laws. The enhanced GP model predicts compressive strength with improved accuracy and quantifies uncertainty, offering significant advantages over traditional methods. The results demonstrate that the enhanced GP model outperforms other models, including artificial neural networks (ANN) and regression models, in terms of reliability and interpretability. This approach provides a robust tool for optimizing UHPC mix designs, reducing experimental costs, and ensuring structural performance.

Power analysis for zero-inflated Poisson and negative binomial generalized linear models using Monte Carlo simulation

Many research fields involve count data with zero inflation. A commonly chosen model for analysing a relationship between predictors and a response variable in these scenarios is a zero-inflated generalized linear model (GLM). This model is a mixture of a count-based GLM and a zero-inflation component, with a mixing proportion that determines the amount of excess zeroes. As the use of zero-inflated count models is rising, it is important to be able to conduct a power analysis to properly design studies with such models. In this paper, we propose a flexible method for power analysis with zero-inflated count models using Monte Carlo simulation. We have created the R package ZIPowerAnalysis, which can be used to easily conduct a power analysis for any designed study that will incorporate a zero-inflated count GLM.

Erratum: Prediction and analysis of strength and economic feasibility of filling materials under the influence of mix proportion and curing age

Erratum: Prediction and analysis of strength and economic feasibility of filling materials under the influence of mix proportion and curing age

Advanced modeling of compressive strength in silica-modified oil well cement: impact of silicate, aluminate, and hydraulic moduli

ABSTRACT This study explores the effect of cement chemical composition on the compressive strength (CS) of pure cement, cement modified with Nano Silica (NS), and Silica Fume (SF) across various mix proportions, curing times, and temperatures. Three new parameters, Silicate Modulus (SM), Aluminate Modulus (AM), and Hydraulic Modulus (HM), were introduced to evaluate cement composition’s impact on CS. SM combines SiO₂, Al₂O₃, and Fe₂O₃; AM integrates Al₂O₃ and Fe₂O₃; and HM includes CaO, SiO₂, Al₂O₃, and Fe₂O₃. Using 355 datasets, two mathematical models, Cubic (CUB) and linear and inverse (L&I), predicted CS, considering factors like cement composition, water-to-binder ratio, NS and SF content, curing time, and temperature. Statistical tools such as R2, RMSE, MAE, and SI validated model accuracy. The study assessed three scenarios: (1) multi-variate analysis of individual cement oxides; (2) bi-variate analysis using SM and AM, and (3) mono-variate analysis using HM. Introducing SM, AM, and HM improved model accuracy and reduced input parameters. Optimal NS and SF replacement percentages were 4.64% and 14.6%, respectively. SM, AM, and HM optimal values were 2.62, 1.38, and 2.21. Although initial costs increase with these replacements, they offer long-term benefits and align with sustainable construction practices.

Alkaleri Metakaolin Powder Performance Evaluation by Response Surface Method.

Alkaleri metakaolin (AMK) based geopolymer binder has been identified as one of the alternatives to the Ordinary Portland Cement (OPC), which qualifies the criteria for green construction material. In this work, 20No of experimental mix proportions (RUNs) were designed by Central Composite Design (CDD) of Response Surface Method (RSM). Mixture of AMK and Alkaline Solution was heated for 45mins at 60oc with stirring interval of 15mins, after which aggregates were added to the hot mix. 60No of 100X100X100mm cubes were cast. Samples were tested at the 28th day of ambient curing. The reported Compressive Strengths are the average of the 60 sample tested with 38.6N/mm2 as the maximum, hence established the experimental optimum mix proportion (EOMP). The ANOVA model developed statistically validated by having F-value of 15.54 and P-value of less than 0.0001 indicating that the model is statistically significant. Also, the adequate precision of 10.7850 which is greater than 4.00 indicates adequate signal and desirable. Furthermore, the difference between predicted R2 and adjusted R2 is less than 0.2 testifying that they are rationally in good agreement. Effects of three principal variables namely: molarity of sodium hydroxide, sodium silicate to sodium hydroxide ratio, and fineness (particle size) on the properties of the fresh and hardened AMK geopolymer concrete (normal consistency, soundness, setting times, slump, dry density, water absorption, compressive strength, flexural strength, split tensile strength, sorptivity and acid attack) were investigated. All the results for the properties investigated were found to have fallen within the ranges of the international standard codes such as ASTM and British Standard, hence AMK is qualify to be used as Metakaolin based geopolymer binder.

From waste to sustainable production: Experimental design and optimization of sustainable concrete using response surface methodology and life cycle assessment

This study focuses on a holistic approach to investigate the engineering properties (microstructural, mechanical, environmental, and economic) of sustainable concretes produced using recycled aggregate and supplementary cementitious material (SCM). Compressive strength tests was conducted on 24 sustainable concrete series for the mechanical properties. Statistical analyses were performed using the response surface methodology, and mathematical models were developed considering the strength values obtained. Developed model-based optimization solutions were applied to determine the mix proportions of the optimization series containing lower cement dosage and SCM with the same hardened concrete properties as the reference concrete series. Considering these mix proportions, life cycle assessments were performed and environmental and economic properties were compared. A comparison of Ref-6 and Opt-6 series reveals that the global warming potential (GWP), energy consumption (EC), and waste generation (WG) values are 422.4-330.9 kg CO2 eq, 1294-1044 MJ, and 1294-1222 kg, respectively. This situation resulted in a reduction of 21.7% in GWP values, 19.3% in EC values, and 5.6% in WG values. Similarly, the economic comparison of these series indicates that the total cost values are 81.9-75.7 €, respectively, resulting in 7.7% reduction. Finally, Scanning Electron Microscopy analyses were conducted to examine the microstructural properties. The use of fly ash at 10% improved the microstructure properties and increased the C-S-H gel formation. It is thought that the process of transition from these waste materials to sustainable production is of critical importance, especially in places that are heavily damaged after devastating earthquakes and where large amounts of waste materials are generated.