Concrete is a very flexible composite material that is extensively employed in the building industry. Steel slag is a waste material produced during steelmaking. It is formed during the separation of molten steel from impurities in steelmaking furnaces. Slag starts as a molten liquid melt and cools to a solid state. It is a solution of silicates and oxides that is rather complicated. Steel slag recovery is environmentally friendly since it conserves natural resources and frees up landfill space. Steel slag has been extensively utilized in concrete as a partial substitute for normal and crushed coarse aggregate to improve the mechanical qualities of normal-strength concrete, such as compressive strength. The researchers and suppliers investigated that using steel slag instead of normal coarse aggregate could save the environment and natural resources. Three hundred thirty-eight (338) data sets were gathered and evaluated in total. During the modeling procedure, the most significant factors affecting the compressive strength of concrete with steel slag replacement were considered, including the curing time of 1–180 days, the cement content of 237.35–550 kg/m3, the water-to-cement ratio of 0.3–0.872, the fine aggregate content of 175.5–1285 kg/m3, the steel slag content of 0–1196 kg/m3, and the coarse aggregate content of 0–1253.75 kg/m3. A credible mathematical model is needed to investigate the influence of steel slag as a partial replacement on concrete compressive strength. Mathematical models will help engineers and concrete industries mix a proper concrete mix design, including steel slag, to achieve a desired compressive strength without doing any experimental work. As a result, an artificial neural network (ANN), an adaptive network-based fuzzy inference system (ANFIS), a multivariate adaptive regression splines (MARS), and an M5P-tree model were presented in this research to predict the compressive strength of concrete with steel slag aggregate replacement. According to previous research findings, all percentages of steel slag improve compressive strength. According to statistical studies, the adaptive network-based fuzzy inference system model outperformed the other models in forecasting steel slag replacement compressive strength for normal strength concrete (ANN, MARS, and M5P-tree). It has a higher coefficient of determination of 0.99, a smaller mean absolute error of 0.74 MPa, a smaller root mean square error of 1.12 MPa, a smaller scatter index of 0.029, and a smaller objective of 0.93 MPa.

Accurately forecasting the depth of wear (Dw) of concrete is essential for ensuring the durability, safety, and efficiency of concrete structures while also promoting sustainability and cost-efficiency in construction projects. This study utilized systematic multiscale models to predict the concrete depth of wear, analyzing 391 samples with varying cement content (107–398 kg/m3), water-to-binder ratio (0.31–0.37), class C fly ash content (0–316 kg/m3), fine aggregate (607–705 kg/m3), coarse aggregate (1099–1266 kg/m3), plasticizer (2.6–2.9 l/m3), air entraining agent (0.3–1.4 l/m3), curing time (28–365 days), and testing time (5–60 min). Linear (LR), pure quadratic (PQ), interaction (IN), and M5P-tree models were used to identify key parameters affecting Dw. The models accurately estimated Dw about mixture proportions based on metrics such as R2, MAE, RMSE, OBJ, SI, and a-20 index. The results indicated that concrete with up to 30

Self-compacting concrete (SCC) is a specialized type of concrete that features excellent fresh properties, enabling it to flow uniformly and compact under its weight without vibration. SCC has been one of the most significant advancements in concrete technology over the past two decades. In efforts to reduce the environmental impact of cement production, a major source of CO2 emissions, silica fume (SF) is often used as a partial replacement for cement. SF-modified SCC has become a common choice in construction. This study explores the effectiveness of soft computing models in predicting the compressive strength (CS) of SCC modified with varying amounts of silica fume. To achieve this, a comprehensive database was compiled from previous experimental studies, containing 240 data points related to CS. The compressive strength values in the database range from 21.1 to 106.6 MPa. The database includes seven independent variables: cement content (359.0-600.0 kg/m3), water-to-binder ratio (0.22-0.51), silica fume content (0.0-150.0 kg/m3), fine aggregate content (680.0-1166.0 kg/m3), coarse aggregate content (595.0-1000.0 kg/m3), superplasticizer content (1.5-15.0 kg/m3), and curing time (1-180 days). Four predictive models were developed based on this database: linear regression (LR), multi-linear regression (MLR), full-quadratic (FQ), and M5P-tree models. The data were split, with two-thirds used for training (160 data points) and one-third for testing (80 data points). The performance of each model was evaluated using various statistical metrics, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), objective value (OBJ), scatter index (SI), and a-20 index. The results revealed that the M5P-tree model was the most accurate and reliable in predicting the compressive strength of SF-based SCC across a wide range of strength values. Additionally, sensitivity analysis indicated that curing time had the most significant impact on the mixture's properties.

In recent years, there has been a global trend toward producing environmentally friendly construction materials. concrete, one of the most widely used construction materials worldwide, has received substantial attention from researchers in the pursuit of eco-friendliness. This is because approximately three-quarters of concrete is composed of natural aggregates, which harm the local environment during their extraction. This study employed soft computing models to predict the compressive strength of fly ash-modified concrete produced with recycled aggregate concrete (RCA) to replace natural fine and/or coarse aggregate. For that purpose, a database containing 295 data points was assembled from the literature and utilized to develop models for predicting CS of various RCA mixture mix proportions. The database included diverse concrete mixtures with varying input parameters: cement content (225–560 kg/m 3 ), water-to-binder ratio (0.29–0.66), natural coarse aggregate (0–1237 kg/m 3 ), natural fine aggregate (0–1050 kg/m 3 ), recycled coarse aggregate (0–1215 kg/m 3 ), recycled fine aggregate (0–1050 kg/m 3 ), fly ash (0–225.5 kg/m 3 ), superplasticizer (0–7.3 kg/m 3 ), and curing times (1–90 days). In the modeling process, machine learning approaches were used, specifically the Multi Expression Programming, Artificial Neural Network, Multi Adaptive Regression Spline , and Extreme Gradient Boosting models. Cross-validation was used to determine tuning the required training parameters. The generated models were assessed using statistical evaluation tools, including the correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Absolute Error, and Scatter Index (SI). Based on the evaluation of the developed models, the XGboost model outperformed other models using fivefold cross-validation, demonstrating high R and low RMSE and SI. SHAP values were then utilized to identify the most influential parameters in predicting the compressive strength of RA concrete, with curing time ranking as the most important, followed by water-to-binder ratio and cement content.

Self-compacted concrete (SCC) is one of the special types of concrete. The SCC represents one of the most significant developments in concrete technology over the previous two decades. It can compact itself using its weight without requiring vibration due to its excellent fresh characteristics, which allow it to flow into a uniform level under the impact of gravity. Since cement manufacturing is one of the largest contributors to CO2 gas emissions into the atmosphere, fly ash (FA) is used in concrete as a cement replacement. Currently, FA-modified SCC is widely utilized in construction. This research aimed to study the potential of soft computing models in predicting the compressive strength (CS) and slump flow diameter (SL) of self-compacted concrete modified with different fly ash content. Hence, two databases were created, and relevant experimental data was collected from previous studies. The first database consists of 303 data points and is used to predict the CS. The second database predicts the SL and contains 86 data points. The dependent parameters are the CS, which varies from 9.7 to 79.2 MPa, and the SL, which varies from 615 to 800 mm. The identical five independent parameters are available in each database. The ranges for CS prediction are water-to-binder ratio (0.27–0.9), cement (134.7–540 kg/m3), sand (478–1180 kg/m3), fly ash (0–525 kg/m3), coarse aggregate (578–1125 kg/m3), and superplasticizer (0–1.4

Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it is not biodegradable and causes environmental issues. One of the most effective ways is recycling those wastes or using them as a replacement for normal aggregate in the concrete mixture, which has high impact resistance and toughness; thus, it will be a good choice. In this study, 135 data were collected from previous literature to develop a model for the prediction of rubberized concrete compressive strength; the database comprised different mixture proportions, the maximum size of the rubber (1-40 mm), and the rubber percentage (0-100%) replacing natural fine and coarse aggregates were among the input parameters in addition to cement content (380-500 kg/m3) water content (129-228 kg/m3), fine aggregate content (0-925 kg/m3), coarse aggregate content (0-1303 kg/m3), and curing time of the samples (1-96 Days); then the collected data were used in developing Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) Models for predicting compressive strength (CS) of rubberized concrete. The parametric analysis reveals that as the maximum rubber size increases, the reduction in compressive strength becomes more pronounced. Notably, this strength decline is more significant when rubber replaces coarse aggregate than its replacement of fine aggregate. Among the input parameters considered, it is evident that the fine aggregate content exerts the most substantial influence on the compressive strength of rubberized concrete. Its impact on predicting compressive strength surpasses other factors, with the concrete samples' curing time ranking second in importance. According to the assessment tools, the ANN model performed better than other developed models, with high R2 and lower RMSE, MAE, SI, and MAPE. Additionally, ANN and MARS models predicted the CS of different sizes better than MEP and NLR models. Subsequently, we employed the collected data to develop predictive models using Multi Expression Programming (MEP), Artificial Neural Network (ANN), Multi Adaptive Regression Spline (MARS), and Nonlinear Regression (NLR) techniques to forecast the compressive strength (CS) of rubberized concrete. The statistical analysis tools assessed the performance of these developed models through various evaluation criteria, including the Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), and Mean Absolute Percentage Error (MAPE). In summary, our study underscores the efficacy of recycling rubber materials in concrete production. It presents a powerful predictive model for assessing the compressive strength of rubberized concrete, with the ANN model standing out as the most accurate and reliable choice for this purpose.

Environmental issues, such as global warming and pollution, could be solved by reducing the carbon dioxide (CO2) footprint on the surrounding atmosphere. Utilizing by-products as a cement substitute in cement production, such as cement kiln dust (CKD), could reduce CO2 emissions from burning raw materials in cement plants. This study investigated the effect of cement kiln dust on cement mortar's physical, flow, and mechanical properties. Cement was replaced with CKD up to 100% (by weight of dry cement). The optimum content of CKD was determined based on compressive strength; loss on ignition (LOI); and chloride (Cl), sulfate (SO3), and magnesium oxide (MgO) contents. Standard sand with a maximum diameter of 2 mm was used in this study, with a sand-to-binder ratio (s/b) of 3 : 1. Three different models-multiexpression programming (MEP), nonlinear regression (NLR), and an artificial neural network (ANN)-were employed for estimating the compressive strength of CKD-modified cement mortar using the present study data (110 data sets) and 152 data collected from other research studies. The compressive strength of cement mortar was predicted as a function of water-to-binder ratio (w/b), sand-to-binder ratio, cement kiln dust content, silicon dioxide content in the binder, calcium oxide content in the binder, the maximum aggregate diameter (MDA), and curing ages (t). Based on the statistical assessments, the ANN models outperformed the MEP and NLR models in the testing phase. According to the sensitivity analysis, curing time is the most critical parameter affecting the compressive strength of CKD-modified cement mortar, and the SiO2 content percentage affected the compressive strength more than did the CaO content percentage.

This study explores self-compacting concrete (SCC) enhancement by incorporating silica fume, fly ash, or both across various mix compositions. The research evaluates six predictive models—linear regression, nonlinear regression, pure quadratic, interaction, full quadratic, and artificial neural network (ANN)—to predict the compressive strength of SCC. A dataset of 330 experimental studies covering a wide range of parameters, such as water/cement ratio, cement content, aggregate content, superplasticizer content, silica fume content, fly ash content, and curing time, is used. The compressive strength of the datasets ranges from 4.9 to 87 MPa, while slump flow diameter ranges from 450 to 790 mm. The models are assessed using objective function, root mean square error (RMSE), scatter index (SI), and mean absolute error (MAE). The ANN is the most accurate among the models, achieving an R2 of 0.94, RMSE of 3.56 MPa, MAE of 2.67 MPa, and SI of 0.09. The models effectively predict compressive strength across various concrete compositions, although they do not predict slump flow diameter, as SCC specifications require it to be within 550 to 850 mm.

Recent advances highlight the potential of low-carbon concrete using recycled aggregates from precast rejects. This material, called precast recycled aggregate concrete (PRAC), shows superior properties compared to traditional recycled aggregate concrete. However, accurately predicting PRAC’s mechanical properties remains challenging due to numerous influencing variables. This challenge is further compounded when PRAC’s design must align with mechanical, economic and ecological objectives. In light of these challenges, this study presents a framework that leverages Bayesian model updating and metaheuristic techniques to optimize PRAC’s mix proportions. Initially, a comprehensive database was compiled from existing literature. Subsequently, employing Bayesian model updating, two definitive expressions were established linking PRAC’s mechanical strengths to key variables. Finally, a multi-objective optimization approach, integrating the posterior model and the Cuckoo search algorithm, was devised to identify optimal mix designs for PRAC. The proposed models adeptly capture PRAC’s nuanced property trends, while the Bayesian-Cuckoo search hybrid significantly expedites the mix optimization process. Results advocate for a minimum of 60% recycled aggregate substitution to strike a balance between PRAC’s strength and environmental impact. In essence, this framework furnishes dependable solutions for PRAC formulations and demonstrates adaptability for potential use in broader concrete mix design scenarios.

This paper studies the use of municipal solid waste incinerator bottom ash as a supplementary cementitious material in concrete products using an innovative chemical treatment approach. The primary objective is to address emissions associated with waste-to-energy facilities and the heavy reliance on ordinary Portland cement as the primary binder in concrete. The proposed method involves the removal of metallic aluminium from the bottom ash and the subsequent use of the treated bottom ash as a partial cement replacement to produce concrete. Concrete specimens were produced with varying proportions of treated or untreated municipal solid waste incinerator bottom ash, replacing 20%, 35%, and 55% of ordinary Portland cement according to EN 197 European standard for common cement. Moreover, class F fly ash was incorporated in equivalent percentages as a reference supplementary cementitious material, and a control mix was prepared using solely Portland cement. The evaluation encompassed multiple visual and analytical techniques, including scanning electron microscopy, X-ray diffraction, X-ray fluorescence, and setting time analyses on pastes made with Portland cement, fly ash, and bottom ash. All specimens were evaluated in terms of mechanical performance, namely compressive strength. The chemical treatment process facilitated the release of a significant quantity of hydrogen, a by-product of aluminium oxidization. Consequently, this resulted in significantly reduced formation of gas bubbles in concrete in the fresh state and, therefore, diminished expansion during the setting process. As the proportion of cement replacement with bottom ash increased, a decline in strength was observed. However, this decline was less pronounced when using treated bottom ash, particularly with lower levels of incorporation.

Environmental issues are raised from global warming due to raised Carbon Dioxide (CO2) emissions of factories worldwide. Cement production provides about 8-10% of the total CO2 emissions to the environment. Cementitious materials, such as fly ash, are suggested as the best alternatives to cement as the main ingredient of concrete. Fly ash is a powder finer than cement, almost rich in silica and alumina. The current study investigated the effect of the ratio of SiO2/ CaO in fly ash on the compressive strength of cement-based concrete modified with different fly ash contents and classes for various mix proportions. 236 fly ash-modified concrete samples were examined, evaluated, and modeled for that purpose. The study includes independent parameters such as; coarse aggregate (801-1246 kg/m3), fine aggregate (522-905 kg/m3), cement (67-356 kg/m3), fly ash (71-316 kg/m3), cement replacement (18-100%), water-to-binder ratio (0.28-0.60), silicon dioxide to calcium oxide ratio (0.984-174.0), and curing time (3-365 days). The dependent parameter is compressive strength (7.98-92.93 MPa); it is divided into three ranges; low-strength (less than 20 MPa), normal strength (20-50 MPa), and high-strength (greater than 50 MPa). This study utilized several mathematical, soft computing, and machine learning modeling tools to develop a dependable model for predicting the compressive strength of con-crete. The Linear Regression (LR), Pure Quadratic (PQ), M5P-tree, and Interaction (IN), were used to prediction. To provide accurate and reliable models, multiple assessment criteria were utilized, such as correlation coefficient (R2), Root mean squared error (RMSE), Mean absolute error (MAE), Scatter Index (SI), Objective function (OBJ), and a-20 index. The IN model was the most effective and accurate model; where R2 of 0.95, RMSE of 4.33 MPa, MAE of 3.45 MPa, and SI of 0.099 were conducted from the analytical studies. The M5P-tree model produced an OBJ value of 1.31 and the lowest residual error value of-17.99 to +16.97 MPa. As well as, the PQ model maintained the best a-20% index value, which was 100%. From the IN model, increasing SiO2/CaO ratio from 0.984 to 17.0 caused a decrease in compressive strength but increased beyond the 17.0 ratio. However, the compressive strength decreased with increasing the cement replacement and fly ash content when the coarse aggregate content was used by less than 905 kg/m3. In addition, the models were applied to the dataset based on the shape and size of the specimens. According to the sensitivity analysis, the curing time and water-to-binder ratio are the most important parameters for predicting the compressive strength of concrete using this dataset.

The aim of this study is to evaluate the synergistic effect of polyester fiber-reinforced and nanoslica on the technical performance and durability of geopolymer mortar in terms of the chemical resistance. The study examined how the addition of polyester fiber and nanosilica affects the short-term severe durability of geopolymer mortar specimens made with fly ash (type F). The specimens were cured under ambient conditions. Different percentages (0.6%, 1.2%, and 1.8%) of polyester fiber were used, both with and without nanosilica. Additionally, a reference mixture containing only nanosilica was prepared.All mixtures had a liquid to binder ratio of 0.50, and the ratio of NaOH to Na2SiO3 solution was kept at 2.5:1 by weight. The produced mixes, after 28 days of ambient curing, were immersed for another 28 days in solutions containing 3.5%, 5%, and 5% of sodium chloride, magnesium sulphate and sulfuric acid, respectively. For comparison, control specimens which were not exposed to chemical attacks were tested at the same age of 56 days. Moreover, water absorption and sorptivity tests were conducted to explain the durability performance in a more detailed way. The test results express that the combination of both materials showed a synergistic effect and resulted in greater improvements in compressive and flexural strengths. Both materials can reduce the reduction in compressive strength caused by sulfuric acid exposure, but polyester fiber can increase mass loss. The presence of magnesium sulfate and sodium chloride can lead to a reduction in strength, but the addition of both polyester fiber and nanosilica can mitigate these effects. The addition of fibers creates a network of pores that can limit water absorption, and nanosilica can further enhance the microstructure and reduce water absorption. However, using polyester fiber beyond 1.2 percent can adversely affect the rate of water absorption.

The brittleness of plain concrete (PC) is a result of its lack of tensile strength and poor resistance to cracking, which in turn limits its potential uses. The addition of dispersed fibres into the binding material has been demonstrated to have a positive impact on the tensile properties of PC. Nevertheless, using new or engineered fibres in concrete significantly increases the overall cost and carbon footprint of concrete. Consequently, the main obstacle in creating environmentally friendly fibre-reinforced concrete is the traditional design process with energy-intensive materials. This study investigated how the engineering properties and life cycle impact of concrete were influenced by varying the volume fractions of jute fibre (JF). The impact of incorporating silica fume (SF) as a partial replacement of Portland cement was also studied. The studied parameters included mechanical behaviour, non-destructive durability indicators, and the life cycle impact of concrete using JF and SF. The efficiency of JF in mechanical performance improved with the increase in age and with the addition of SF. When using both SF and 0.3% JF, there was an improvement of around 28% in the compressive strength (CS). When 0.3% JF was added, in the presence and absence of SF, the splitting tensile strength (STS) improvement was around 20% and 40%, respectively. The addition of JF improved the residual flexural strength (FS) and flexural ductility of PC. The SF addition overcame the drawbacks of the poor resistance of JF-reinforced concrete (JFRC) against water absorption (WA) and rapid chloride ion penetration (RCIP).

In the current study, the effect of cement kiln dust on the fresh and hardened cement mortar was investigated; chemical analysis of the binder was also included. Cement kiln dust (CKD) content (

Fly ash is a by-product almost found in coal power plants; it is available worldwide. According to the hazardous impacts of cement on the environment, fly ash is known to be a suitable replacement for cement in concrete. A lot of carbon dioxide (CO2) is released during cement manufacturing. The investigations estimate that about 8 - 10% of the total CO2 emissions are maintained by cement production. Since fly ash has nearly the same chemical compounds as cement, it can be utilized as a suitable alternative to cement in concrete (green concrete). The current study analyzes the effect of the quantity of the two main components of fly ash, CaO, and SiO2, on the compressive strength of concrete modified with different fly ash content for various mix proportions. For this purpose, various concrete samples modified with fly ash were collected from the literature (236 datasets), analyzed, and modeled using four different models; Full-quadratic (FQ), Nonlinear regression (NLR), Multi-linear regression (MLR), and Artificial neural network (ANN) model to predict the compressive strength of concrete with different geometry and size of the specimens. The accuracy of the models was evaluated using correlation coefficient (R2), Mean absolute error (MAE), Root mean squared error (RMSE), Scatter Index (SI), a-20 index, and Objective function (OBJ). According to the modeling results, increasing SiO2 (%) increased the compressive strength, while increasing CaO (%) increased compressive strength only when the cement replacement with fly ash was between 52 and -100%. Based on R2, RMSE, and MAE, the ANN model was the most effective and accurate on predicting the compressive strength of concrete in different strength ranges. According to the sensitivity analysis, curing time is the most critical characteristic for predicting the compression strength of concrete using this database. The primary objective of this work is to explore and evaluate various machine learning models for predicting compressive resistance. The study emphasizes these models' development, comparison, and performance assessment, highlighting their potential to predict compressive strength accurately. The research primarily uses machine learning, leveraging algorithms and techniques to build predictive models. The focus is on harnessing the power of data-driven approaches to improve the accuracy and reliability of compressive strength predictions.

It is generally known that the two most crucial elements of concrete that depend on the slump value of the mixture are workability and compressive strength. In addition, slump retention is more delicate than the commonly used slump value since it reflects the concrete mixture’s durability for usage in civil engineering applications. In this study, the effect of three water-reducer additives was tested on concrete’s workability and compressive strength from 1 day to 28 days of curing. The slump of the concrete was measured at the time of adding water to the mix and after 30 min of adding water. This study employed 0–1.5% (%wt) water-reducer additives. The original ratio between water and cement (wc) was 0.65, 0.6, and 0.56 for mixtures incorporating 300, 350, and 400 kg of cement. It was lowered to 0.3 by adding water-reducer additives based on the additives type and cement content. Depending on the kind and amount of water-reducer additives, w/c, gravel content, sand content, crushed content, and curing age, adding water-reducer additives to the concrete increased its compressive strength by 8% to 186%. When polymers were added to the concrete, they formed a fiber net (netting) that reduced the space between the cement particles. As a result, joining the cement particles quickly enhanced the fresh concrete’s viscosity and the hardened concrete’s compressive strength. The study aims to establish mathematical models (nonlinear and M5P models) to predict the concrete compressive strength when containing water-reducer additives for construction projects without theoretical restrictions and investigate the impact of mix proportion on concrete compressive strength. A total of 483 concrete samples modified with 3 water-reducer additives were examined, evaluated, and modeled for this study.

Nylon waste fibers similar to new nylon fibers possess high tensile strength and toughness; hence, they can be used as an eco-friendly discrete reinforcement in high-strength concrete. This study aimed to analyze the mechanical and permeability characteristics and life cycle impact of high-strength concrete with varying amounts of nylon waste fiber and micro-silica. The results proved that nylon waste fiber was highly beneficial to the tensile and flexural strength of concrete. The incorporation of a 1% volume of nylon waste fiber caused net improvements of 50% in the flexural strength of concrete. At the combined addition of 0.5% volume fraction of nylon fiber and 7.5% micro-silica, splitting tensile and flexural strength of high-strength concrete experienced net improvements of 49% and 55%, respectively. Nylon fiber-reinforced concrete exhibited a ductile response and high flexural toughness and residual strength compared to plain concrete. A low volume fraction of waste fibers was beneficial to the permeability resistance of high-strength concrete against water absorption and chloride permeability, while a high volume (1% by volume fraction) of fiber was harmful to the permeability-resistance of concrete. For the best mechanical performance of high-strength concrete, 0.5% nylon waste fiber can be used with 7.5% micro-silica. The use of micro-silica minimized the negative effect of the high volume of fibers on the permeability resistance of high-strength concrete. The addition of nylon waste fibers (at 0.25% and 0.5% volume) and micro-silica also reduced carbon emissions per unit strength of concrete.

The influence of two types of polymeric admixture on the stress–strain (σ-ε) behavior of cement mortar is discussed in this article. The characteristics of cement mortars modified with two types of polymeric admixtures containing 0.20 percent (% wt.) were investigated. For early age (1 day) and 28-day curing periods, the σ-ε behavior of modified cement mortar with polymeric admixtures was investigated. Depending on the polymeric structure and content, adding polymeric admixture reduced the water/cement ratio (w/c) by 14.5 to 25.5 %. When 0.20 % of polymeric admixture was added to cement mortar, the compression strength improved by 72 % to 153 %. The cement mortar samples became brittle as the polymeric admixtures content increased. The amorphous gel fills the spaces between cement particles and sand particles, decreasing voids and porosity and improving the density of the cement mortar. To forecast the connection of the modified cement mortar with polymer, the nonlinear Vipulanandan p-q equation was used, and it was compared to the Farazdaghi- Harris-YD and Sinusoidal models. The compressive stress–strain models were evaluated using statistical methods, and nonlinear approaches were utilized to predict compressive strength as a function of water/cement ratio, curing age, and polymer quantity. The less complicated model, the Vipulanandan p-q model, already has a smaller sum of squares than the other two models. Therefore, the Vipulanandan p-q model is the best one.

Owing to a high tensile strength and fatigue toughness, recycled tyre steel fiber (RSF) can be used as a potential fiber reinforcement to advance the ductility of high-performance concrete (HPC). In the present study, compressive strength, splitting-tensile strength, load-deflection behavior, flexural strength, flexural toughness, residual strength, and chloride-migration coefficient of HPC made with different doses of RSF was investigated. The performance of RSF was compared with manufactured steel fiber (MSF) at the same fiber volume. The results showed that in splitting-tensile test results, RSF was 54% to 75% effective as compared to MSF, while flexural-tensile results showed that RSF was 61% to 77% effective compared to MSF at the same fiber volume. Chloride permeability of both MSF and RSF-reinforced concrete was almost similar. The increase in electrical conductivity due to presence of metallic fibers caused a rise in the chloride permeability, however, the corrosion-potential risk of RSF-reinforced HPC was 'low'.

This research work proposed an economic and eco-efficient idea to supplement the ductility and durability of concrete by the simultaneous incorporation of several processed waste materials i.e., ground blast furnace slag (GBFS), recycled coarse aggregate (RCA), and coconut fibre (CF). Two concrete families were produced containing 0% and 100% coarse RCA. GBFS was incorporated as by 25% replacement for cement. CF was used as a fibre reinforcement at 0.25% volume fraction. The results revealed that the recycled-aggregate concrete (RAC) modified with GBFS, CF, and modified plasticizer dosage can attain similar or higher mechanical performance than unmodified natural-aggregate concrete (NAC). At a later age, the strength gain in RAC mixes due to 25% GBFS addition was found to be notably greater than that observed in NAC mixes. At 91 days, RAC containing 0.25% CF and 25% GBFS correspondingly showed 30.5% and 33% more flexural strength and splitting tensile strength, as compared to conventional plain NAC. GBFS inclusion can effectively eliminate the corrosion risk of medium-strength NAC and RAC and control the negative effects of CF incorporation on the durability of concrete. RAC with 0.25% CF and 25% GBFS yielded 1.5% and 16% lower water absorption than plain NAC, at 28 and 91 days, respectively. The modification of SP dosage can substantially enhance the strength and permeability-resistance of CF-reinforced RAC.