Concrete Mix Design Research Articles

Enhancing Quality Control in the Mix Design of High-Strength Concrete Using a Capacity-Based Approach

The mix design of concrete is an important aspect that affects its strength and durability. This paper aims to revisit the existing mix design method given in IS 10262:2019 through a capacity-based approach. The approach involves identifying the possible failure modes in concrete and eliminating the undesirable ones leading to significant reduction in dispersion. This is accomplished by utilizing coarse aggregates that meet a specific minimum strength requirement or threshold (e.g., ~ 77 MPa for M95 grade of concrete), which is determined through a priori estimating the cohesion and friction angle of the concrete. The methodology to estimate the cohesion and friction angle from a single unconfined compression test is proposed based on the Mohr–Coulomb theory and using the orientation of failure plane of fractured specimen as a supplemental information from the same experiment. This paper also offers a simple and approximate test procedure to estimate the aggregate's compressive strength (~ 106 MPa in this mix design) reasonably which is essential for the capacity-based mix design. An experimental programme is also carried out to design the concrete mix using the proposed capacity-based approach. The results indicate that M95 concrete is achieved with a low standard deviation and coefficient of variation (~ 3%), falling in class of excellent quality control as per ACI 214R-11. This quality control is crucial in seismic structural design as variations in concrete strength is likely to negate the underlying principle of strong column–weak beam philosophy resulting in the triggering of undesirable shear modes of failure.

What is the embodied CO2 cost of getting building design wrong?

Today, carbon and cost-efficient construction are well matched. However, in the future, as steel production is increasingly done from recycled scrap in electric arc furnaces (EAFs) and concrete mix design is improved, the current balance of CO[Formula: see text] impacts and costs can be altered. When this happens, structural designers need to update their design strategies, and incentives must be put in place to retain the alignment between environmental impact and cost. Here, I assess the potential of carbon taxation to improve the structural design. I also assess the discrepancy in embodied carbon outcomes if construction costs remain constant, but the embodied carbon of materials is varied. Finally, I look at the effect of an early-stage design tool, PANDA, on embodied carbon outcomes of real projects. I find that carbon taxes need to be extremely high to have an effect, and that this effect is limited to certain types of frames. Embodied carbon in construction can become disconnected from costs if the embodied carbon of materials varies heterogeneously. Finally, novel design tools can help designers substantially improve the embodied carbon of their design. This happens despite the absence of a significant monetary incentive to that effect.This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Utilization of waste phosphogypsum in high-strength geopolymer concrete: Performance optimization and mechanistic exploration

Phosphogypsum (PG) is an industrial waste produced during the manufacture of phosphoric acid. In this study, dihydrate phosphogypsum (DPG), ground granulated blast furnace slag (GGBFS), fly ash (FA), Portland cement clinker (PCC), sulfate alumina cement clinker (SACC), water reducer, sand, and stone were combined to create a phosphogypsum-based geopolymer (PBG) concrete. Concrete mix design was carried out based on the principle of maximum packing density, and the effects of water reducer type, sand ratio, water-to-binder ratio, and DPG content on PBG concrete properties were investigated. Results indicated that compared to SiKa ViscoCrete 540P (SVC 540P) water reducer, Melflux PLUS 1087L (MP1087L) significantly enhanced the performance of PBG concrete. With a water-to-binder ratio of 0.34, a sand ratio of 32.2 %, and a PG:GGBFS:FA ratio of 5:4:1, the 28-day unconfined compressive strength (UCS) of the concrete exceeded 60 MPa. The concrete primarily consisted of phases such as dihydrate gypsum (CaSO4·2H2O), ettringite (Ca6(Al(OH)6)2(SO4)3(H2O)26), polymer (-Si-O-Al-), quartz (SiO2), albite (Na(AlSi3O8)) and Muscovite ((K,Na)Al2(Si,Al)4O10(OH)2). Using two mild alkalis as activators, PBG concrete can avoid the efflorescence often associated with geopolymer preparation and can cure at 20 °C. PBG concrete offers high strength, ease of preparation, and environmental benefits, providing a new avenue for the reuse of PG.

Fresh Concrete Properties Required for Vertical Slip-form Concrete

Abstract: The guidelines available in the present concrete mix design standard IS-10262-2019 [2] are silent about the special mix proportion criteria for slip-form concrete. The slip-form concrete is subjected to dynamic loads in the fresh state due to the movement of the slip-form panel over the fresh concrete. In current practice, particularly in power plant industries where RCC chimneys are required, concrete mix design is often carried out by a third party based solely on hardened concrete design strength criteria. However, such proportioning guidelines as per IS-10262-2009 [2] are suitable only for static concrete pouring operations. For slip-form concrete, special criteria for fresh concrete is needed, like adequate shear strength of the cover concrete to be self-sustaining against the lifting friction of the slip-form panel during its movement, as well as to prevent surface problems in the RCC chimney shell during construction using the slip-form technique. In this research work, addressing real execution problems of RCC chimney shell surface issues, it has been concluded that the greater the shear strength of fresh concrete, the more resistant it will be to the lifting friction of the slip-form panel, resulting in better surface quality of the chimney shell and fewer surface problems. The research also concluded that various properties of concrete ingredients directly influence the shear strength of the concrete and plays an important role in preventing surface problems of the chimney shell during construction.

Generative artificial intelligence and optimisation framework for concrete mixture design with low cost and embodied carbon dioxide

This research presents a generative Artificial Intelligence (AI) and design framework that integrates machine learning (ML) and optimisation methodologies to discover new concrete mixture designs. Unlike traditional ML models that predict based on existing data, this framework innovatively generates new concrete mix designs that meet specific requirements such as strength, cost-efficiency, and reduced embodied CO2. To propose a powerful and reliable generative AI model, several advanced ML algorithms were considered, e.g., CatBoost, XGBoost, and LGBM. These models were trained on a unique dataset consisting of 4,936 data points collected from five different batching plants and have not been published yet. Bayesian Optimisation was employed to fine-tune model hyperparameters, resulting in the most effective models attaining R2 values of 0.94 and 0.89 for raw and grouped data, respectively. To verify the trained generative AI model, a case study was conducted, in which the model was requested to provide designs of a mix with pre-determined strength and optimised cost and embodied CO2. The mix designs generated by the framework were successfully validated through experimental tests, corroborating the predictive outcomes. The research culminated in the development of a web application, a tool crafted to streamline the concrete mixture design and optimisation process. This generative AI design framework can be applied to many other aspects of material design and engineering problems.

Innovative Approaches to Concrete Mix Design for Enhanced Project Efficiency

Innovative Approaches to Concrete Mix Design for Enhanced Project Efficiency

Mesoscale modelling of recycled aggregate concrete: A parametric analysis of properties of mesoscopic phases of RAC

This paper analyses the relative effects of ten parameters of recycled aggregate concrete (RAC) mix design on its mechanical properties. Research on the effect of the source concrete and of the quality of the interfacial transition zones (ITZ) on the properties of RAC is scarce and additional knowledge would guide RAC mix design towards efficiency and understanding of the effect of recycled aggregates (RA) on concrete. Therefore, this paper presents the outcomes of parametric numerical analyses on this topic. Within the range of parameters commonly found in conventional concrete mix design, it is found that: (1) the quality of both the old and new mortar substantially influences the mechanical behaviour of RAC. High-quality RAC can be produced with RA sourced from, at least, medium quality concrete (C40); (2) a poor-quality ITZ results in a significant decrease in concrete properties, particularly decreasing the tensile strength up to 58%, since micro-cracks that initiate in weak ITZ can readily develop into mortar; (3) the most relevant factors that determine the strength of RAC are the quality of mesoscopic phases, with the relative effects that range from 8% to 53% for compressive strength and from 22% to 59% for tensile strength; (4) the concrete mix design was less relevant, with a maximum influence of 23%; (5) the previous two observations imply that the most efficient way to improve the properties of RAC is to use RA of better quality; (6) a high proportion of coarse aggregates and a lower replacement ratio of natural aggregate with RA can effectively improve the stiffness of RAC by 16% and 15%. This paper contributes to understanding the influence of mix design and raw materials on the properties of RAC and determines the limiting factors that prevent a concrete mix from reaching its intended performance.

Development of New Method for Concrete Carbon Emission evaluation: The Anti-freezing durability-oriented Carbon Emission Indicator (CEI) System and its application in design of low carbon HPC with high durability

This research focuses on the development of the Anti-freezing Durability-Oriented Carbon Emission Indicator (CEI) system for assessing concrete's carbon footprint with an emphasis on durability, particularly in freeze-thaw conditions. This novel approach reduces the environmental impact of concrete by integrating durability metrics into carbon emission evaluations. The paper presents experimental findings on mix designs for high-strength, low-carbon concrete, which demonstrate improved freeze-thaw durability. And found out that concrete mixes using sulfate-resistant cement with GGBS outperform those with FA and OPC in terms of low-carbon performance, in the newly developed 19 mixes, concrete mix incorporating air-entraining agents with sulfate-resistant cement and 40% GGBS as a supplementary cementitious material, namely the HPC1-P•HSRF-S group, demonstrates a distinct low-carbon advantage across various indicators.Overall, the inclusion of fibers is shown to improve freeze-thaw durability, though with limited impact on compressive strength. The research underlines the importance of optimizing concrete mix designs for both environmental and durability considerations, presenting a significant step towards more sustainable construction practices.

Service life prediction model for biogenic acid corrosion: Advancement of life factor method for concrete sewer design

Current service life prediction models for concrete sewer design, including the widely used Life Factor Method (LFM), do not efficiently incorporate certain key corrosion contributing factors, such as material selection, concrete mix design, and sewer environment. This paper describes an advancement of the LFM model to cover these factors, broaden its usage, and improve concrete sewer design and applications. By integrating the first principles of the original LFM model, advanced knowledge from the literature, and results from field performance studies of various sewer concretes, a practical and relatively simple LFM model was advanced. With concrete mix design, oxide composition of all concrete ingredients, and sewer environmental and hydraulic conditions, the so-called ‘advanced LFM model’ could predict the corrosion rate of various concrete mixes. The model emphasised the influence of various modern binders, including calcium aluminate and calcium sulpho-aluminate cements, and various aggregate types including siliceous, calcareous, and alumina-based aggregates, when subjected to different sewer environments.

Predictive models for properties of hybrid blended modified sustainable concrete incorporating nano-silica, basalt fibers, and recycled aggregates: Application of advanced artificial intelligence techniques

The main objective of this work is to improve the compressive strength of concrete, specially in sustainable construction is to develop more precise predictive modeling techniques. The compressive strength prediction of basalt fiber reinforced concrete filled with nano-silica and recycled aggregates can be done using a hybrid deep learning model suggesting the use of the combination of Convolutional Neural Networks and Long Short-Term Memory networks. The CNN captures microstructural features from SEM images, while the LSTM models temporal dependencies from sequential curing data samples. To enhance the prediction accuracy, PCA was performed on feature dimensionality reduction and GA optimized hyperparameters both for the model as well as the concrete mix design for improved strength with cost effectiveness. With an R² value of 0.92–0.95, the performance results of the presented model came out better than the baseline models, as well as reducing the MAE by 20 %. Besides, there existed a 5–8 % better compressive strength in GA-optimized mix designs. Robustness comes into play with the model that shows steady strength predictions, regardless of conditions of curing under multiple conditions and at different material composition levels. Furthermore, the reutilization of recycled aggregates and nano-silica gives a real environmental benefit as less waste is produced but the material performance is maximized. This kind of outcome indicates how the proposed model can be practically applied in optimizing concrete design in terms of strength and sustainability features, thus providing an accessible instrument for decision-making in the construction field. It is an effective tool to improve the performance of concrete while minimizing environmental and material wastes.

Performance-Based Design of Ferronickel Slag Alkali-Activated Concrete for High Thermal Load Applications.

This study aimed to develop optimized alkali-activated concrete using ferronickel slag for high-temperature applications, focusing on minimizing environmental impact while maintaining high compressive strength and slump. A response surface methodology, specifically the mixture design of experiments, was employed to optimize five components: water, FNS-based alkali-activated binder, and three aggregate sizes. Twenty concrete mixes were tested for slump and compressive strength before and after exposure to 600 °C for two hours. The optimal mix achieved 88 MPa compressive strength before heat exposure and 34 MPa after, with a slump of 140 mm. An upscaled version with improved workability (210 mm slump) maintained similar unheated strength but showed reduced post-heating strength (23.5 MPa). Replacing limestone with olivine aggregates in the upscaled mix resulted in 65 MPa unheated and 32 MPa post-heating strengths. Life Cycle Analysis revealed that the optimized ferronickel slag alkali-activated concrete's CO2 emissions were 77% lower than those of ordinary Portland cement concrete of equivalent strength. This approach demonstrated the applicability of mixture design of experiments as an alternative design methodology for alkali activated concrete, providing a valuable performance-based design tool to advance the application of alkali-activated concrete in the construction industry, where no prescriptive standards for alkali-activated ferronickel concrete mix design exist. The study concluded that the developed ferronickel slag alkali-activated concrete, obtained through a performance-based mixture design methodology, offers a promising, environmentally friendly alternative for high-strength, high-temperature applications in construction.

Use of macromolecules lignosulfonate and graphene oxide to prepare non-autoclaved aerated concrete

The development of new structural materials with thermal insulation properties is urgently need in the construction of smart buildings. Besides, there is a need to develop environmentally friendly and sustainable concrete mix designs. Lignosulfonate (LS) macromolecule and graphene oxide (GO) were used to prepare non-autoclaved aerated concrete (NAC). The addition of a complex GO/LS additive of the composition (0.16/0.0002 wt%) increased the compressive strength by 54 %, and bending strength by 45 % at the age of 28 days' strength gain. The addition of an effective complex GO/LS additive to NAC made it possible to achieve a reduction in water absorption by up to 63 % and thermal conductivity by up to 29 % in comparison. The thermal conductivity coefficient of such NAC specimens was 0.092 W/m·K with water absorption of 9 %. The options for the interactions of the GO/LS modifier with calcium hydroaluminates using the type of ion exchange were proposed. Thus, the introduction of GO/LS nano modifier contributed to high-quality filling of free areas of the cement mixture with mineral formations, which helpd to increase the strength of the aerated concrete and its durability.

Enhancing concrete structures: Integrating machine learning and deep learning for optimizing material strength, fire resistance, and impact protection

This paper presents an innovative approach to enhancing the performance of concrete structures by integrating advanced machine learning (ML) and deep learning (DL) techniques. Concrete, a ubiquitous material in construction, is known for its strength and durability, but its performance can be significantly improved through the optimization of material properties such as strength, fire resistance, and impact protection. Traditional methods of optimizing these properties rely heavily on empirical testing and expert intuition, which can be time-consuming and may not fully capture the complex interactions between different material components. In this study, we propose a framework that leverages ML and DL algorithms to analyse vast datasets of concrete compositions and their corresponding performance metrics [1]. By employing these computational techniques, we aim to identify patterns and relationships that can guide the design of more resilient concrete mixes. The integration of DL models, particularly neural networks, allows for the prediction of material behaviour under various conditions, leading to more accurate and reliable optimization of concrete properties. The proposed framework has the potential to revolutionize the construction industry by enabling the development of concrete with enhanced properties, reducing the need for extensive physical testing, and accelerating the innovation process. This paper discusses the methodology, implementation, and potential impact of using ML and DL in concrete optimization, providing a roadmap for future research and practical applications in improving the performance of concrete structures [2].

Flexural behaviour and post-cracking performance of polypropylene fibre-reinforced waste cardboard blended concrete

The requalification of recycled materials has become of significance owing to a global shift towards net zero emissions and the severe environmental impact of misused recyclable waste. Cardboard and polypropylene have evolved into significant waste products, and the purpose of the study was to focus on the repurposing of both waste materials in determining the structural behaviour of an eco-efficient fibre-reinforced waste concrete. The study extended upon the findings of a precursor study in the development of an eco-efficient fibre-reinforced waste concrete mix. Finely processed municipally collected waste cardboard and recycled polypropylene macro-fibres were incorporated into a concrete mix design for which an experimental program was realised. To achieve this, uniaxial compressive strength, modulus of rupture, modulus of elasticity, and indirect tensile strength of the waste and reference mixtures were determined for 7- and 28-day intervals. Crack mouth open displacement testing was achieved to assess the post-cracking response, and retaining wall beams were prepared and loaded to determine the efficacy of the concrete mixture for low load-bearing structures. Mechanical and post-cracking properties of the hybrid waste concrete underscored excellent performance in comparison to other waste fibre-reinforced concrete mixtures, and superior to those blended with similar kraft fibre volume fractions. Moreover, the structural performance of beams cast from the hybrid mixture closely matched those of the reference mixture, emphasising the viability of the mixture and the role waste materials have in the construction and building industry.

Advanced Predictive Modeling of Concrete Compressive Strength and Slump Characteristics: A Comparative Evaluation of BPNN, SVM, and RF Models Optimized via PSO

This study presents the development of predictive models for concrete performance, specifically targeting the compressive strength and slump value, utilizing the quantities of individual raw materials in the concrete mix design as input variables. Three distinct machine learning approaches—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were employed to establish the prediction models independently. In the model construction process, the Particle Swarm Optimization (PSO) algorithm was integrated with cross-validation to fine-tune the hyperparameters of each model, ensuring optimal performance. Following the completion of training and modeling, a comprehensive comparison of the predictive accuracy among the three models was conducted, with the aim of selecting the most suitable model for incorporation into an optimized objective function. The findings reveal that among the chosen machine learning techniques, BPNN exhibited superior predictive capabilities for the compressive strength of concrete. Specifically, in the validation set, BPNN achieved a high correlation coefficient (R) of 0.9531 between the predicted and actual outputs, accompanied by a low Root Mean Square Error (RMSE) of 4.2568 and a Mean Absolute Error (MAE) of 2.6627, indicating a precise and reliable prediction. Conversely, for the prediction of the concrete slump value, RF outperformed the other two models, demonstrating a correlation coefficient (R) of 0.8986, an RMSE of 9.4906, and an MAE of 5.5034 in the validation set. This underscores the effectiveness of RF in capturing the complexity and variability inherent in slump behavior. Overall, this research highlights the potential of integrating advanced machine learning algorithms with optimization techniques for enhancing the accuracy and efficiency of concrete performance predictions. The identified optimal models, BPNN for compressive strength and RF for slump, can serve as valuable tools for engineers and researchers in the field of construction materials, facilitating the design of concrete mixes tailored to specific performance requirements.

An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete

Graphene oxide (GO) has shown promise in improving concrete strength. Despite its frequent use in cement composites, its effect on concrete properties is less explored. The influence of GO on concrete remains unclear due to various interactions among constituents such as GO type and percentage, Superplasticiser (SP) type and percentage, dispersion technique, and curing age. Therefore, making prediction of compressive strength using simple mathematical formation is challenging. This study represents the first-ever attempt at developing a comprehensively validated machine learning model to predict GO-concrete's compressive strength characteristics by considering all key variable parameters. A comprehensive laboratory experiment program was conducted to collect the data required for training the machine learning (ML) models. Once the ML models were trained, they were used to predict the relationship of each of those input variables towards the compressive strength properties. Four machine learning models—(a) multiple linear regression (MLR), (b) k-nearest neighbour (KNN), (c) random forest (RF) and (d) extreme gradient boost (XGB)—were utilised to model the relationship between input parameters and compressive strength. Also, explainable machine learning (XML) methods were employed to elucidate the impact of mixed constituents on the compressive strength of GO concrete. The results from test predictions showcase that the XGB has attained a coefficient of determination (R2) of 0.981, coefficient of correlation (R) of 0.99, mean square error (MSE) of 0.9, mean normalised bias (MNB) of 0.004, scatter index (SI) of 0.2 with mean absolute error (MAE) of 0.8 MPa for the predictions, outperforming the remaining models. XML highlighted the true impact of GO and the remaining variables, emphasising that the presence of GO significantly improves compressive strength. The optimum GO content and optimum superplasticiser content observed from the experimental work agreed with results obtained from the XML analysis, showing the consistency of the explanation with the experimental results. This implies the superiority of using XML methods in concrete mix design applications in industry.

Resistance to acid, alkali, chloride, and carbonation in ternary blended high-volume mineral admixed concrete

The World Bank study predicts that 4 °C warming will bring high temperatures, sea-level rise, and saltwater intrusion to coastal areas, damaging coastal concrete structures. Increased CO2 from industrialization exacerbates this, necessitating durable, low-carbon concrete. Combined use of fly ash (FA) and ground granulated blast furnace slag (GGBFS) as high-volume OPC replacements boosts performance while reducing concrete’s carbon footprint. In this perspective current study examines the durability of concrete against aggressive agents (H2SO4, MgSO4, NaCl, and CO2) causing premature deterioration of concrete structures. Initially, three cost-effective sustainable concrete mix designs were developed, incorporating 50% replacement of OPC with locally available supplementary cementitious materials, specifically FA and GGBFS. These mixes were then evaluated for their mechanical and durability performances. The impact of aggressive ions (SO4 2−, Cl−, and CO3 2−) was studied by examining the changes in mechanical performance and phase assemblages. Thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR) techniques were used to estimate the phase compositions. Ternary blended concrete having 50% OPC+ 30% GGBFS + 20% FA exhibited optimal synergistic performance, enhancing pozzolanic and hydraulic reactions for better resistance to harmful ions. The sorptivity test confirmed that as the GGBFS content increased, the sorption rate decreased, indicating the higher reactive nature of GGBFS to that of FA. Deleterious compounds formed due to the action of SO4 2-, Cl-, and CO3 2- were identified to be ettringite (Ca6Al2(SO4)3(OH)12.32H2O, AFt) and gypsum (CaSO4.2H2O, Gy), Friedel’s salt (Ca4Al2(OH)12Cl2.4H2O, Fs) and polymorphs of calcium carbonate (CaCO3), respectively through TG mass loss curve. These results were corroborated by FTIR analysis, which showed predominant characteristic bands at 662 cm−1 for SO4 2−, 459 cm−1 for Mg–O stretching, 790 cm−1 for Al–OH bending, and 1431-1443 cm−1 for C–O, confirming the presence of the deleterious compounds.

Calcined Clays as Concrete Additive in Structural Concrete: Workability, Mechanical Properties, Durability, and Sustainability Performance.

Calcined clays (CCs) as supplementary cementitious materials (SCMs) can be a promising option to reduce clinker content and CO2 emissions in eco-friendly concretes. Although CCs as components of composite cements in combination with Ordinary Portland Cement (OPC) and limestone powder (LSP) have attracted industry interest, their use as concrete additives is limited. This study investigates the effects of the addition of CCs on the fresh and hardened properties of industry-standard ready-mixed concretes. Four concrete mix designs, each with three superplasticizer dosages, were tested, resulting in 12 variations. The CCs used, which are typical of 2:1 bentonite clays with low metakaolin content, reflect the clays available in Germany. The results showed that CCs significantly influenced the workability, which could be controlled with a high superplasticizer dosage. Increased CC contents reduced bleeding tendencies, which was beneficial for certain structural applications. Early age strength decreased with CCs, but the 28-day strength exceeded that of pure OPC concretes up to 30 wt% CCs. Resistance to CO2-induced carbonation decreased with higher levels of CCs but was comparable up to 15 wt%. Freeze-thaw damage decreased, and chloride migration resistance improved due to a denser microstructure. The global warming potential (GWP) of the concretes tested is in line with that reported in the literature for concretes made from highly blended cements, suggesting that CCs can improve the sustainability of concrete production.

Report of RILEM TC 281-CCC: A critical review of the standardised testing methods to determine carbonation resistance of concrete

The chemical reaction between CO2 and a blended Portland cement concrete, referred to as carbonation, can lead to reduced performance, particularly when concrete is exposed to elevated levels of CO2 (i.e., accelerated carbonation conditions). When slight changes in concrete mix designs or testing conditions are adopted, conflicting carbonation results are often reported. The RILEM TC 281-CCC ‘Carbonation of Concrete with Supplementary Cementitious Materials’ has conducted a critical analysis of the standardised testing methodologies that are currently applied to determine carbonation resistance of concrete in different regions. There are at least 17 different standards or recommendations being actively used for this purpose, with significant differences in sample curing, pre-conditioning, carbonation exposure conditions, and methods used for determination of carbonation depth after exposure. These differences strongly influence the carbonation depths recorded and the carbonation coefficient values calculated. Considering the importance of accurately determining carbonation potential of concrete, not just for predicting their durability performance, but also for determining the amount of CO2 that concrete can re-absorb during or after its service life, it is imperative to recognise the applicability and limitations of the results obtained from different tests. This will enable researchers and practitioners to adopt the most appropriate testing methodologies to evaluate carbonation resistance, depending on the purpose of the conclusions derived from such testing (e. g. materials selection, service life prediction, CO2 capture potential).

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.