Machine Learning Modeling Integrating Experimental Analysis for Predicting Compressive Strength of Concrete Containing Different Industrial Byproducts

Advances in Civil Engineering, Год журнала: 2024, Номер 2024, С. 1 - 11

Опубликована: Март 1, 2024

This study aimed to develop accurate models for estimating the compressive strength (CS) of concrete using a combination experimental testing and different machine learning (ML) approaches: baseline regression models, boosting model, bagging tree-based ensemble average voting (VR). The research utilized an extensive dataset with 14 input variables, including cement, limestone powder, fly ash, granulated glass blast furnace slag, silica fume, rice husk marble brick coarse aggregate, fine recycled water, superplasticizer, voids in mineral aggregate. To evaluate performance each ML five metrics were used: mean absolute error (MAE), squared (MSE), root (RMSE), coefficient determination (R2-score), relative (RRMSE). comparative analysis revealed that VR model exhibited highest effectiveness, displaying strong correlation between actual estimated outcomes. boosting, bagging, achieved impressive R2-scores range 86.69%–92.43%, MAE ranging from 3.87 4.87, MSE 21.74 38.37, RMSE 4.66 RRMSE 8% 11%. Particularly, outperformed all other R2-score (92.43%) lowest rate. developed demonstrated excellent generalization prediction capabilities, providing valuable tools practitioners, researchers, designers efficiently CS concrete. By mitigating environmental vulnerabilities associated impacts, this can significantly contribute enhancing quality sustainability construction practices.

Язык: Английский

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Preliminary Life Cycle Assessment and Environmental Impact Evaluation of RC Bridge Deck: A Case Study in Norway

Lecture notes in civil engineering, Год журнала: 2024, Номер unknown, С. 233 - 244

Опубликована: Янв. 1, 2024

Язык: Английский

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Experimental and machine learning approaches to investigate the application of sugarcane bagasse ash as a partial replacement of fine aggregate for concrete production

Journal of Building Engineering, Год журнала: 2023, Номер 76, С. 107168 - 107168

Опубликована: Июнь 22, 2023

Язык: Английский

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A novel framework for strength prediction of geopolymer mortar: Renovative precursor effect

Journal of Building Engineering, Год журнала: 2023, Номер 76, С. 107041 - 107041

Опубликована: Июнь 8, 2023

Язык: Английский

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Incorporating non-destructive UPV into machine learning models for predicting compressive strength in SCM concrete

Materials Today Proceedings, Год журнала: 2024, Номер unknown

Опубликована: Апрель 1, 2024

Язык: Английский

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