Fly Ash Application as Supplementary Cementitious Material: A Review

Biochar affects compressive strength of Portland cement composites: a meta-analysis

One strategy to reduce CO2 emissions from cement production is to reduce the amount of Portland cement produced by replacing it with supplementary cementitious materials (SCMs). Biochar is a potential SCM that is an eco-friendly and stable porous pyrolytic material. However, the effects of biochar addition on the performances of Portland cement composites are not fully understood. This meta-analysis investigated the impact of biochar addition on the 7- and 28-day compressive strength of Portland cement composites based on 606 paired observations. Biochar feedstock type, pyrolysis conditions, pre-treatments and modifications, biochar dosage, and curing type all influenced the compressive strength of Portland cement composites. Biochars obtained from plant-based feedstocks (except rice and hardwood) improved the 28-day compressive strength of Portland cement composites by 3-13%. Biochars produced at pyrolysis temperatures higher than 450 °C, with a heating rate of around 10 C min-1, increased the 28-day compressive strength more effectively. Furthermore, the addition of biochar with small particle sizes increased the compressive strength of Portland cement composites by 2-7% compared to those without biochar addition. Biochar dosage of < 2.5% of the binder weight enhanced both compressive strengths, and common curing methods maintained the effect of biochar addition. However, when mixing the cement, adding fine and coarse aggregates such as sand and gravel affects the concrete and mortar's compressive strength, diminishing the effect of biochar addition and making the biochar effect nonsignificant. We concluded that appropriate biochar addition could maintain or enhance the mechanical performance of Portland cement composites, and future research should explore the mechanisms of biochar effects on the performance of cement composites.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s42773-024-00309-2.

Feasibility of Using New Sustainable Mineral Additions for the Manufacture of Eco-Cements

Due to a continuously developing population, our consumption of one of the most widely used building materials, concrete, has increased. The production of concrete involves the use of cement whose production is one of the main sources of CO2 emissions; therefore, a challenge for today's society is to move towards a circular economy and develop building materials with a reduced environmental footprint. This study evaluates the possibility of using new sustainable supplementary cementitious materials (SCMs) from waste such as recycled concrete aggregates (RCAs) and mixed recycled aggregates (MRAs) from construction and demolition waste, as well as bottom ash from olive biomass (BBA-OL) and eucalyptus biomass ash (BBA-EU) derived from the production of electricity. A micronisation pre-treatment was carried out by mechanical methods to achieve a suitable fineness and increase the SCMs' specific surface area. Subsequently, an advanced characterisation of the new SCMs was carried out, and the acquired properties of the new cements manufactured with 25% cement substitution in the new SCMs were analysed in terms of pozzolanicity, mechanical behaviour, expansion and setting time tests. The results obtained demonstrate the feasibility of using these materials, which present a composition with potentially reactive hydraulic or pozzolanic elements, as well as the physical properties (fineness and grain size) that are ideal for SCMs. This implies the development of new eco-cements with suitable properties for possible use in the construction industry while reducing CO2 emissions and the industry's carbon footprint.

Potential use of fly ash in structural fill application: a review

Globally, over the years, fly ash (FA) has been successfully used in structural fills as a substitute for conventional infill material. As per the global industry trends and forecast report, the utilization rate of FA in 2021 was 74% in China, 65% in India, and 70% in the United States (US). Despite substantial research being done on the usage of FA as a substitute all over the world, only up to 15% by mass of total produce has been utilized as a replacement for infill soils. This indicates that there is a lot of potential for increased usage. From the view point of increasing the utilization rate, the present study focuses on summarizing the geotechnical properties of FA by taking strength characteristics into account as compared to conventional infill material. Moreover, this review underlines the chemical composition, index, and engineering properties. Firstly, it reviews the current state of the application of FA in structural fills by considering 141 articles that have been published since 2004 to till date. Secondly, it emphasizes the limited literature available on structural fill applications of FA. It also recommends the classification of FA besides the existing ASTM codes. Moreover, considering future research, this review also highlights the gaps in the previous studies, such as the need for amendments in existing standard codes for FA utilization as structural fill.

Empirical models for compressive and tensile strength of basalt fiber reinforced concrete

When molten magma solidifies, basalt fiber (BF) is produced as a byproduct. Due to its remaining pollutants that could affect the environment, it is regarded as a waste product. To determine the compressive strength (CS) and tensile strength (TS) of basalt fiber reinforced concrete (BFRC), this study will develop empirical models using gene expression programming (GEP), Artificial Neural Network (ANN) and Extreme Gradient Boosting (XG Boost). A thorough search of the literature was done to compile a variety of information on the CS and TS of BFRC. 153 CS findings and 127 TS outcomes were included in the review. The water-to-cement, BF, fiber length (FL), and coarse aggregates ratios were the influential characteristics found. The outcomes showed that GEP can accurately forecast the CS and TS of BFRC as compared to ANN and XG Boost. Efficiency of GEP was validated by comparing Regression (R2) value of all three models. It was shown that the CS and TS of BFRC increased initially up to a certain limit and then started decreasing as the BF % and FL increased. The ideal BF content for industrial-scale BF reinforcement of concrete was investigated in this study which could be an economical solution for production of BFRC on industrial scale.

Effects of different supplementary cementitious materials on durability and mechanical properties of cement composite - Comprehensive review

Ordinary Portland cement is the highest produced cement type in the world, however its production is high energy consumption means expensive, huge natural resource consumptive, and creating high environmental pollution. Hence many researchers studied to reduce the effect of ordinary Portland cement by substituting artificial and natural supplementary cementitious materials (SCMs) commonly in a concrete/mortar mixture. However, the comprehensive effect of different SCMs on various properties of cement composite materials are not well known. So the present study sought to review the effect of different natural and artificial SCMs on the durability and mechanical properties of cement composites, especially due to their doses, types, chemical composition, and physical properties. Hence the review shows that many SCMs used by literatures from different places satisfy ASTM replacement standard based on their chemical compositions. Also, the review indicated as adding 5-20% of different SCMs positively affect mechanical properties, durability, and microstructures of the cement composite materials, specifically as most researchers found isolately adding of 15% SCMs such as bentonite, kaolin, and biomass, 20% addition of volcanic ash and 10% employment of fly ash, silica fume, and zeolite to the cement composites achieves the most optimum compressive and split tensile strength. These observations reveal that most natural pozzolana can more replace cement to give optimum strength, hence can more reduce energy consumption, production cost, and environmental pollution comes due to cement production. Furthermore, most researchers found employing different SCMs generally improves durability, however there is a limited study on the effect of silica fume on water absorption and acidic attack resistance of cementitious materials. Therefore, it is recommended that future research should also focus more to know the effect of silica fume on the durability of cement composites.

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R²), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials.

Effect of Particle Size and Morphology of Siliceous Supplementary Cementitious Material on the Hydration and Autogenous Shrinkage of Blended Cement

Supplementary cementitious material (SCM) plays an important role in blended cement, and the effect of the particle size and morphology of siliceous supplementary cementitious material on hydration should not be ignored. In this study, 0.5 h and 1 h of wet grinding was applied to pretreat iron ore tailing powder (TP), and the divergence in pozzolanic behavior and morphology were investigated. Then, the treated TPs were used to replace the 30% cement contents in preparing blended cementitious paste, and the impact mechanism of morphology on performance was studied emphatically. M, the autogenous shrinkages of pastes were tested. Finally, hydration reaction kinetics was carried out to explore the hydration behavior, while X-ray diffraction (XRD) and thermogravimetric analysis (TGA) were used to characterize the hydration product properties, respectively. Meanwhile, microscopy intrusion porosimetry (MIP) was also carried out to characterize the pore structures of hardened specimens. Results indicated that wet grinding has a dramatic effect on particle size and morphology, but hardly affects the phase assemblages and pozzolanic reactivity of TP, while the particle shape of TP changes from sub-circular to clavate and, finally, back to sub-circular. The results of hydration reaction kinetics, representing the morphology of particles, had a significant effect on hydration rate and total heat, and compared with the sub-circle one, the clavated particle could inhibit the hydration procedure. With the increasing grinding time, the compressive strength of cementitious paste was increased from 17.37% to 55.73%, and the micro-pore structure became denser; however, the autogenous shrinkage increased.

Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches' results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

Evaluating the compressive strength of glass powder-based cement mortar subjected to the acidic environment using testing and modeling approaches

This study conducted experimental and machine learning (ML) modeling approaches to investigate the impact of using recycled glass powder in cement mortar in an acidic environment. Mortar samples were prepared by partially replacing cement and sand with glass powder at various percentages (from 0% to 15%, in 2.5% increments), which were immersed in a 5% sulphuric acid solution. Compressive strength (CS) tests were conducted before and after the acid attack for each mix. To create ML-based prediction models, such as bagging regressor and random forest, for the CS prediction following the acid attack, the dataset produced through testing methods was utilized. The test results indicated that the CS loss of the cement mortar might be reduced by utilizing glass powder. For maximum resistance to acidic conditions, the optimum proportion of glass powder was noted to be 10% as cement, which restricted the CS loss to 5.54%, and 15% as a sand replacement, which restricted the CS loss to 4.48%, compared to the same mix poured in plain water. The built ML models also agreed well with the test findings and could be utilized to calculate the CS of cementitious composites incorporating glass powder after the acid attack. On the basis of the R2 value (random forest: 0.97 and bagging regressor: 0.96), the variance between tests and forecasted results, and errors assessment, it was found that the performance of both the bagging regressor and random forest models was similarly accurate.

Biomass Bottom Ash as Supplementary Cementitious Material: The Effect of Mechanochemical Pre-Treatment and Mineral Carbonation

The need to mitigate the CO₂ emissions deriving from the cement industry becomes imperative as the climate crisis advances. An effective strategy to achieve this is increasing the replacement level of cement clinkers by waste-derived supplementary cementitious materials (SCMs). In this study, the use of mechanochemically activated biomass ash for high-volume (up to 40%) substitution of cement is investigated. The effect of mineral carbonation treatment on the performance of the mechanochemically treated biomass ash as SCM was also examined. The results showed that the mechanochemically treated biomass ash was the most effective SCM, with the respective samples at 40% cement replacement reaching 63% of the strength at 28 days as compared to samples with 100% Portland cement, while only 17% of the strength was achieved in samples with 40% untreated biomass ash. As suggested by the isothermal calorimetry, XRD, FTIR, and TG analysis, the mechanochemical treatment enhanced the reactivity and the filler effect of the biomass ash, leading to improved mechanical performances of these mortars compared to those containing untreated biomass ash. Mineral carbonation reduced the reactivity of the mechanochemically treated biomass ash but still led to better strength performances in comparison to the untreated biomass ash.

Effective Microorganisms and Glass Nanopowders from Waste Bottle Inclusion on Early Strength and Microstructure Properties of High-Volume Fly-Ash-Based Concrete

In concrete production, the use of high-volume fly ash (FA) as the cement substitute became interesting to achieve more sustainable and eco-friendly construction materials. However, concrete produced using high volumes of FA as cement substitute suffers from various limitations such as low strength at early ages. Considering the engineering solutions and economy of FA-included concrete, it has become vital to address such issues. In this perception, some concrete mixes were designed using more abundant and low-cost local waste materials such as waste glass bottle nanopowders (WGBNPs) and effective microorganisms (EMs) to determine the feasibility of compensating for the strength loss at early ages due to FA inclusion. The proposed mixes contained 10% of EMs as water replacement, 50% of FA, and various percentages of WGBNPs as cement replacement. The effects of EMs and WGBNPs inclusion on the early strength and microstructure properties of the produced FA-based concrete mixes were determined. The results show that the strength indexes of the concrete at all test ages were improved due to WGBNP and EM incorporation. At almost all curing ages, the mechanical performance of the concrete made with 10% EMs and 4% WGBNPs was comparable to that of normal concrete (control mix), wherein the mix containing 6% WGBNPs outperformed the control mix. The microstructure analysis of the studied mixes revealed an increase in the hydration products, structural compactness, and homogeneity due to the synergy of WGBNPs and EMs, especially the specimen made using 10% EMs and 6% WGBNPs. It is established that the proper utilization of EMs and WGBNPs in FA-based concrete can be beneficial for waste recycling and landfill problems, thus lowering environment pollution.

Evaluating the Strength and Impact of Raw Ingredients of Cement Mortar Incorporating Waste Glass Powder Using Machine Learning and SHapley Additive ExPlanations (SHAP) Methods

This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R²), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

A Scientometric-Analysis-Based Review of the Research Development on Geopolymers

A scientometric-based assessment of the literature on geopolymers was conducted in this study to determine its critical aspects. Typical review studies are restricted in their capability to link disparate segments of the literature in a systematic and exact way. Knowledge mapping, co-citation, and co-occurrence are very difficult components of creative research. This study adopted an advanced strategy of data mining, data processing and analysis, visualization and presentation, and interpretation of the bibliographic data on geopolymers. The Scopus database was used to search for and retrieve the data needed to complete the study’s objectives. The relevant sources of publications, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas actively engaged in the research of geopolymers are recognized during the data assessment. The VOSviewer (VOS: visualization of similarities) software application was employed to analyze the literature data comprising citation, bibliographic, abstract, keywords, funding, and other information from 7468 relevant publications. In addition, the applications and restrictions associated with the use of geopolymers in the construction sector are discussed, as well as possible solutions to overcome these restrictions. The scientometric analysis revealed that the leading publication source (journal) in terms of articles and citations is “Construction and building materials”; the mostly employed keywords are geopolymer, fly ash, and compressive strength; and the top active and contributing countries based on publications are China, India, and Australia. Because of the quantitative and graphical representation of participating nations and researchers, this study can help academics to create collaborative efforts and exchange creative ideas and approaches. In addition, this study concluded that the large-scale usage of geopolymer concrete is constrained by factors such as curing regime, activator solution scarcity and expense, efflorescence, and alkali–silica reaction. However, embracing the potential solutions outlined in this study might assist in boosting the building industry’s adoption of geopolymer concrete.

Progress in Sustainability and Durability of Concrete and Mortar Composites

The origins of concrete as a construction material date back more than 2000 years ago, but the origins of the term itself are still under debate due to its many different interpretations throughout history [...]

Enhanced Eco-Friendly Concrete Nano-Change with Eggshell Powder

One of the unifying factors for all countries is the large consumption of chicken, and other, eggs in food and other types of economic activity. After using various types of eggs for their intended purpose, a large amount of waste accumulates in the form of eggshells. Currently, this problem exists and needs a non-trivial, original solution. The aim of the work was to fill the scientific gap in the direction of studying the microstructure formation of improved nano-modified environmentally-friendly concrete based on eggshell powder and obtaining a concrete composition for the manufacture of an industrial sample of such a material. An environmentally-friendly concrete was obtained, the characteristics of which were improved relative to standard concrete by modifying it with eggshell powder, for which the optimal dosage was determined. The most effective was the replacement of part of the cement with eggshell powder in the amount of 10%. The maximum increase in strength characteristics ranged from 8% to 11%. The modulus of elasticity increased by 4% compared to the control samples without eggshell powder. The maximum reduction in deformations under axial compression and tension in comparison with the control values ranged from 5% to 10%. The study of the composite’s microstructure nano-modified with eggshell powder, and an analysis of the changes occurring in this microstructure due to nano-modification, confirmed the improvement in characteristics and the optimal dosage of eggshell powder.

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.

Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters

Geopolymers might be the superlative alternative to conventional cement because it is produced from aluminosilicate-rich waste sources to eliminate the issues associated with its manufacture and use. Geopolymer composites (GPCs) are gaining popularity, and their research is expanding. However, casting, curing, and testing specimens requires significant effort, price, and time. For research to be efficient, it is essential to apply novel approaches to the said objective. In this study, compressive strength (CS) of GPCs was anticipated using machine learning (ML) approaches, i.e., one single method (support vector machine (SVM)) and two ensembled algorithms (gradient boosting (GB) and extreme gradient boosting (XGB)). All models' validity and comparability were tested using the coefficient of determination (R2), statistical tests, and k-fold analysis. In addition, a model-independent post hoc approach known as SHapley Additive exPlanations (SHAP) was employed to investigate the impact of input factors on the CS of GPCs. In predicting the CS of GPCs, it was observed that ensembled ML strategies performed better than the single ML technique. The R2 for the SVM, GB, and XGB models were 0.98, 0.97, and 0.93, respectively. The lowered error values of the models, including mean absolute and root mean square errors, further verified the enhanced precision of the ensembled ML approaches. The SHAP analysis revealed a stronger positive correlation between GGBS and GPC's CS. The effects of NaOH molarity, NaOH, and Na₂SiO₃ were also observed as more positive. Fly ash and gravel size: 10/20 mm have both beneficial and negative impacts on the GPC's CS. Raising the concentration of these ingredients enhances the CS, whereas increasing the concentration of GPC reduces it. Gravel size: 4/10 mm has less favorable and more negative effects. ML techniques will benefit the construction sector by offering rapid and cost-efficient solutions for assessing material characteristics.

Nano-Silica-Modified Concrete: A Bibliographic Analysis and Comprehensive Review of Material Properties

Several review studies have been performed on nano-silica-modified concrete, but this study adopted a new method based on scientometric analysis for the keywords’ assessment in the current research area. A scientometric analysis can deal with vast bibliometric data using a software tool to evaluate the diverse features of the literature. Typical review studies are limited in their ability to comprehensively and accurately link divergent areas of the literature. Based on the analysis of keywords, this study highlighted and described the most significant segments in the research of nano-silica-modified concrete. The challenges associated with using nano-silica were identified, and future research is directed. Moreover, prediction models were developed using data from the literature for the strength estimation of nano-silica-modified concrete. It was noted that the application of nano-silica in cement-based composites is beneficial when used up to an optimal dosage of 2–3% due to high pozzolanic reactivity and a filler effect, whereas a higher dosage of nano-silica has a detrimental influence due to the increased porosity and microcracking caused by the agglomeration of nano-silica particles. The mechanical strength might enhance by 20–25% when NS is incorporated in the optimal amount. The prediction models developed for predicting the strength of nano-silica-modified concrete exhibited good agreement with experimental data due to lower error values. This type of analysis may be used to estimate the essential properties of a material, therefore saving time and money on experimental tests. It is recommended to investigate cost-effective methods for the dispersion of nano-silica in higher concentrations in cement mixes; further in-depth studies are required to develop more accurate prediction models to predict nano-silica-modified concrete properties.

Study on Properties and Optimization of Ternary Auxiliary Cementing Materials for IOTs

In order to control energy consumption and reduce pollution, the use of supplementary cementitious materials (SCMs) instead of cement to produce green cementitious materials can save energy, reduce emissions and achieve sustainable development. This study demonstrates the possibility of developing SCMs with iron tailings (IOTs), fly ash (FA) and ceramic powder (CP) ternary system, as well as the optimization and improvement scheme of gelation activation. The effects of activator dosage, mix ratio and substitution rate on mechanical properties of ternary SCMs system were investigated. The formation and evolution of hydration products were analyzed by differential thermogravimetric analysis (DTA) and scanning electron microscopy (SEM). The results of the study show that there is synergy in the system. The results show that there is synergy in the system and the hydration reaction is sufficient. At the substitution rate of 30%, the doping ratio of IOTs, CP and FA is 1:2:2 and the Ca(OH)₂ is 0.6%, the strength reaches 39.9 MPa and the activity index is 91.5%, which can provide a basis for the application and more in-depth study of IOTs multi SCMs.

Comparative Study of Experimental and Modeling of Fly Ash-Based Concrete

The application of supplementary cementitious materials (SCMs) in concrete has been reported as the sustainable approach toward the appropriate development. This research aims to compare the result of compressive strength (C-S) obtained from the experimental method and results estimated by employing the various modeling techniques for the fly-ash-based concrete. Although this study covers two aspects, an experimental approach and modeling techniques for predictions, the emphasis of this research is on the application of modeling methods. The physical and chemical properties of the cement and fly ash, water absorption and specific gravity of the aggregate used, surface area of the cement, and gradation of the aggregate were analyzed in the laboratory. The four predictive machine learning (PML) algorithms, such as decision tree (DT), multi-linear perceptron (MLP), random forest (RF), and bagging regressor (BR), were investigated to anticipate the C-S of concrete. Results reveal that the RF model was observed more exact in investigating the C-S of concrete containing fly ash (FA), as opposed to other employed PML techniques. The high R2 value (0.96) for the RF model indicates the high precision level for forecasting the required output as compared to DT, MLP, and BR model R² results equal 0.88, 0.90, and 0.93, respectively. The statistical results and cross-validation (C-V) method also confirm the high predictive accuracy of the RF model. The highest contribution level of the cement towards the prediction was also reported in the sensitivity analysis and showed a 31.24% contribution. These PML methods can be effectively employed to anticipate the mechanical properties of concretes.