Machine learning models for predicting the compressive strength of agro-waste stabilized bricks for sustainable buildings

Obianyo, Ifeyinwa Ijeoma; Auta, Jonathan Timothy; Sciacca, David; Mahamat, Assia Aboubakar; Kelechi, Sylvia Echezona; Onwualu, Azikiwe Peter

doi:10.1007/s44290-024-00052-4

Discov Civ Eng

2024

DOI: 10.1007/s44290-024-00052-4

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Machine learning models for predicting the compressive strength of agro-waste stabilized bricks for sustainable buildings

Ifeyinwa Ijeoma Obianyo,

Jonathan Timothy Auta,

David Sciacca

et al.

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Cited by 2 publications

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Microstructural analysis and prediction of strength response of black expansive soil

Attah

2024

Discov Civ Eng

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Microstructural analysis and prediction of strength response of black expansive soil

Attah

2024

Discov Civ Eng

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Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber

Mahamat,

Boukar,

Leklou

et al. 2024

Applied Sciences

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This research focuses on the environmental-friendly production of Borassus fruit fibers (BNF), its characterization, and hygroscopic properties determination via Dynamic Vapor Sorption (DVS). The experimental results obtained from the hygroscopic behavior analysis were used to create a primary dataset to train and test Decision Tree Regression (DTR) and Gradient Boosting Regressor (GBR) models. The created primary dataset comprised 294 observations, from which 80% were used to train the models, and the remaining 20% were used for the testing of the two models. The models exhibited high accuracy, easy interpretability on the small-size dataset, and flexibility with regards to the nature of the relationship between the input and output variable. Both models successfully predicted the hygroscopic behavior with the Gradient Boosting Regressor outperforming Decision Tree Regression by indicating values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF, and values of 0.012, 0.109, 0.059, and 0.999 for MSE, RMSE, MAE, and R2, respectively, during the desorption of the BNF. This suggests that the Gradient Boosting Regressor illustrated the maximum accuracy. The outcomes can be utilized to provide an alternative for traditional methods, which can often be costly and time-consuming by improving the engineering properties of BNF. The models can be used in the construction sector to lower costs as they are able to pinpoint elements influencing the characteristics for specific applications to grasp its various properties through the prediction of its hygroscopic properties.

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Machine learning models for predicting the compressive strength of agro-waste stabilized bricks for sustainable buildings

Cited by 2 publications

References 54 publications

Microstructural analysis and prediction of strength response of black expansive soil

Microstructural analysis and prediction of strength response of black expansive soil

Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber

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