Compressive Strength Of Fly Ash Research Articles

A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete

Compressive strength (CS) of concrete is one of the most important factors in the construction industry and various time and effort-consuming tasks are required to measure it. To tackle such problems, the use of machine learning (ML), a branch of artificial intelligence, has recently resulted in a dramatic revolution in the construction sector, resulting in increased efficiency, accuracy, and creativity. Taking these factors into consideration, the current research was conducted on concrete manufactured with recycled coarse aggregate and fly ash generated as a byproduct of construction and demolition activities and thermal power plants. A large dataset consisting of 444 data points, along with ten input parameters, has been collected from the literature to forecast the CS of fly ash and recycled coarse aggregate-based self-compacting concrete. In this regard, ten advanced ML models, including K-Nearest Neighbors (KNN), Extra Tree Regressor (ETR), Bagging Regressor (BR), Adaboost Regressor (AR), Extreme Gradient Boosting (XGB), Linear Regression (LR), Random Forest (RF), Decision Tree Regression (DTR), Support Vector Regression (SVR) and Gradient Boosting Regression (GBR) have been considered. Furthermore, various data visualization plots and model’s performance matrices such as scatter plot, histograms, heatmaps, Shapley Additive Explanation (SHAP) Analysis, Regression Error Characteristics (REC), and errors have been utilized. In order to evaluate the most influential input parameter and depict the overall performance of ML models, sensitivity analysis and Taylor’s diagram are used. As a method of validation, the Kfold cross-validation approach has been implemented to justify the obtained output. Based on the outcome of the study, the BR model has displayed remarkable accuracy with insignificant errors and high R-squared values (R2 = 0.961), while XGB (R2 = 0.959), and DTR (R2 = 0.952) models also achieved commendable, as compared to other ML models. Additionally, water content, curing days, fly ash, and w/c ratio were found to be the most critical components that directly impact the CS of fly ash and RCA-based SCC. To cater to diverse and extensive practices, a graphical user interface has been developed to assist researchers and engineers in getting instant results of their fly ash and RCA-based SCC mixes prior to the execution of time- and resource-consuming laboratory work.

Predictive modelling of compressive strength of fly ash and ground granulated blast furnace slag based geopolymer concrete using machine learning techniques

Ordinary Portland cement (OPC) is proving to be hazardous to the environment. To replace the OPC, geopolymers (GPs) are introduced. However, to fully replace the OPC by GPs extensive laboratory tests are required to assess the long-term and short-term properties of GPs in different scenarios. Given the shortage of time for performing such extensive testing, artificial intelligence (AI) is used to analyze the properties of GPs. In this study, different AI techniques such as artificial neuro network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) are used to obtain the predictive models for estimating the compressive strength of fly ash and ground granulated blast furnace slag-based GP concrete. Different statistical parameters are used to evaluate the performance of predictive models. Similarly, sensitivity and parametric analysis are also conducted on the input parameters. Additionally, multiple linear regression was also performed on the whole database. After comparing all the results, it was concluded that GEP is the best AI technique to predict the compressive strength of GP-based concrete.

Compressive strength of fly ash based geopolymer utilizing waste completely decomposed granite

An attempt to recycle construction waste soils in southern cities of China was presented in this study, where completely decomposed granite (CDG) was utilized to replace fly ash in the production of environment-friendly low-carbon geopolymers. Five crucial parameters were identified and incorporated into the experimental design using the Taguchi method, including fine and gravel particle content of CDG, fly ash content, water-binder ratio, and curing temperature. Test results demonstrate that the newly formulated geopolymer can achieve a compressive strength of up to 14 MPa with a relatively high usage of CDG. The strength initially increases and then stabilizes with an increase in fine particle content. However, no significant relationship is observed between the compressive strength and gravel particle content. Higher curing temperatures and greater fly ash content lead to increased strength, while the strength initially decreases and then slightly increases with changes in the water-binder ratio. To examine the significance of these factors, Analysis of variance (ANOVA) were conducted and a correlation heatmap was drawn, revealing the following order of significance: curing temperature > fly ash content > water-binder ratio > fine particle content > gravel particle content. Furthermore, a robust strength prediction model was modified through multiple regression analysis. Another two additional groups of tests were performed to validate the proposed model, demonstrating the model's strong performance, particularly for curing ages beyond 14 days.

Hybrid Cements and Construction Elements Based on Alkaline Activation with Sodium Sulfates from Fly Ash and Construction and Demolition Waste.

This article demonstrates the possibility of producing hybrid cementitious materials (pastes, mortars, concretes, and precast elements) based on fly ash (FA) and construction and demolition wastes (CDW) using alkaline activation technology. Sodium sulfate was used as an activator and fine and coarse aggregates were obtained from CDW residues. An addition of Portland cement (OPC) (10 to 30%) allowed for improvement in the mechanical behavior of the hybrid cements and them to be cured at room temperature (25 °C). The FA and CDW cementitious materials obtained compressive strengths of 37 MPa and 32 MPa, respectively. The compressive strength of FA and CDW alkali-activated concretes at 28 days of curing was 22 MPa and 18 MPa, respectively, which identifies them as structural concretes according to NSR-10 title C in Colombia. The potential use of these concretes was validated by obtaining and classifying precast materials.

Microstructural Analysis and Compressive Strength of Fly Ash and Petroleum Sludge Ash Geopolymer Mortar under High Temperatures

The development of sustainable building materials and construction to decrease environmental pollution in both production and operational stages of the materials’ life cycle is appealing to great interest in the construction industries worldwide. This study evaluated the negative effect of temperature up to 1000 °C on the compressive strength and microstructure of fly ash and petroleum sludge ash (PSA) geopolymer mortar. A sodium silicate and sodium hydroxide mixture is used as an activator. The synthesized mortar was investigated using X-ray Diffraction (XRD), Fourier Transformation Infrared Spectroscopy (FTIR), Mercury Intrusion Porosimetry (MIP), and Field Emission Scanning Electron Microscopy (FESEM). As the temperature increased, the compressive strength of the geopolymer mortar decreased. The strength degradation is due to the damage to microstructure because of the temperature-induced dehydroxylation, dehydration thermal incompatibility between geopolymer aggregate and paste of geopolymer mortar at high temperatures. With an increase in temperature, the cumulative pore volume increased. The FESEM image showed the decomposition of the geopolymer matrix started at a temperature of 600 °C. Incorporating PSA in geopolymer mortar could result in an eco-friendly and sustainable environment that may reduce the problems associated with sludge disposal.

Experimental and Numerical Investigation of the 28-Days Compressive Strength of Fly Ash Based Geopolymer Concrete

Experimental and Numerical Investigation of the 28-Days Compressive Strength of Fly Ash Based Geopolymer Concrete

The effectuation of seawater on the microstructural features and the compressive strength of fly ash blended cement at early and later ages

AbstractThe current work scrutinizes the effectuation of seawater on morphological properties, pore structure, and compressive strength during the hydration process of fly ash blended cement at 3, 7, 28, 56, and 90 days to better understand the influence of salinity conditions of seawater on the microstructural modification and strength development of the hydration products as well as the total porosity. The chemical reaction's mechanism of mightily soluble salts, for example, Mg2SO4 and NaCl, with hydrated fly ash and blended cement (calcium‐bearing phases) was also confirmed. Fourier‐transform infrared spectroscopy has been appointed to observe and characterize the energetics of variation in the formulation of portlandite (CH), calcium silicate hydrate, gypsum (Gy), ettringite (AFt), and calcium chloroaluminate (Friedel's salt [FS]) throughout the hydration process of fly ash blended cement with seawater in comparison with deionized water. X‐ray diffraction analysis exposed that the peak intensities of FS, portlandite, and some particular phases of the hydrated fly ash blended cement in seawater are higher and sharper than the comparable peaks in deionized water. Mercury intrusion porosimetry‐measurements have been appointed that the total porosity of artificial seawater (ASW) was decreased from 28.9% at 3 days to 19.4% at 56 days. In addition, the average, median, and critical pore diameter were decreased in ASW while compared to deionized water (DIW). The reaction products of this work were also characterized using scanning electron microscopy, EDS, compressive strength, and isothermal calorimeter.

Conventional and Ensemble Machine Learning Models to Predict the Compressive Strength of Fly Ash Based Geopolymer Concrete

Cement is the primary component of concrete, a material that is widely used in the construction world. Due to the release of numerous gases, cement production and its use harm the environment. To mitigate this negative impact, the researchers have developed a solution named “Geopolymer Concrete (GPC)”. Among the various mechanical properties of concrete, the Compressive Strength (CS) of concrete is regarded as one of the crucial criteria in many design codes and standards and its dependency is highly non-linear on its ingredients including supplementary cementitious materials (SCMs). SCMs are pozzolanic materials, and their use in concrete results in the enhancement of its properties. Among the various SCMs, Fly Ash (FlA) is one of the most effective and sustainable material to improve concrete's CS. So, to predict the influence of the materials used along with FlA on the CS of GPC, either it requires the extensive experiments, requiring money and time or use soft computing techniques. With the help of various soft computing techniques, a more reliable model for the prediction of CS was developed, under which the assets can be saved. Also, with the help of these models for CS, we can receive recommendations for how to schedule the construction process and remove the formwork. In this study, the two Conventional Machine Learning (CML) models as Linear Regression, Artificial Neural Network and AdaBoost as an Ensemble Machine Learning (EML) model were developed to predict the CS of FlA based GPC. On comparison, between the developed models, AdaBoost model was found to be the most effective for an accurate CS prediction with Correlation coefficient (R2) as 0.944, Root Mean Squared Error (RMSE) as 2.506 and Mean Absolute Error (MAE) as 1.259. Linear Regression model was deemed to be inferior with R2 as 0.701, RMSE as 5.805 and MAE as 4.502.

Effect of slag and solid activator on flowability and compressive strength of fly ash based one-part geopolymer pastes

The geopolymerization process has led to the transformation of industrial by-products into sturdy and long-lasting construction materials, such as geopolymer binders, which can be used to mitigate the massive CO2 emissions associated with the production of Ordinary Portland Cement (OPC). These binders are produced from aqueous solutions of alkali activators and alumina and silica rich industrial waste materials. Strong, caustic, and viscous aqueous solutions are used in alkali activation. Its handling, usability, and mass production are all tough, even transport and site difficulties compound these issues. The solid alumina-silica rich components, solid alkali activators, and free water are dry mixed in this work to create a unique “one-part” or “simply add water” geopolymer binder that is equivalent to OPC in its manufacture. Researchers looked at the flowability and compressive strength properties of fly ash based one-part geopolymer mixes while adding ground granulated blast furnace slag and a solid activator (anhydrous sodium metasilicate powder). At the 25 and 50% replacement levels, GGBS was used in place of fly ash. Solid activator content varied from 8 to 16% at an interval of 2% for each replacement level of GGBS. Microstructural and mineralogical alterations were analyzed using scanning electron microscopy (SEM) and X-ray diffraction (XRD), respectively. According to the findings of the tests, the flowability and compressive strength improved with decreasing slag and solid activator concentration. It was found that activator content increments beyond 12% result in minor reduction in compressive strength, and that the highest compressive strength was measured at 50% GGBS and 12% activator content. Both flowability and compressive strength were improved by the 50% GGBS and 12% activator mixture, which also displayed symptoms of having a dense and compacted microstructure.

Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach

ABSTRACT This study presents a prediction model for estimating the compressive strength of pervious concrete through the utilisation of machine learning techniques. The models were trained and tested using 437 datasets sourced from published literature. This work employed a collection of six machine learning algorithms as statistical evaluation tools to determine the optimal and dependable model for forecasting the compressive strength of pervious concrete. Out of all the models considered, the eXtreme Gradient Boosting model had greater performance in predicting the compressive strength. The coefficient of determination value for the train data is 0.99, indicating a strong correlation between the predicted and actual values. The root mean squared error for the train data is 0.86 MPa, representing the average deviation between the predicted and measured values. Similarly, the coefficient of determination value for the test datasets is determined to be 0.95, accompanied by a root mean squared error of 2.53 MPa. The eXtreme Gradient Boosting model's sensitivity analysis findings suggest that the aggregate size is the greatest parameter on forecasting the compressive strength of pervious concrete. This study delivers a systematic assessment of the compressive strength of pervious concrete, contributing to the current knowledge base and practical implementation in this field.

Research on prediction of compressive strength of fly ash and slag mixed concrete based on machine learning.

Every year, a large amount of solid waste such as fly ash and slag is generated worldwide. If these solid wastes are used in concrete mixes to make concrete, it can effectively save resources and protect the environment. The compressive strength of concrete is an essential indicator for testing its quality, and its prediction is affected by many factors. It is difficult to predict its strength accurately. Therefore, based on the current popular machine learning supervised learning algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVR), three models established a nonlinear mapping between multi-factor features and target feature concrete compressive strength. Using the three completed training models, we validated the test set with 206 example sets, and the Root Mean Square Error (RMSE), fitting coefficient (R2), and Mean Absolute Error (MAE) were used as evaluation metrics. The validation results showed that the values of RMSE, R2, and MAE for the RF model were 0.1, 0.9, and 0.21, respectively; the values of XGBoost model were 0.05, 0.95, and 0.15, respectively. The values of SVR were 0.15, 0.86, and 0.3, respectively. As a result, Extreme Gradient Boosting (XGBoost) has better generalization ability and prediction accuracy than the other two algorithms.

Comparison of Compressive Strength of Fly Ash Bonded Flue Dust Briquettes with that of Briquettes made with Bentonite, Sodium Silicate, Lime, Starch, Pitch, Molasses and Dextrin Binder

Comparison of Compressive Strength of Fly Ash Bonded Flue Dust Briquettes with that of Briquettes made with Bentonite, Sodium Silicate, Lime, Starch, Pitch, Molasses and Dextrin Binder

Compressive Strength Prediction of Fly Ash Concrete Using Machine Learning Techniques

It is time-consuming and uneconomical to estimate the strength properties of fly ash concrete using conventional compression experiments. For this reason, four machine learning models—extreme learning machine, random forest, original support vector regression (SVR), and the SVR model optimized by a grid search algorithm—were proposed to predict the compressive strength of fly ash concrete on 270 group datasets. The prediction results of the proposed model were compared using five evaluation indices, and the relative importance and effect of each input variable on the output compressive strength were analyzed. The results showed that the optimized hybrid model showed the best predictive behavior compared to the other three models, and can be used to forecast the compressive strength of fly ash concrete at a specific mix design ratio before conducting laboratory compression tests, which will save costs on the specimens and laboratory tests. Among the eight input variables listed, age and water were the two relatively most important features with superplasticizer and fly ash being of weaker relative importance.

Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model.

This article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were collected and analysed according to ten input variables (all relevant mix-design parameters) and the output variable (cylindrical compressive strength). The developed optimal FLNN model proved to be a powerful tool for predicting the compressive strength of FA geopolymer concrete with a small range of mean squared error (MSE = 10.4 and 15.0), a high correlation coefficient with the actual values (R = 96.0 and 97.5) and a relatively small root mean squared error (RMSE = 3.22 and 3.87 MPa) for the training and testing data, respectively. Based on the optimised model, a powerful design chart for determining the mix-design parameters of FA geopolymer concretes was generated. It is applicable for both one- and two-part geopolymer concretes, as it takes a wide range of mix-design parameters into account. The design chart (with its relatively small error) will ensure cost- and time-efficient geopolymer production in future applications.

Statistical analysis of fly ash and slag blended geopolymer concrete

Geopolymer concrete (GPC) allows a complete replacement of cement clinker with industrial wastes, contributing to a reduction in global carbon footprint. The recent evolution of GPC cured at ambient temperatures, widens its suitability and applicability to concrete-based structures. To improve the mix design method of such a material, in this study, a predictive model for the compressive strength of fly ash and slag blended GPC was developed using non-linear regression analysis, as a machine learning tool, on a database of 156 samples, aggregated from the literature. Statistical investigations were carried out on the influence of parameters, identified from existing literature while developing the model. 75% of the data were randomly chosen to train the model while the remaining 25% was used to test the efficacy of the proposed model. Root Mean Squared Error (RMSE) of the predictions on the training and testing datasets and R-squared value of the model based on training data were used as criteria to select the model. The proposed model shows RMSE of 6.3 MPa, and 6.7 MPa, respectively, for the training and test datasets. The statistical investigation reveals that the compressive strength is significantly affected by the combined effect of the proportion of Na2SiO3-to-NaOH ratio, activator-to-binder ratio, Silica-to-Alumina (Si-to-Al) ratio, Lime-to-Silica (Ca-to-Si) ratio, and the curing temperature. From the prediction of the proposed model, it was found that the compressive strength increases with the increase in Ca-to-Si ratio and curing temperature while it decreases with the increase in the activator-to-binder ratio. Further, the model prediction indicates that the Fly ash and Slag blended GPC can reach a compressive strength of about 70–80 MPa, for the following range of mix proportions and conditions: the ratio of Na2SiO3-to-NaOH (1.5–2.5), activator-to-binder ratio (0.2–0.4), Si-to-Al ratio (1.4–1.8), Ca-to-Si ratio (0.4–0.6), and curing temperature (30 °C to 60 °C).

The effect on the compressive strength of fly ash based geopolymer concrete with the generation of hydroxy sodalite

The influence factors of the high compressive strength of fly ash based geopolymer concrete (FGPC) were studied rarely mentioned the generation of hydroxy sodalite before. In this paper, FGPC was prepared by using low-calcium fly ash and NaOH acted as the main component in the base activator. By comparing the compressive strengths of FGPC under different preparation parameters, the microstructures of cross-section and hydroxy sodalite (Na4Al3Si3O12OH) in FGPC were observed by SEM. The spectra information of FGPC were characterized by XRD and FT-IR. The compressive strength of FGPC was analyzed by studying the growth of hydroxy sodalite and the microstructure of the matrix section under different conditions. The results showed that increasing the concentration of NaOH solution, changing curing temperature and curing time in a certain range can effectively enhance the activity of fly ash and promote the generation of N-A-S-H gel and hydroxy sodalite. The compressive strength of FGPC was decided by matrix structure and the generation of hydroxy sodalite. When the ratio of Na2SiO3/NaOH is 1.7, the ratio of alkali activator/fly ash is 0.58, the compressive strength of the FGPC can reach 110.2 MPa by curing at 75 ℃ for 24 h after 28 days. The results can help to understand the mechanism of promotion of hydroxy sodalite and give a novelty reference to produce a high compressive strength FGPC.

Effect of corundum sand proportion on strength properties geopolymer mortar based on fly ash

The main objective of this study is to investigate the possibility of replacing the traditional construction aggregate, river sand, with corundum material and to evaluate the effect of the proportion of corundum sand on the strength properties of fly ash based geopolymer mortar. Electrocorundum (white) and fly ash from a thermal power plant in Skawina were used to make the samples. Different fractions of electrocorundum were used to prepare the geopolymer mass. A geopolymer based on fly ash and river sand was used as reference material. The results showed that the average compressive strength of fly ash and alumina-based materials increased (results ranging from 25.9 to 47.0 MPa) compared to the reference samples (20.7 MPa). The flexural strength results showed a weakening of the strength properties of the electrocorundum and fly ash-based samples (results ranging from 6.1 to 4.8 MPa) compared to the reference sample (7.0 MPa). Only the geopolymer, which in its composition contained a mixture of electrocorundum with several grain gradations, showed an increase in flexural strength (7.3 MPa), compared to the reference sample. Additionally, surface topography analysis of the geopolymer samples was performed by scanning electron microscopy. The whole tests carried out on the above types of material indicate that it can be used as a substitute for sand in geopolymer mortars.

Support Vector Machine (SVM)-Based Optimal Design Procedure of Fly Ash Blended Concrete

A support vector machine (SVM) is widely used for predicting the properties of fly ash blended concrete. However, the studies about the optimal design of fly ash blended concrete based on SVM are very limit. This study shows an SVM-based optimal design procedure of fly ash blended concrete. First, we built an SVM model and evaluated the compressive strength of fly ash blended concrete considering the effects of water to binder ratio, fly ash replacement ratio, and test ages. Second, we made parameter studies based on the SVM model. The parameter studies show that fly ash can improve the late age strength of concrete. This improvement is obvious for concrete with lower water to binder ratio. The optimal fly ash replacement ratio increases as the water to binder ratio decreases.

The Role of Rice Husk Ash in Enhancing the Fresh Properties, Density, and Compressive Strength of Fly Ash Based Self Compacting Geopolymer Concrete

This paper describes the effect of rice husk ash on the fresh properties, density, and compressive strength of fly ash-based self compacting geopolymer concrete (SCGC). Fresh properties were tested by the slump flow test. Density and compressive strength were tested with cylindrical concrete samples. Diameters of samples were about 150 mm and 300 mm high. The variations of Rice Husk Ash (RHA) as Fly Ash (FA) substitution were 0%, 5%, 10%, and 15%. The alkaline activators used Na2SiO3 and NaOH with ratio at 1.0 and a molarity value of NaOH was fixed at 14 M. The water-binder ratio was fixed at 0.3. The dosage of superplasticizer used 1%. The treatment of concrete used ambient temperature. The test results showed that rice husk ash decreased the slump flow; was not affected on the concrete density; and increased the compressive strength of SCGC. The maximum percentage of rice husk ash substitution to achieve the EFNARC slump flow standard was 7.16%. The density of the SCGC was 2076.4 kg / m3 – 2104.3 kg / m3, and classified to the normal concrete. The maximum compressive strength of SCGC was 11.476 MPa at the percentage of rice husk ash substitution about 8.65%.

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm.

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.