Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete

Results in Engineering, Год журнала: 2025, Номер 25, С. 104542 - 104542

Опубликована: Март 1, 2025

Язык: Английский

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Strength performance and microscopic mechanism of cement mortar incorporating fine recycled concrete aggregate and natural sand

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 111767 - 111767

Опубликована: Янв. 1, 2025

Язык: Английский

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Application of novel deep neural network on prediction of compressive strength of fly ash based concrete

Nondestructive Testing And Evaluation, Год журнала: 2024, Номер unknown, С. 1 - 31

Опубликована: Ноя. 18, 2024

Fly ash (FA)-based high-strength concrete (HSC) has attracted significant interest due to its potential substitute Portland cement, offering both environmental benefits and improved performance. However, the design of FA-HSC is challenging, as key factors such fly percentage, water content, superplasticizer dosage have a complex influence on compressive strength. This study aims develop an efficient predictive tool for mix design, using artificial intelligence (AI) models address inherent variability uncertainty in these parameters. Six AI models, including Deep Neural Network (DNN), were employed analyse relationships between variables The DNN model, particular, demonstrated superior performance compared other with high coefficient determination (R2 = 0.89), variance accounted (VAF 88.3%), root mean square error (RMSE 0.06), residual standard (RSR 0.31). These results indicate that model can provide reliable predictions strength, more alternative traditional trial-and-error methods. AI-based approach save time material costs while optimising Overall, this AI-driven contributes advancement sustainable technology by enabling precise resource-efficient designs FA-based concrete.

Язык: Английский

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Development of Ultra-High Performance Concrete (UHPC) matrix based on recycled concrete fines subjected to coupling curing of microwave and wet carbonation

Journal of Building Engineering, Год журнала: 2024, Номер 95, С. 110038 - 110038

Опубликована: Июнь 24, 2024

Язык: Английский

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Predictive and experimental assessment of chloride ion permeation in concrete subjected to multi-factorial conditions using the XGBoost algorithm

Journal of Building Engineering, Год журнала: 2024, Номер 98, С. 111041 - 111041

Опубликована: Окт. 12, 2024

Язык: Английский

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Mechanical properties and radiological implications of replacing sand with waste ceramic aggregate in ordinary concrete

Journal of Radiation Research and Applied Sciences, Год журнала: 2024, Номер 17(4), С. 101175 - 101175

Опубликована: Ноя. 8, 2024

Язык: Английский

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Physical & mechanical properties of pervious concrete incorporating municipal solid waste incineration bottom ash

Journal of Building Engineering, Год журнала: 2024, Номер 96, С. 110599 - 110599

Опубликована: Авг. 30, 2024

Язык: Английский

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Synergetic valorization of recycled aggregates and waste glass powder in sustainable concrete: Microstructure, mechanical strength, and bonding performance

Construction and Building Materials, Год журнала: 2024, Номер 458, С. 139609 - 139609

Опубликована: Дек. 20, 2024

Язык: Английский

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Predicting compressive strength in cement mortar: The impact of fly ash composition through machine learning

Sustainable Chemistry and Pharmacy, Год журнала: 2025, Номер 43, С. 101915 - 101915

Опубликована: Янв. 19, 2025

Язык: Английский

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A Comparative Performance Analysis of Machine Learning Models for Compressive Strength Prediction in Fly Ash-Based Geopolymers Concrete Using Reference Data

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04207 - e04207

Опубликована: Янв. 1, 2025

Язык: Английский

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