Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

Shah, Muhammad Izhar; Amin, Muhammad; Khan, Kaffayatullah; Niazi, Muhammad Sohaib Khan; Aslam, Fahid; Alyousef, Rayed; Javed, Muhammad Faisal; Mosavi, Amir

doi:10.3390/su13052867

Cited by 36 publications

(16 citation statements)

References 73 publications

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“…Mohammed et al [ 72 ] have previously proven the efficiency and capacity of several predictive models with small MAE values, specifically ranging between 3 and 11. Shah et al [ 73 ] also proved the accuracy of compressive, flexural, and splitting tensile strength models using MAE and RMSE. In their findings, both MAE and RMSE values were considerably low.…”

Section: Resultsmentioning

confidence: 99%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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Although free-cement-based alkali-activated paste, mortar, and concrete have been recognised as sustainable and environmental-friendly materials, a considerable amount of effort is still being channeled to ascertain the best binary or ternary binders that would satisfy the requirements of strength and durability as well as environmental aspects. In this study, the mechanical properties of alkali-activated mortar (AAM) made with binary binders, involving fly ash (FA) and granulated blast-furnace slag (GBFS) as well as bottle glass waste nano-silica powder (BGWNP), were opti-mised using both experimentally and optimisation modelling through three scenarios. In the first scenario, the addition of BGWNP varied from 5% to 20%, while FA and GBFS were kept constant (30:70). In the second and third scenarios, BGWNP (5–20%) was added as the partial replacement of FA and GBFS, separately. The results show that the combination of binary binders (FA and GBFS) and BGWNP increased AAM’s strength compared to that of the control mixture for all scenarios. In addition, the findings also demonstrated that the replacement of FA by BGWNP was the most significant, while the effect of GBFS replacement by BGWNP was less significant. In particular, the highest improvement in compressive strength was recorded when FA, GBFS, and BGWNP were 61.6%, 30%, and 8.4%, respectively. Furthermore, the results of ANOVA (p values < 0.0001 and high F-values) as well as several statistical validation methods (R > 0.9, RAE < 0.1, RSE < 0.013, and RRSE < 0.116) confirmed that all the models were robust, reliable, and significant. Similarly, the data variation was found to be less than 5%, and the difference between the predicted R2 and adj. R2 was very small (<0.2), thus confirming that the proposed non-linear quadratic equations had the capability to predict for further observation. In conclusion, the use of BGWNP in AAM could act as a beneficial and sustainable strategy, not only to address environmental issues (e.g., landfill) but to also enhance strength properties.

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Section: Resultsmentioning

confidence: 99%

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

et al. 2021

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“…Moreover, Suba (2009) employed ANN for mechanical properties prediction and compared the result with linear regression, the forecast of the ANN model was pretty close to actual work. MEP was used to estimate the mechanical properties of concrete and provide acceptable results as implemented by Shah et al (2021). The parametric study revealed the accuracy of the MEP model, with a high correlation coe cient (R).…”

Section: Mohammed Et Al (2020amentioning

confidence: 99%

Implementing The Multi Expression Programming, Nonlinear Regression, Artificial Neural Network, and M5P-Tree Models to Predict The Long-Term of Compressive Strength of Cement- Mortar Modified With Fly Ash

Abdalla

Mohammed

2022

Preprint

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In the recent decade, supplementary cementing ingredients have become an essential part of various strength ranges of concrete and cement-mortar mix design. Examples are natural materials, by-products, industrial wastes, and materials that require less energy and time to generate. Fly ash is one of the most widely utilized additional cementing ingredients. Fly ash is a by-product substance produced by coal combustion. It's being used in cement mortar and concrete as a pozzolanic substance. It has demonstrated significant influence in improving liquid and solid properties of cement mortar, such as compressive strength. Multi Expression Programming (MEP) is employed in this study to estimate the compressive strength (CS) of cement mortar modified with fly ash. The outcomes of this model were compared and evaluated with several other models such as the Nonlinear Regression model (NLR), Artificial Neural Network (ANN), and M5P-tree models that have been used in the construction fields. The input parameters included water/cement ratio (w/c), curing time (t days), and fly ash content (FA %), while the target property was compressive strength up to 360 days of curing. Four hundred fifty (450) data are collected from previous literature on modifying cement mortar with fly ash for that purpose. The water/cement ratio ranged from 0.24 to 1.2, and the fly ash was used to replace cement up to 55% (%wt. of dry cement). Based on the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Scatter Index (SI), Objective (OBJ), Mean Absolute Error (MAE), t-test value, the uncertainty of 95%, Performance Index (ρ), and boxplot for actual and predicted compressive strength. The MEP model performed better than other developed models according to evaluation tools. The compressive strength was also correlated with flexural and splitting tensile strengths using different nonlinear models.

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“…Nowadays, soft computing methods are widely used in many areas of science. In the literature, one can see numerous scientific papers that have used soft computing methods in mining [ 28 , 29 , 30 , 31 ] and engineering geology [ 32 , 33 , 34 , 35 , 36 , 37 ]. However, in terms of determining the relationship between natural stone properties, noticing such works is difficult.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Böhme Abrasion Value of Natural Stones through Artificial Neural Networks (ANN)

Strzałkowski

Köken

2022

Materials

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This present study explored the Böhme abrasion value (BAV) of natural stones through artificial neural networks (ANNs). For this purpose, a detailed literature survey was conducted to collect quantitative data on the BAV of different natural stones from Turkey. As a result of the ANN analyses, several predictive models (M1–M13) were established by using the rock properties, such as the dry density (ρd), water absorption by weight (wa), Shore hardness value (SHV), pulse wave velocity (Vp), and uniaxial compressive strength (UCS) of rocks. The performance of the established predictive models was evaluated by using several statistical indicators, and the performance analyses indicated that four of the established models (M1, M5, M10, and M11) could be reliably used to estimate the BAV of natural stones. In addition, explicit mathematical formulations of the proposed ANN models were also introduced in this study to let users implement them more efficiently. In this context, the present study is believed to provide practical and straightforward information on the BAV of natural stones and can be declared a case study on how to model the BAV as a function of different rock properties.

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Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

Cited by 36 publications

References 73 publications

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

Optimisation of GBFS, Fly Ash, and Nano-Silica Contents in Alkali-Activated Mortars

Implementing The Multi Expression Programming, Nonlinear Regression, Artificial Neural Network, and M5P-Tree Models to Predict The Long-Term of Compressive Strength of Cement- Mortar Modified With Fly Ash

Assessment of Böhme Abrasion Value of Natural Stones through Artificial Neural Networks (ANN)

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