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

Abstract

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.

The Influence of Fly Ash Contents on Mechanical Properties and Energy Response of Concrete at a High Strain Rate

“…With the application of more and more fy ash concrete on high in-situ stress environment, it could be found that the stress wave in high in-situ stress environment led to the failure of fy ash concrete. In recent years, some scholars have begun to study the dynamic mechanical properties of fy ash concrete [19][20][21][22][23]. Zhang et al [24] conducted dynamic splitting tests on concrete with fy ash (contents of 0%, 5%, 10%, 15%, 20%, and 30%) and found that the dynamic splitting tensile strength of concrete with water-cement ratio of 0.45 and 0.6 reaches the maximum value when fy ash content is 10%.…”

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

“…In addition, regression methods may exaggerate the significance of some variables [ 37 ]. Methods based on artificial intelligence, such as supervised machine learning (ML), are amongst the most highly advanced modeling approaches employed in the current topic area [ 38 , 39 , 40 , 41 , 42 ]. These approaches simulate reactions based on input characteristics, and testing validates the yield models.…”

In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches

“…Concrete continues to be the most widely utilized manmade substance on the planet. Given the considerable environmental impact of cement production, research has concentrated on both reducing the impact on the environment and cost reductions for cement-based products [ 1 , 2 , 3 ]. The utilization of metakaolin (MK) as an additive or partial substitute for cement is a major area of research in the manufacture of concrete materials.…”