Novel hybrid machine learning approach for predicting structural response of RC beams under blast loading

Almustafa, Monjee K.; Nehdi, Moncef L.

doi:10.1016/j.istruc.2022.04.007

Cited by 24 publications

(3 citation statements)

References 29 publications

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“…Almustafa et al [126] introduced a hybrid machinelearning model for predicting maximum displacement in blast-loaded reinforced concrete (RC) beams. Integrating gradient-boosted regression trees with the HGSO algorithm, the model demonstrated superior performance in displacement prediction (MAE of 4.48 mm, R2 of 93.4%).…”

Section: B Hybridized Versions Of Hgsomentioning

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: B Hybridized Versions Of Hgsomentioning

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

View full text Add to dashboard Cite

show abstract

“…The present study focuses on introducing an efficient development of a machine learning model that predicts compressive strength. A supervised model based on a support vector machine (SVR) is considered [49][50][51]. The model hyperparameters are optimally determined by Henry's gas solubility optimization (HGSO) and particle swarm optimization (PSO) algorithms that there are many successful examples for HGSO [49][50][51].…”

Section: Introductionmentioning

confidence: 99%

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

Chen

2022

Eng. Res. Express

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Using machine learning models to provide a reliable and accurate model to predict the compressive strength of high-performance concrete helps save the time-cost and financial cost of concrete casting. On the other hand, applying admixtures such as fly ash and silica fume in the concrete structure to replace cement helps diminish carbon dioxide emissions. In the present study, a support vector machine-based regression was considered to overcome the difficulties of compressive strength, which is intensified with a modern mix design of high-performance concrete. The reliability and accuracy of the model were enhanced by providing an optimal structure by employing novel Henry's gas solubility optimization (HGSO) and particle swarm optimization (PSO) algorithms. The comparative study aimed to prove that the model optimized with Henry's gas solubility algorithm has a higher potential in predicting compressive strength. The obtained OBJ values for HGSO based model and PSO-based model of 1.4156 and 1.5419, respectively, confirmed the higher accuracy of HGSO based model.

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“…Research significance. The use of machine learning and numerical modeling techniques to develop different engineering models of FRRAC has recently gained much importance [41][42][43][44] . In this research, a 3D modeling of FRRAC and its constituent phases at meso and macro scales was performed, with the purpose to capture the specifications of actual models through analytic and numerical simulations.…”

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confidence: 99%

A comprehensive quantitative bottom-up analysis of fiber-reinforced recycled-aggregate concrete behavior

Hosseinzadeh

Dehestani

Samadvand³

2023

Sci Rep

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This study provides a more profound understanding of the influence of the phases of fiber-reinforced recycled-aggregate concrete (FRRAC), on its elastic properties, in particular Young’s modulus and Poisson’s ratio. Multi-scale modeling analyses of mortar and FRRAC were conducted to assess the effect of variations in the fiber content, fiber elastic modulus, RA content, and water-to-cement ratio (w/c) on the elastic properties at each scale. Thus, the analytic Mori–Tanaka (MT) homogenization algorithm developed in Python programming language and the three-dimensional finite element (FE) homogenization scheme were applied to evaluate the elastic properties of FRRAC. As such, different fiber types including steel, basalt, glass, and propylene, at a volume fraction range of 0–2%, along with the variations in fiber elastic modulus, and different RA replacement levels ranging from 0 to 100% were incorporated in the modeling process at different w/c ratio. Based on the results, the Poisson’s ratio of steel FRRAC in the MT approach surges with increasing fiber content. Furthermore, the elastic modulus of FRRAC is highly susceptible to an increase in Young’s modulus of polypropylene fiber, among other fiber types. The elastic modulus of concrete experiences a sharp decrease with increasing w/c for all fiber types in both FE and MT approaches.

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Novel hybrid machine learning approach for predicting structural response of RC beams under blast loading

Cited by 24 publications

References 29 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

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

A comprehensive quantitative bottom-up analysis of fiber-reinforced recycled-aggregate concrete behavior

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