Novel hybrid HGSO optimized supervised machine learning approaches to predict the compressive strength of admixed concrete containing fly ash and micro-silica

Chen, Liangliang; Li, Fenghua; Wu, Fufei

doi:10.1088/2631-8695/ac6d49

Cited by 6 publications

(2 citation statements)

References 66 publications

(64 reference statements)

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“…Chen et al [137] introduced a support vector machinebased regression model for precise prediction of highperformance concrete compressive strength. The study addresses challenges in concrete casting with admixtures by utilizing HGSO and PSO.…”

Section: A Engineeringmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

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Section: A Engineeringmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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“…Various kernel functions adaptable to the SVR model allow handling the dataset with complex inner non-linearity. In some research, the SVR model is optimized by metaheuristic optimizers [41,42].…”

Section: Introductionmentioning

confidence: 99%

Flow direction algorithm-based machine learning approaches for the prediction of high-performance concrete strength property

He¹,

Zong-wei²,

Xu³

2022

Eng. Res. Express

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The main significance of utilizing the high-performance concrete as an effective item in the construction industry is the compressive strength assessment which requires a vast investigation of the design mix with calculated relevant compressive strength. Through the intelligence approaches, planning an accurate relationship between high-performance concrete different mix designs and their compressive strength is obtainable with the lowest cost of time and finance. In this regard, two models based on support vector regression methods are developed. The optimal output is calculated by tuning support vector regression key constraints by flow direction and biography-based optimization algorithm. The data set collected from the literature is divided into the training, and the testing phase, where the training data is used to develop the models, and the testing data is utilized to validate the accuracy of the models. The results showed a higher accuracy of the FDA_SVR method than the BBO_SVR method, with R2 values of 0.9939 and 0.9755, respectively. moreover, the U_95 confidence level obtained about 4.0273 and 7.367 for the FDA-SVR and BBO-SVR, respectively, demonstrating the accurate prediction capacity of the FDA-SVR model for high-performance concrete compressive strength.

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Optimal boosting method of HPC concrete compressive and tensile strength prediction

Chao

2023

Structural Concrete

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The evaluation of fly ash (FA) and micro‐silica (MS) effects on the mechanical properties of concrete at various ages prompts the search for efficient factors in forecasting the compressive strength (CS) and tensile strength (TS), which are extendable for future investigation as well as practical use. The main aim of this article is to overcome the complexity caused by nonlinearity, which is rooted in the relationship between input variables and outputs. Also, the nonlinearity is getting worse through the existence of admixtures. The Adaptive Boosting (ADA) approach was utilized in the related study as a hybrid model to provide an optimal relation between the ingredients and the mechanical strengths for evaluating the parameters most useful in predicting the CS and TS of high‐performance concrete. Furthermore, model boosting is applied using the starling murmuration optimization (SMO), termite queen algorithm (TQO), and gannet optimization algorithm (GOA) algorithms to reach the highest convergence. As a result of assessing the related models, the ADA‐SMO obtained a coefficient correlation of 0.9981 and 0.9825 for the predictions of CS and TS, respectively. Considering the adaptive framework provided to have the best compatibility with the complex physics of the problem, the ADA‐SMO earned the highest level of accuracy in estimating CS and TS among other hybrid models and was introduced as a trustable model in the prediction of strength properties.

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Novel hybrid HGSO optimized supervised machine learning approaches to predict the compressive strength of admixed concrete containing fly ash and micro-silica

Cited by 6 publications

References 66 publications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Flow direction algorithm-based machine learning approaches for the prediction of high-performance concrete strength property

Optimal boosting method of HPC concrete compressive and tensile strength prediction

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