Predicting the Compressive Strength of the Cement-Fly Ash-Slag Ternary Concrete Using the Firefly Algorithm (FA) and Random Forest (RF) Hybrid Machine-Learning Method.

The paper investigated the effect of locally sourced fly ash and ground granulated blast-furnace slag (GGBS) on the compressive strength and chloride resistance of concrete. The mix proportion was cementitious material (total of ordinary Portland cement (OPC), fly ash and GGBS): sand: coarse aggregate: water of 1:2:3:0.6 in which 20% by mass of total cementitious materials was replaced by class F fly ash and GGBS. Compressive strength and rapid chloride penetration tests were conducted at 28, 56 and 120 days. The results shows that fly ash and GGBS reduce slightly the compressive strength but improve significantly the choloride resistance of concrete. Within the range of investigation, 10% of fly ash and 10% of GGBS are recommended to replace OPC as they improve the chloride resistance and maintain the compressive strength of concrete.

The integration of sustainable materials such as fly ash, blast-furnace slag, recycled aggregates, and seawater into concrete mixes offers significant potential for reducing the environmental impact of construction. However, traditional experimental methods for determining the compressive strength of these concrete mixes are time-consuming and resource-intensive. This study leverages Artificial Neural Networks (ANNs) and Machine Learning (ML) to develop a predictive model for the compressive strength of sustainable concrete, using a dataset of 768 concrete mix samples. Input variables include the concrete age as well as concrete composition, including cement, water, fine and coarse aggregates, seawater, fly ash, blast-furnace slag, and superplasticizer contents, while the output is the compressive strength. The developed model captures the non-linear relationships among these variables to predict compressive strength efficiently. The best ANN model achieved a test loss of 0.051, demonstrating its ability to accurately predict compressive strength and reduce reliance on traditional testing methods. Moreover, the model’s results were compared with those of alternative algorithms to ensure its validity. These findings highlight the potential of machine learning in advancing sustainable construction practices. A relevant future research direction is to analyze feature importance in machine learning models to identify key variables and guide more effective optimization and decision-making, in addition to extending their application to other material properties and advanced concrete mixes.

The efficacy of two machine learning algorithms to predict the concrete compressive strength is investigated in this research. For this objective, a vast amount of experimental data from numerous academic research articles was statically evaluated and modelled. In all, 265 observations were considered in this investigation 70% of the data was used for training, while the remaining 30% was used for testing. The data used in this paper was divided randomly. Cement, ground granulated blast furnace slag, limestone powder, fly ash, rice husk ash, fine aggregate, coarse aggregate, silica fume, superplasticizer, water, viscosity modifying admixture, and coarse aggregate were among the 11 input parameters employed in the study. The concrete compressive strength was taken as the output parameter of the model. To assess the models’ prediction abilities, correlation coefficient (CC), the mean absolute error (MAE) and root mean square error (RMSE) values were employed. From the results it was concluded that RF-based model outperforms the M5P Model. The CC, RMSE and MAE values for RF are 0.9867, 3.7887 and 2.9244 for training and 0.8668, 9.1723 and 5.8552 for testing stage respectively.

[ID] Concrete is a very popular building material used in the world of construction services, consisting of a mixture of Portland Cement (PC) or other hydraulic cement, fine aggregates, coarse aggregates and water, with or without using additional materials. The quality of materials such as cement also greatly affects the strength of the concrete after hardening, so the selection of cement quality must be in accordance with the concrete planning regulations in order to obtain optimal results. In Indonesia there are many new cement factories that produce to meet the needs of the community, one of which is the Conch brand cement. So in connection with the above, Conch cement can be examined to compare the value of compressive strength and flexural strength with old cement, namely cement Padang, Tiga Roda, Holcim and Bosowa which are generally always used in concrete planning at this time.The purpose of this study was to determine the comparison of compressive strength and flexural strength of the concrete and the multiplier between cement Padang, Three Wheels and Conch at 28 days of age. In this study using the method SNI 03-2834-2000. With cylindrical test specimens (150 mm x 300 mm) and size beams (150 mm x 150 mm x 600 mm) three specimens were made for each cement.The maximum concrete compressive strength is found in Padang cement with a compressive strength of 45.86 Mpa, for the minimum compressive strength found in Tiga Roda cement with compressive strength value of 40.19 Mpa and for the compressive strength of cement Conch there is a second with compressive strength value 42.84 Mpa. From the explanation above, the results of 28 days of concrete compressive strength with each cement brand still not reached the planned concrete compressive strength of 38 MPa. The maximum concrete flexural strength is found in Padang cement with a flexural strength value of 5.03 Mpa, for a minimum flexural strength value found in Tiga Roda cement with a flexural strength value of 3.96 Mpa and for the value of Conch cement compressive strength there is a second with flexural strength 4.43 Mpa. From the explanation above, the results of 28 days of concrete flexural strength with each cement brand that has not reached the 4.4 Mpa plan, namely the three-wheeled cement brand.
 [EN] Concrete is a very popular building material used in the world of construction services, consisting of a mixture of Portland Cement (PC) or other hydraulic cement, fine aggregates, coarse aggregates and water, with or without using additional materials. The quality of materials such as cement also greatly affects the strength of the concrete after hardening, so the selection of cement quality must be in accordance with the concrete planning regulations in order to obtain optimal results. In Indonesia there are many new cement factories that produce to meet the needs of the community, one of which is the Conch brand cement. So in connection with the above, Conch cement can be examined to compare the value of compressive strength and flexural strength with old cement, namely cement Padang, Tiga Roda, Holcim and Bosowa which are generally always used in concrete planning at this time.The purpose of this study was to determine the comparison of compressive strength and flexural strength of the concrete and the multiplier between cement Padang, Three Wheels and Conch at 28 days of age. In this study using the method SNI 03-2834-2000. With cylindrical test specimens (150 mm x 300 mm) and size beams (150 mm x 150 mm x 600 mm) three specimens were made for each cement.The maximum concrete compressive strength is found in Padang cement with a compressive strength of 45.86 Mpa, for the minimum compressive strength found in Tiga Roda cement with compressive strength value of 40.19 Mpa and for the compressive strength of cement Conch there is a second with compressive strength value 42.84 Mpa. From the explanation above, the results of 28 days of concrete compressive strength with each cement brand still not reached the planned concrete compressive strength of 38 MPa. The maximum concrete flexural strength is found in Padang cement with a flexural strength value of 5.03 Mpa, for a minimum flexural strength value found in Tiga Roda cement with a flexural strength value of 3.96 Mpa and for the value of Conch cement compressive strength there is a second with flexural strength 4.43 Mpa. From the explanation above, the results of 28 days of concrete flexural strength with each cement brand that has not reached the 4.4 Mpa plan, namely the three-wheeled cement brand.

The building industry, which emits a significant quantity of greenhouse gases, is under tremendous pressure due to global climate change and its consequences for communities. Given the environmental issues associated with cement production, geopolymer concrete has emerged as a sustainable construction material. Geopolymer concrete is an eco-friendly construction material that uses industrial or agricultural by-product ashes as the principal binder instead of Portland cement. Fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin, and palm oil fuel ash were all employed as binders in geopolymer concrete, with fly ash being the most frequent. The most important engineering property for all types of concrete composites, including geopolymer concrete, is the compressive strength. It is influenced by different parameters such as the chemical composition of the binder materials, alkaline liquid to binder ratio, extra water content, superplasticizers dosages, binder content, fine and coarse aggregate content, sodium hydroxide and sodium silicate content, the ratio of sodium silicate to sodium hydroxide, the concentration of sodium hydroxide (molarity), curing temperature, curing durations inside oven, and specimen ages. In order to demonstrate the effects of these varied parameters on the compressive strength of the fly ash-based geopolymer concrete, a comprehensive dataset of 800 samples was gathered and analyzed. According to the findings, the curing temperature, sodium silicate content, and alkaline solution to binder ratio are the most significant independent parameters influencing the compressive strength of the fly ash-based geopolymer concrete (FA-BGPC) composites.

Waste marble, an industrial byproduct generated from marble cutting and polishing processes, can be effectively utilized as a partial replacement in concrete mixtures. Incorporating waste marble in concrete not only addresses environmental concerns related to marble waste disposal but also contributes to the sustainability of construction materials. Using machine learning (ML) to predict the impact of waste marble on the compressive strength of traditional concrete offers several advantages over repeated laboratory experiments. ML offers a powerful alternative to costly and time-consuming laboratory experiments, enabling faster and more sustainable exploration of the potential of waste marble in improving concrete’s compressive strength. This research has focused on evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning (ML). Advanced ML techniques such as the Group Methods Data Handling Neural Network (GMDH-NN), Support Vector Regression (SVR), K-Nearest Neighbors (kNN) and Adaptive Boosting (AdaBoost) have been applied in this research work. The GMDH-NN model was created using GMDH Shell 3.0 software, while AdaBoost, SVR and kNN models were created using “Orange Data Mining” software version 3.36. Error indices such as the sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Error (%), and performance metrics such as Accuracy % and the R2 between predicted and calculated compressive strength parameters were used to evaluate the overall behavior of the models. Finally, the Hoffman sensitivity analysis procedure was applied to determine the individual relative impact of the input variables on the output. At the end of the processes, a total of 1135 waste marble concrete entries were collected containing constituents such as the cement density (C), waste marble (WM), fine aggregate (FAg), coarse aggregate (CAg), water (W), superplasticizer (PL) and curing age (Age) used as input variables of the waste marble concrete model. The records were divided into training set (900 records = 80%) and validation set (235 records = 20%) following standard partitioning pattern reported in the literature. The kNN and AdaBoost, with SSE of 1408.5 MPa2 and 1397 MPa2 respectively and a tie Accuracy of 95.5% and R2 of 0.985 showed the best models suggesting excellent model performance while GMDH-NN showed the worst. Conversely, RF balances accuracy and model complexity, making it a practical alternative to kNN and AdaBoost. And lastly, Age, Coarse Aggregates, Water, and Plasticizer play the most significant roles in determining the compressive strength, while Cement, Waste Marble, and Fine Aggregates have comparatively smaller impacts. However, considering the standard proportion required for waste marble powder to replace cement, it showed a remarkable influence on the behavior of the concrete thus a recommended potential for its used as replacement for cement.

Performance of sustainable concretes containing very high volume Class-F fly ash and ground granulated blast furnace slag

RETRACTED: The impact of GGBS and ferrous on the flow of electrical current and compressive strength of concrete

This paper investigates the compressive strength properties of concrete with Ground Granulated Blast Furnace Slag (GGBS) and Fly Ash in concrete by partial replacement of cement. The incremental demand of cement in the construction field is a concern for environmental degradation, in this regard; replacement of cement is carried out with waste materials by using GGBS and Fly Ash. On optimum level of GGBS and Fly Ash was assessed with varied percentage from 0 to 30% for different curing days. Replaced concrete were tested with the slump, compaction factor, Vee-bee and compressive strength. Cement to water ratio was maintained at 0.47 for all mixes. The compressive strength tests were conducted for 3, 7, 14 and 28 days of curing on a M25 grade concrete. The results obtained from the slump, compaction factor, Vee-bee and compressive strength of concrete containing GGBS and Fly Ash was increased as the curing time increases. The workability of replaced concrete improved when slump value achieved 30% as compared to controlled one SF0 and the compressive strength obtained 26.30% improvement at SF9 as compared to SF0. The outcomes indicated that the addition of GGBS and Fly Ash enhances the workability and compressive strength which eventually improved the mechanical properties of concrete.

An alternative method using Artificial Intelligence (AI) to predict the 28-day strength of concrete from its primary ingredients is presented in this research. A series of 424 data samples collected from a previous study were employed for developing, testing, and validation of Adaptive Neuro-Fuzzy Inference System (ANFIS) models. Seven mix parameters, namely Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, and Fine Aggregate were used as the inputs of the models while the output was the 28-day compressive strength of concrete. In the first step, different models with various input membership functions were explored and compared to obtain an optimal ANFIS model. In the second step, that model was utilized to predict the compressive strength value for each concrete sample, and to compare with those obtained from the compressive test in laboratory. The results showed that the selected ANFIS model can be used as a reliable tool for predicting the compressive strength of concrete with Root Mean Squared Error values of 5.97 MPa and 7.73 MPa, respectively, for the training and test sets. In addition, the sensitivity analysis results revealed that the accuracy of the proposed model improved with an increase in the number of input parameters/variables.
 Keywords:
 artificial intelligence; adaptive neuro-fuzzy inference system; concrete strength; sensitivity analysis.

This paper demonstrates the application of Random Forest (RF) algorithm for prediction of compressive strength of sustainable self-consolidating concrete (SCC) in which significant amount of cement was replaced with minerals such as fly ash, ground granulated blast furnace slag (GGBS), and silica fume. SCC improves the quality of the finished concrete product and is considered an environmentally friendly alternative to conventional concrete. RF proved capable of predicting compressive strength with high accuracy. The ability of RF algorithm to predict compressive strength established confidence on the experimental data itself which can be used for further studies on properties of self-consolidating concrete. The high level of accuracy in predicting essential engineering properties of concrete through RF algorithms offers important opportunities to enhance quality in ready mix production industry.

The effects of fly ash, Air Cooled Blast Furnace Slag (ACBFS), and silica fume on cement compressive strength are examined. The compressive strength of cement concrete is studied by the addition of fly ash, air-cooled blast furnace slag (ACBFS), and silica fume. The effect of fly ash, ACBFS, and silica fume on the compressive strength of concrete was investigated. The cement content of the concrete was reduced by 25%, 22.50%, 20%, and 17.50%, respectively; fly ash was used to replace 20%, 40%, and 60% of the coarse aggregate; and fly ash was partially replaced by 2.5%, 5%, and 7.50% of the cement content. Contrarily, fly ash was left out of the mixing process for M30. The compressive strength of concrete cubes and beams aged 7 and 28 days was tested.

Development of waste-based concretes containing foundry sand, recycled fine aggregate, ground granulated blast furnace slag and fly ash

The utilization of waste material, such as fly ash, in the concrete industry will provide a valuable alternative solution for creating an eco-friendly environment. However, experimental work is time-consuming; employing soft machine learning techniques can accelerate the process of forecasting the strength properties of concrete. Ensemble machine learning modeling using Python Jupyter Notebook was employed in the forecasting of compressive strength (CS) of high-performance concrete. Multilayer perceptron neuron network (MLPNN) and decision tree (DT) were used as individual learning which then ensembled with bagging and boosting to provide strong correlations. Random forest (RF) and gradient boosting regression (GBR) were also used for prediction. A total of 471 data points with input parameters (e.g., cement, fine aggregate, coarse aggregate, superplasticizer, water, days, and fly ash), and an output parameter of compressive strength (CS), were retrieved to train and test the individual learners. Cross-validation with K-fold and statistical error (i.e., MAE, MSE, RMSE, and RMSLE) analysis was applied to check the accuracy of all models. All models showed the best correlation with an ensemble model rather than an individual one. DT with AdaBoost and random forest gave a strong correlation of R2 = 0.89 with fewer errors. Cross-validation results revealed a good response with an error of less than 10 MPa. Thus, ensemble modeling not only trains the data by employing several weak learners but also produces a robust correlation that can then be used to model and predict the mechanical performance of concrete.

The alarming rate of depletion of natural stone based coarse aggregates is a cause of great concern. The coarse aggregates occupy nearly 60-70% by volume of concrete being produced. Research efforts are on to look for alternatives to stone based coarse aggregates from sustainability point of view. Response surface methodology (RSM) is adopted to study and address the effect of ferrochrome slag (FCS) replacement to coarse aggregate replacement in the ordinary Portland cement (OPC) based concretes. RSM involves three different factors (ground granulated blast furnace slag (GGBS) as binder, flyash (FA) as binder, and FCS as coarse aggregate), with three different levels (GGBS (0, 15, and 30%), FA (0, 15, and 30%) and FCS (0, 50, and 100%)). Experiments were carried out to measure the responses like, workability, density, and compressive strength of FCS based concretes. In order to optimize FCS replacement in the OPC based concretes, three different traditional optimization techniques were used (grey relational analysis (GRA), technique for order of preference by similarity (TOPSIS), and desirability function approach (DFA)). Traditional optimization techniques were accompanied with principal component analysis (PCA) to calculate the weightage of responses measured to arrive at the final ranking of replacement levels of GGBS, FA, and FCS in OPC based concretes. Hybrid combination of PCA-TOPSIS technique is found to be significant when compared to other techniques used. 30% GGBS and 50% FCS replacement in OPC based concrete was arrived at, to be optimal.