Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

Alzabeebee, Saif; Mohamad, Safaa A.; Al‐Hamd, Rwayda Kh. S.

doi:10.1080/14680629.2021.1995471

Cited by 15 publications

(4 citation statements)

References 58 publications

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“…To use the EPR-MOGA, the user must determine the correlation's structure, the range of exponents, and the number of terms. A more in-depth explanation of the EPR-MOGA can be found in (Alani et al, 2014;Alzabeebee, Dhahir, et al, 2022;Alzabeebee, Mohamad, et al, 2022;Assaad et al, 2021;Giustolisi & Savic, 2006;Zuhaira et al, 2021).…”

Section: Multi-objective Evolutionary Polynomial Regression (Moga-epr)mentioning

confidence: 96%

Application of soft computing in predicting the compressive Strength of Self-Compacted Concrete Containing Recyclable Aggregate

Albostami,

Al-Hamd,

Alzabeebee

et al. 2023

Preprint

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Self-compacting concrete (SCC) is a type of concrete that is known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP). The results of the analysis indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the Linear Regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R2) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.

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Section: Multi-objective Evolutionary Polynomial Regression (Moga-epr)mentioning

confidence: 96%

Application of soft computing in predicting the compressive Strength of Self-Compacted Concrete Containing Recyclable Aggregate

Albostami,

Al-Hamd,

Alzabeebee

et al. 2023

Preprint

Self Cite

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“…Evolutionary polynomial regression analysis (EPR-MOGA) is used to aid the development of the new model. EPR-MOGA stands for multi-objective evolutionary polynomial regression analysis, an intelligent computational method that leverages input data to generate a novel solution to a specific problem [10,14]. This method is based on regression analysis and employs a genetic algorithm (GA) to create a mathematical model that can explain the relationship between physical input variables [21,31].…”

Section: Epr-mogamentioning

confidence: 99%

Multiscale soft computing-based model of shear strength of steel fibre-reinforced concrete beams

Alzabeebee

Qiu

Nassr

et al. 2023

Innov. Infrastruct. Solut.

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Concrete is weak in tension, so steel fibres are added to the concrete members to increase shear capability. The shear capacity of steel fibre-reinforced concrete (SFRC) beams is crucial when building reinforced concrete structures. Creating a precise equation to determine the shear resistance of SFRC beams is challenging since many factors can influence the shear capacity of these beams. In addition, the precision available equations to predict the shear capacity are examined. The current research aims to examine the available equations and propose novel and more accurate model to predict the shear capacity of SFRC beams. An innovative evolutionary polynomial regression analysis (EPR- MOGA) is utilized to propose the new equation. The proposed equation offered improved prediction and increased accuracy compared to available equations, where it scored a lower mean absolute error ($$\mathrm{MAE}$$ MAE ) and root mean square error ($$\mathrm{RMSE}$$ RMSE ), a mean ($$\mu$$ μ ) close to the optimum value of 1.0 and a higher coefficient of determination ($${R}^{2}$$ R 2 ) when a comparison with literature was conducted. Therefore, the new equation can be employed to assure more resilient and optimized design calculations due to their improved performance.

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“…EPR-MOGA can be defined as an intelligent computational method that uses the input data to create an innovative novel solution for practical problems. 22,33,34 This approach is based on regression analysis and uses a genetic algorithm (GA) to produce a mathematical correlation that can describe the relationship between the physical input variables. 30,31 The EPR-MOGA uses regression analysis and implements a GA to search for the best correlation.…”

Section: Multi-objective Evolutionary Polynomial Regression Analysis ...mentioning

confidence: 99%

Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach

et al. 2022

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This paper assesses the capability of using a new data-driven approach to predict the bond strength between steel rebar and concrete subjected to high temperatures. The analysis has been conducted using a novel evolutionary polynomial regression analysis (EPR-MOGA) that employs soft computing techniques, and new correlations have been proposed. The proposed correlations provide better predictions and enhanced accuracy than existing approaches, such as classical regression analysis. Based on this novel approach, the resulting correlations have achieved a lower mean absolute error (MAE), and root mean square error (RMSE), a mean (μ) close to the optimum value (1.0) and a higher coefficient of determination (R 2 ) compared to available correlations, which use classical regression analysis. Based on their enhanced performance, the proposed correlations can be used to obtain better optimised and more robust design calculations.

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Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve

Abstract: Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve '. Road Materials and Pavement Design, 23(12).

Cited by 15 publications

References 58 publications

Application of soft computing in predicting the compressive Strength of Self-Compacted Concrete Containing Recyclable Aggregate

Application of soft computing in predicting the compressive Strength of Self-Compacted Concrete Containing Recyclable Aggregate

Multiscale soft computing-based model of shear strength of steel fibre-reinforced concrete beams

Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach

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