Machine learning-based prediction of compressive strength and slump in high-performance concrete

The design of a concrete structure heavily depends on one of the most significant performance parameters: concrete strength. Reliable strength prediction can save design expenses and time, as well as reduce material waste from multiple mixing experiments. Because it has a very long service life, imp...

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Vydáno v:	Multiscale and Multidisciplinary Modeling, Experiments and Design Ročník 8; číslo 10; s. 463
Hlavní autoři:	Zhang, Ruikun, Meng, Chenyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.11.2025 Springer Nature B.V
Témata:	Algorithms Cement Characterization and Evaluation of Materials Compressive strength Concrete mixes Concrete mixing Concrete properties Concrete structures Construction industry Engineering Fire resistance Machine learning Mathematical Applications in the Physical Sciences Mechanical Engineering Numerical and Computational Physics Optimization Original Paper Reinforced concrete Search algorithms Service life Simulation Soft computing Solid Mechanics Support vector machines CS XGBR Population-Based Vortex Search Algorithm Slump Honey Badger Algorithm High-performance concrete Escaping Bird Search for Constrained Optimization
ISSN:	2520-8160, 2520-8179
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Abstract	The design of a concrete structure heavily depends on one of the most significant performance parameters: concrete strength. Reliable strength prediction can save design expenses and time, as well as reduce material waste from multiple mixing experiments. Because it has a very long service life, improves with time in strength, and is highly fire-resistant, concrete is the safest and most environmentally friendly building material used worldwide. It is estimated that between 21 and 31 billion tons are consumed annually. To generate concrete with the required strength, durability, and functionality in the most cost-efficient manner, designing a concrete mix entails selecting appropriate concrete elements and determining their relative ratios. This work used Extreme Gradient Boosting Regression (XGBR) to estimate the Compressive Strength (CS) and Slump (SL) in high-performance concrete. Three optimization methods were employed: The Population-Based Vortex Search Algorithm (PBVSA), the Escaping Bird Search for Constrained Optimization (EBSCO), as well as the Honey Badger Algorithm (HBA), to increase the predictive capacity of the underlying model. To improve accuracy, a calculated decision was made to combine these optimizers with existing schemes, resulting in the development of novel hybrid schemes with enhanced prediction capabilities. XGBR + PBVSA (XGPB), XGBR + EBSCO (XGEB), and XGBR + HBA (XGHB) are the names of the resulting hybrid schemes. The XGEB model, with a value of 0.985, performs exceptionally well during the test phase, as indicated by the R 2 index value in the SL section. The XGPB model, with a value of 0.975, comes in second. XGEB has the highest performance model in the CS section during the test phase, with an R 2 index value of 0.991, while XGB has the lowest productivity model with a value of 0.968. Graphical abstract
AbstractList	The design of a concrete structure heavily depends on one of the most significant performance parameters: concrete strength. Reliable strength prediction can save design expenses and time, as well as reduce material waste from multiple mixing experiments. Because it has a very long service life, improves with time in strength, and is highly fire-resistant, concrete is the safest and most environmentally friendly building material used worldwide. It is estimated that between 21 and 31 billion tons are consumed annually. To generate concrete with the required strength, durability, and functionality in the most cost-efficient manner, designing a concrete mix entails selecting appropriate concrete elements and determining their relative ratios. This work used Extreme Gradient Boosting Regression (XGBR) to estimate the Compressive Strength (CS) and Slump (SL) in high-performance concrete. Three optimization methods were employed: The Population-Based Vortex Search Algorithm (PBVSA), the Escaping Bird Search for Constrained Optimization (EBSCO), as well as the Honey Badger Algorithm (HBA), to increase the predictive capacity of the underlying model. To improve accuracy, a calculated decision was made to combine these optimizers with existing schemes, resulting in the development of novel hybrid schemes with enhanced prediction capabilities. XGBR + PBVSA (XGPB), XGBR + EBSCO (XGEB), and XGBR + HBA (XGHB) are the names of the resulting hybrid schemes. The XGEB model, with a value of 0.985, performs exceptionally well during the test phase, as indicated by the R 2 index value in the SL section. The XGPB model, with a value of 0.975, comes in second. XGEB has the highest performance model in the CS section during the test phase, with an R 2 index value of 0.991, while XGB has the lowest productivity model with a value of 0.968. Graphical abstract The design of a concrete structure heavily depends on one of the most significant performance parameters: concrete strength. Reliable strength prediction can save design expenses and time, as well as reduce material waste from multiple mixing experiments. Because it has a very long service life, improves with time in strength, and is highly fire-resistant, concrete is the safest and most environmentally friendly building material used worldwide. It is estimated that between 21 and 31 billion tons are consumed annually. To generate concrete with the required strength, durability, and functionality in the most cost-efficient manner, designing a concrete mix entails selecting appropriate concrete elements and determining their relative ratios. This work used Extreme Gradient Boosting Regression (XGBR) to estimate the Compressive Strength (CS) and Slump (SL) in high-performance concrete. Three optimization methods were employed: The Population-Based Vortex Search Algorithm (PBVSA), the Escaping Bird Search for Constrained Optimization (EBSCO), as well as the Honey Badger Algorithm (HBA), to increase the predictive capacity of the underlying model. To improve accuracy, a calculated decision was made to combine these optimizers with existing schemes, resulting in the development of novel hybrid schemes with enhanced prediction capabilities. XGBR + PBVSA (XGPB), XGBR + EBSCO (XGEB), and XGBR + HBA (XGHB) are the names of the resulting hybrid schemes. The XGEB model, with a value of 0.985, performs exceptionally well during the test phase, as indicated by the R2 index value in the SL section. The XGPB model, with a value of 0.975, comes in second. XGEB has the highest performance model in the CS section during the test phase, with an R2 index value of 0.991, while XGB has the lowest productivity model with a value of 0.968.
ArticleNumber	463
Author	Meng, Chenyu Zhang, Ruikun
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SubjectTerms	Algorithms Cement Characterization and Evaluation of Materials Compressive strength Concrete mixes Concrete mixing Concrete properties Concrete structures Construction industry Engineering Fire resistance Machine learning Mathematical Applications in the Physical Sciences Mechanical Engineering Numerical and Computational Physics Optimization Original Paper Reinforced concrete Search algorithms Service life Simulation Soft computing Solid Mechanics Support vector machines
Title	Machine learning-based prediction of compressive strength and slump in high-performance concrete
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