Optimum Mix Design Research Articles

Silicomanganese fume-based alkali-activated mortar: experimental, statistical, and environmental impact studies.

This paper evaluates the flowability and strength properties of alkali-activated mortar produced using silicomanganese fume (SiMnF) as the sole binder, combined with alkaline activators and sand, cured at room temperature (23 ± 1 °C). A total of 18 mixes were prepared by varying binder content (370, 470, and 570 kg/m3), alkaline activator content (33, 43, and 53% of binder by weight), and NaOH concentration (8 M and 12 M). The SiMnF-based alkali-activated pastes were characterized using SEM, XRD, and FTIR techniques to study morphology, mineral composition, and functional groups, respectively. Statistical modeling, including analysis of variance (ANOVA) and response surface method (RSM), was performed to optimize the mixes, and a life cycle assessment was conducted to evaluate the environmental impact of the developed SiMnF-based alkali-activated mortars (SiMnF-AAM). The experimental results showed that an optimal mix design with 470kg/m3 SiMnF, 43% alkaline activator content, and NaOH concentrations of 8M and 12M achieved the best balance of flow and strength. XRD and FTIR analyses confirmed that Nchwaningite was the primary reaction product, with secondary phases including magnetite, manganese ferrite, and potassium feldspar, influenced by alkali concentration. The SiMnF-based mixtures had a significantly lower CO₂ footprint (0.08kg CO₂/kg) compared to the cement-based mix, with alkali activators being the primary contributors to emissions. The developed SiMnF-AAM mixes, cured at room temperature, exhibited improved workability, mechanical properties, and reduced environmental impact, making them adaptive to real-life applications.

Fracture mechanism and ductility performances of fiber reinforced shotcrete under flexural loading insights from digital image correlation (DIC)

This paper investigates the fracture mechanism and ductility performance of fiber-reinforced shotcrete (FRS) under flexural loading through digital image correlation (DIC) analysis. The focus is on determining the optimal mix design, utilizing recycled and manufactured fibers in shotcrete through four-point bending tests. These tests reveal significant improvements in flexural strength and ductility, as well as crack resistance, attributed to the synergistic effect of both types of fiber in hybrid fiber reinforcement. Notably, the inclusion of recycled fibers from automobile tires enhances mechanical characteristics and impact resistance, contributing to environmental sustainability and cost reduction. DIC analysis offers crucial insights into crack initiation and propagation in shotcrete, highlighting the impact of fiber reinforcement on crack patterns. Manufactured fibers delay crack onset effectively, while hybridization enhances fracture characteristics, offering improved crack control and flexural strength. The study underscores the potential of hybrid fiber mixes for enhancing structural performance in tunnel support applications, emphasizing the synergistic effect of hybrid of different fiber types. Overall, the research contributes to advancing understanding of fracture behavior in fiber-reinforced shotcrete and provides practical insights for optimizing mix designs to achieve superior mechanical properties and durability.

Influence of sustainable waste granite, marble and nano-alumina additives on ordinary concretes: a physical, structural, and radiological study

This study investigates the individual and combined effects of enhancing the radiation shielding properties of waste concrete using the optimal mix design of two waste material powders of different compositions. Marble (MD) and granite (GD) waste dust were individually utilized as partial replacements for cement at a replacement ratio of 6%. Furthermore, two additional mixes were prepared by incorporating 1% by cement weight of nano alumina (NA) to enhance the microstructure of the studied waste concrete. The MGA-concrete was analyzed using X-ray Fluorescence, Energy dispersive X-ray, X-ray diffraction analysis, transmission electron microscopy, and scanning electron microscope techniques. The radiation shielding assets of the examined Concrete samples, such as the linear attenuation coefficient (μ), half value layer (H1/2), tenth value layer (T1/10), and fast neutron removal cross-section were evaluated using the MCS5 Monte Carlo simulation algorithm and Phy-X software. The results showed that the linear attenuation for the GMN-concretes’ order is CO < MD < GD < NA < MD + NA < GD + NA. The GD + Na concrete sample presents the best neutron performance. The studied GMN-concrete samples provide the best protection against γ-rays and fast neutrons. Lastly, the excellent performance of the mixes of waste Granite, Marble, and Nano-Alumina on ordinary would pave the way for their employment as radiation shielding in various nuclear and medical facilities.

Active Control of Properties of Fresh and Hardening Concrete

Concrete mixtures have an optimized mix design in view of attaining desired properties. However, after mixing, during further processing, it is typically not possible to further adjust the performance of the fresh and hardening concrete. A new and emerging approach is to actively control the concrete properties by means of responsive particles or polymers triggered by an externally applied signal. Active control of properties of concrete refers to the concept of on- demand changes of one or more properties of the concrete after mixing by triggering a response to one or more of the constituents using a specific trigger signal (e.g. thermal, chemical, electrical, magnetic...). The on-demand control of properties can focus on the processing stage (including e.g. pumping, casting, 3D printing), the curing and hardening stage (including e.g. control of capillary pressure, shrinkage, setting, and hardening) and even on the hardened stage during service life (e.g. active corrosion control, active crack healing...). Addressing specific obstacles in cementitious environments, ensuring responsive material stability, controlling signal applicability, cost, logistics, and on-site safety is crucial for successful implementation. A RILEM technical committee has been initiated in 2023, working on the concept of Active Control of Properties of Concrete (RILEM TC 317-ACP). The committee will focus on active control of properties of fresh and hardening concrete. This paper gives a short introduction to scope and activities of TC 317-ACP.

Optimized mix design method of ultra-high performance concrete (UHPC) and effect of high steel fiber content: Mechanical performance and shrinkage properties

In this study, UHPC was successfully prepared based on the Modified Andreason and Andersen (MAA) model (distribution modulus m= 0.23 was confirmed) and Excel solver tool. Subsequently, the effect of steel fiber content (0%–6% by volume) on the mechanical properties and shrinkage properties was investigated. It was found that the compressive and flexural strengths increased significantly with the increase of steel fiber content from 0 % to 5 %, but decreased when it exceeded 5 %. The split tensile strength was always increased. In addition, the plastic shrinkage, autogenous shrinkage, and drying shrinkage of UHPC were reduced by 18.3%–67.4 %. It indicated that incorporating higher content of steel fiber into UHPC could enhance mechanical strength, but the mixing approach should be optimized when content of steel fiber exceeds 5%. In addition, the prevalence of steel fiber agglomeration was shown under X-CT, but the agglomeration of steel fiber still led to an increase in UHPC properties within a certain range. The air content of UHPC with 6 % fiber content is 3.5 %, which shows the reasonableness and good performance of the mix proportion calculated by the Excel solver demonstrated in the article. The article can lay a light on the industrial design and preparation of UHPC.

Study on design optimization, road performance verification and preparation process selection of hybrid fiber reinforced asphalt mixture composite

To achieve long-lasting, high-strength, and sustainable asphalt pavement, this study prepared a hybrid fiber asphalt mixture using micron-scale calcium sulfate whiskers (CSW) and millimeter-scale basalt fibers (BF). The response surface method was employed to optimize the mix design, develop a prediction model, and verify road performance. The preparation process was refined based on the optimal mix design. The findings revealed that the best overall road performance was obtained with 5 % CSW content, 6 mm BF length, and 0.32 % BF content. The mixture demonstrated optimal performance with CSW wet mixing and BF dry mixing. Additionally, testing the fiber asphalt mixture at the mixture level was found to be crucial, as results can vary from those obtained at the mastic level. This study provides an in-depth analysis of the hybrid fiber asphalt mixture’s action mechanisms in material optimization, performance verification, and preparation processes, offering valuable insights for related research and engineering applications of hybrid fibers.

Multi-objective optimization of seawater mixing in cement-based materials with supplementary cementitious materials using response surface methodology

Seawater instead of freshwater to produce concrete is an effective measure to save freshwater resources. However, the presence of harmful ions in seawater restricts its broader application. The incorporation of supplementary cementitious materials (SCMs) has the potential to significantly mitigate these adverse effects. Consequently, there is a growing interest in understanding the mechanisms by which SCMs function in seawater-mixed cementitious systems. In this study, a quadratic polynomial prediction model with the mechanical properties and dry shrinkage of the composite matrix as the response target was analyzed by the response surface method (RSM). Tests and analytical techniques elucidated the effects of metakaolin (MK) and ground granulated blast-furnace slag (GGBS) on the hydration process of the pastes after seawater mixing. The results revealed that the compressive strength and dry shrinkage were influenced by single-factor or multi-factor interactions. The optimal mix design (MK=20.2 % and GGBS=10.0 %) was experimentally validated, showing an absolute error of 1.5 %. The microstructural analysis indicated that MK and GGBS expedited essential pozzolanic reactions. Enhanced strength in mixtures before 3 d was attributed to cation exchange and initial flocculation. In the 28 d specimens, the increase in strength was related to the growth of Friedel’s salt crystals and C-(A)-S-H gels. Due to the presence of the C-(A)-S-H gels and AFm phases, chloride ions were efficiently immobilized, and the shrinkage of the matrix was reduced.

Characterization of Taguchi optimized fiber reinforced copper slag concrete composites with strength properties

Establishing the ideal design has always been a difficulty for concrete engineers because of the inclusion of many alternative construction materials particularly when dealing with the incorporation of slag elements and fibers in concrete mix design. In this study, the Taguchi optimization method is implemented to predict the optimal mix design of fiber reinforced copper slag aggregate concrete (FRCSC) with mechanical strength properties. For this purpose, different parameters influencing mechanical strength were defined based on the available literature. The Taguchi approach was subsequently applied to develop an orthogonal array of parameters, and experimental tests were carried out in accordance with the suggested design array to acquire experimental data for each and every strength property. Three major influencing parameters like fiber length (FL), fiber percentage (FP), and copper slag content (CS) were considered in order to get optimal mix design for FRCSC. Analysis of variance was also employed to evaluate the effective factors on compressive, split-tensile, flexural, water absorption and void content properties for FRCSC. It was found that the proposed method was effective in finding the optimal mixture design from strength point of FRCSC and the experimental results indicated a significant improvement in five selected strength properties of FRCSC. From the results of Taguchi analysis, it can be concluded that, for five selected strength properties, 60% CS + 1% of fiber + 20 mm of fiber length is the optimal mix design combination for FRCSC. Finally, physical and microstructural characterization through FESEM and EDX were carried out to have a closer justification for the test results.

Emerging eco-friendly fiber-reinforced concrete with shaped synthetic aggregates using Taguchi grey relational analysis and utility concept

The present study assessed the applicability of employing Taguchi-based Grey relational analysis (T-GRA) and the Taguchi approach combined with the utility concept (T-UC) for enhancing the mix design parameters of fiber-reinforced synthetic aggregate concrete (FRSAC). Cement was supplemented with binders such as ground granulated blast furnace slag (GGBFS), metakaolin (MK), and lime powder (LP). The optimized mix proportion of FRSAC was determined to enhance the fresh and mechanical performance. Furthermore, this investigation used two weighting approaches: equally weighted and maximum deviation methods. An established L16 (45) Taguchi orthogonal array was used with five different factors, namely, cement replacement (40 %, 50 %, 60 % and 70 %), binder content (B1, B2, B3, B4), water-to-cement ratio (0.31, 0.32, 0.33, 0.34), fiber dosage (0.1 %, 0.2 %, 0.3 % and 0.4 %), and synthetic aggregate replacement (25 %, 50 %, 75 %, and 100 %) were employed to design the experiments. Then, T-GRA and T-UC were used to determine the optimum mix proportions for five response parameters: workability, compressive strength, split tensile strength, elastic modulus, and impact ductility index. Results indicated that the two methods, GRA and UC, can be successfully integrated with the Taguchi method to derive the optimized FRSAC mixture. Confirmation experiments were performed after determining the optimum mix design parameters, including cement replacement of 60 %, binder content of B4, the water-to-cement ratio of 0.34, fiber dosage of 0.1 %, and synthetic aggregate replacement of 75 %. The optimum FRSAC mix was examined for its fresh (workability) and hardened (compressive strength, split tensile strength, elastic modulus, impact ductility index) properties.

Performance of Modified Polymer as a Binder with Qarry Dust and Ballast in the Production of Paving Blocks

The city of Nairobi is in need of pedestrian and cyclist infrastructure as an answer to an increasing reliance on automobiles. Collaborative efforts are underway to reduce congestion, although challenges such as sand mining persist. The utilization of plastic waste and Quarry Dust (QD) may provide a sustainable solution. The proposed study aims to assess the mechanical properties and optimal blend design for the production of paving blocks using a modified polymer and a combination of ballast (B) and QD. The gradation curve constituted a pivotal stage in the research process, as it provided vital information regarding the ratio of B and QD required for the optimal mix design. The study methodically explored the optimal ratio of materials for the production of paving blocks incorporating a modified polymer, QD, and B. It was observed that there were minor reductions in strength under acidic conditions, which suggests that the proposed paving blocks may be suitable for use in a variety of outdoor applications. Additionally, it would be advantageous to investigate innovative solutions for Non-Motorized Transport (NMT) infrastructure.

Ann Prediction of Mechanical Properties of GGBFS and Alccofine Based High Strenth Self-Compacting Concrete

Abstract In this study, we use Artificial Neural Networks (ANN) and Multiple Regression Analysis to evaluate the prediction of two crucial self-compacting concrete properties: compressive strength and split tensile strength. It was possible to create four different datasets, each of which had different concrete mix proportions along with their respective ages in days, compressive strengths (MPa), and split tensile strengths (MPa). Separate ANN models and Regression models were trained and tested using these datasets. As a gauge of prediction accuracy, Mean Squared Error (MSE) was used to assess the performance of the models. This study offers insightful information on the application of multiple regression analysis and artificial neural networks to forecast the characteristics of self-compacting concrete using GGBS and Alccofine. Here Alccofine functions as an additive and GGBS acts as a partial substitute for cement at 0 to 60% with a fluctuation of 10%. The outcomes highlight the potential of neural networks as a tool for concrete mix design optimization and quality control since they can capture complex correlations between input variables and concrete strength.

Taguchi-integrated grey relational analysis for multi-response optimization of mix design for alkali-activated concrete

Although alkali-activated concrete (AAC) has gained significant attention as a sustainable alternative to traditional Portland cement-based concrete due to its reduced carbon emissions and improved durability, optimizing AAC mix design still remains a challenging task as it involves complex interactions between various factors, constituent materials, and their proportions. This study presents the performance based multi-response optimization of alkali-activated concrete mix using Taguchi-integrated Grey Relational Analysis aiming for improved workability, mechanical and permeability properties. This study employs Taguchi’s L9 orthogonal array to reduce the experimental trials, thereby saving time and resources. The Grey Relational Analysis optimizes factors like binder ratio, solution to binder (Al/b) ratio, and molarity of NaOH and Na2SiO3/NaOH ratio, encompassing alkali-activated concrete’s fresh, mechanical, and durability characteristics with structural grade properties. The results show that binder ratio (FA:GGBS) of 90:10, Al/b ratio of 0.45, 10 M NaOH solution with Na2SiO3/NaOH ratio of 1 has produced the optimum alkali-activated concrete mix of structural (M40) grade.

Introducing a novel optimised binder activator with redcar mudstone feedstock in the UK for geopolymer cement formation

This paper evaluates Redcar Mudstone (RM), a type of shale rock, as a potential geopolymeric binder for cement production, highlighting its preparation through milling and calcination, followed by blending with an alkali activator. The study utilises an L8 orthogonal array matrix to determine the optimal mix design based on maximum compressive strength. Mechanical tests reveal that sodium-activated RM serves as a viable feedstock, producing cement comparable to or better than other mineral-based feedstocks, achieving a maximum mean compressive strength of 71.98 MPa. The mix optimisation process indicates the RM to alkali ratio and the sodium silicate to sodium hydroxide ratio as critical factors in developing this geopolymer cement. Results show a significant presence of aluminosilicates in their oxidised state, essential for the geopolymerisation process, aligning with all mineralogical analyses. The competitive results of this cement experimentation with Ordinary Portland Cement (OPC) and other geopolymers underscore its potential. This research underscores the environmental and strength performance advantages of RM derived geopolymers, suggesting their potential as an effective waste valorisation method and a green alternative in cement production, driven by the global push to reduce CO2 emissions and the extensive research on geopolymers and innovative binders.

Enhancing compressive strength and sustainability: a machine learning approach to recycled brick and tile concrete mix design

Optimizing concrete mix designs, especially for recycled brick and tile (RBT) concrete, is critical in construction materials. The optimization of RBT concrete mix designs was investigated using machine learning (ML) methods and metaheuristic algorithms. Hyperparameter tuning of the XGBoost (XGB) model was conducted using SFO, HGS, and HBA algorithms. Critical hyperparameters, including population size and iterations, were identified to ensure model convergence and performance. The model's predictive accuracy for compressive strength (CS) was evaluated using the coefficient of determination (R2) metric, revealing its superiority in mitigating overfitting. SHAP analysis highlighted testing age, cement content, and total coarse aggregate as influential input variables. A practical approach to RBT concrete mix design optimization was introduced, enabling the achievement of specific CS targets while maximizing RBT usage. Pareto solutions provided actionable insights for engineers to create tailored high-performance concrete mixes. This research contributes significantly to sustainable concrete mix design by offering practical ML tools.

Integrated use of Bayer red mud and electrolytic manganese residue in limestone calcined clay cement (LC3) via thermal treatment activation

Limestone calcined clay cement (LC3) is a crucial low-carbon alternative for the future of the cement industry. Bayer red mud (RM) and electrolytic manganese residue (EMR), byproducts of aluminum and manganese production, respectively, pose significant environmental challenges due to their high volume and hazardous components. Utilizing these materials in LC3 not only mitigates disposal issues but also leverages their rich content of Al2O3 and SiO2, which resemble the properties of calcined clay after thermal treatment. This study investigates the mixture of RM and EMR with cement clinker, limestone powder, and gypsum to create a cementitious material with low carbon emissions. It examines the impact of thermal treatment temperature on RM and EMR reactivity and determines the optimal mix design. The findings indicate that the ideal thermal treatment temperature is 600 °C, with a 1:1 RM to EMR ratio, achieving the best mechanical properties and synergistic effects. The compressive strength of the RM/EMR-based LC3 reached 43.4 MPa at 28 days with a water-to-cement ratio of 0.35. Microstructural analysis reveals that RM, EMR, and limestone powder react to form monocarboaluminate in a calcium hydroxide-induced alkaline environment. Active Al2O3 and SiO2 in the RM and calcium sulfate in the EMR facilitate a secondary hydration reaction. The cementitious material, prepared from thermally activated RM and EMR, can potentially reduce CO2 emissions by 26.2 % and energy consumption by 14.9 % per ton compared to conventional Portland cement. This study demonstrates a sustainable approach for the cement industry, offering a viable solution for waste management and carbon reduction.

Optimization of geopolymer pervious concrete design using multi-phase discrete element modeling

This study aims to optimize mix designs of geopolymer pervious concrete using multi-phase discrete element modeling. Metakaolin based geopolymer concrete was prepared with different mix designs for laboratory testing. A multi-phase discrete element model (DEM) for pervious concrete was developed with randomly generated porous structure and realistic contact models between each component. Contact model parameters were calibrated using orthogonal analysis based on measurements of mechanical strengths and elastic modulus. Results of model validation indicate that the developed numerical model has good reliability to predict mechanical properties of pervious concrete with the influence of porosity. Both actual and simulated pervious concrete specimens present consistent crack propagation patterns and failure behavior. The optimization of mixture design is conducted by considering aggregate gradation and aggregate to paste ratio of geopolymer pervious concrete, which are based on experimental results and post-calibration simulations. The gradation with larger size aggregate is suitable for preparing geopolymer pervious concrete with higher permeability and tensile strength. More importantly, it is feasible to reduce paste content in geopolymer pervious concrete while maintaining similar or even higher mechanical strengths, which reduces the embedded carbon footprint. The study findings demonstrate the beneficial use of numerical modeling to determine the optimized mix design of composite material such as geopolymer pervious concrete.

Characterization of porosities and optimization of mix design of pervious concrete using image analysis

Although pervious reduces surface runoff and replenishes groundwater resources, industrial applications are hindered by uncertainty in properties during mass productions. Most experimental studies correlated design parameters to performance parameters predominantly analysing porosity measured from water replacement method. This explains only a fraction of porosity and leads to inappropriate correlations between the influencing parameters. This study quantified porosity using a novel image analysis. 480 samples of pervious concrete were cast varying aggregate to cement ratio and compaction levels and performance parameters such as, wet-density, compressive strength, apparent, actual and effective porosities were assessed. The developed novel image analysis method facilitates accurate computation of porosity and actual, apparent and effective porosities was analysed quantified and optimised. An optimum A/C ratio was observed as 4.0 corresponding to 30 blows of compaction and a resultant actual porosity of approximately 0.2. Further the study establishes relationships between different types of porosities and their performance implications.

Machine learning guided iterative mix design of geopolymer concrete

Current mix design methods for Geopolymer concrete (GPC) require substantial efforts of trial-and-error experiments and is applicable only to those formulated by specific precursor materials. In this work, a machine learning (ML) guided mix design method for GPC is proposed, which can considerably reduce the experimental workload and be versatile with a broad range of precursor materials. First, a database for the slump and compressive strength of GPC was established, and ML prediction models were developed with these as objectives. Subsequently, the mix design for GPC was conducted using the particle swarm optimization (PSO) algorithm. The designed mixture proportions underwent experimental validation, and if targets were not met, results were then used for model iteration until desired performance targets were achieved. The results showcase the excellent performance of the established slump and compressive strength prediction models, with an accuracy and coefficient of determination of 94 % and 0.95, respectively. The developed ML guided model effectively generates concrete mix achieving compressive strengths of 20 MPa, 40 MPa, and 60 MPa, while concurrently satisfying slump requirements. The experimentally validated results for the three designated target strengths after iterations were 25.2 MPa, 43.8 MPa, and 66.6 MPa, respectively. These positive results emphasize the ability of the proposed optimized mix design method to meet both the strength and workability targets in GPC via a minimum number of trail experiments.

Mix design strategy and optimization considering characteristic evaluation of geopolymer concrete

In this study, a unique strategy for mix design of geopolymer concrete (GPC) was suggested by integrating fly ash, silica fume, and GGBFS as precursors with varied alkali activator molarities in both ambient and oven curing environments. GPC's crystalline and microscopic qualities were assessed by X-ray diffraction and scanning electron microscopy tests; while its' mechanical properties were assessed using compressive strength test (CST). The experimental results showed a rising trend between the alkali activator (NaOH) concentration and the compressive strength of the GPC. GGBFS precursor based GPC had the highest compressive strength of 87.53 MPa at 14 molarity of NaOH after oven curing at 60°C. Furthermore, Artificial Neural Network (ANN) and Response Surface Method (RSM) were used to evaluate significant correlation and interaction between different parameters considered in mix design. By incorporating six input variables from the experimental test data, namely total precursor content used, secondary precursor content used, curing temperature, number of curing days, total aggregate content, and molarity of NaOH used, and compressive strength as output/response variable. The R-value for ANN found to be 0.99, while the R2 value for the RSM model was 0.98, indicating a satisfactory fit having a good interaction and optimization of GPC parameters. The optimize values of different independent parameters evaluated can obtain maximum CST value of 111.104 MPa.

Optimization of concrete mix design for enhanced performance and durability: integrating chemical and physical properties of aggregates

Optimization of concrete mix design for enhanced performance and durability: integrating chemical and physical properties of aggregates