A Machine Learning Led Investigation Predicting the Thermos-mechanical Properties of Novel Waste-based Composite in Construction-Reference-Cited by-同舟云学术

A Machine Learning Led Investigation Predicting the Thermos-mechanical Properties of Novel Waste-based Composite in Construction

Published:2024-05-04 Issue:9 Volume:15 Page:5445-5461
ISSN:1877-2641
Container-title:Waste and Biomass Valorization
language:en
Short-container-title:Waste Biomass Valor

Author:

Mahamat Assia Aboubakar^ORCID,Boukar Moussa Mahamat,Leklou Nordine,Obianyo Ifeyinwa Ijeoma,Stanislas Tido Tiwa,Bih Numfor Linda,Ayeni Olugbenga,Ibrahim Nurudeen Mahmud,Savastano Holmer

Funder

L’Oréal-UNESCO for women in science

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12649-024-02538-9.pdf

Reference56 articles.

1. Mahamat, A.A., et al.: Dimensionnal stability and strength appraisal of termite hill soil stabilisation using hybrid bio-waste and cement for eco-friendly housing. Heliyon 8(5), e09406 (2022). https://doi.org/10.1016/j.heliyon.2022.e09406

2. Medvey, B., Dobszay, G.: Durability of stabilized earthen constructions: a review. Geotech. Geol. Eng. 38(3), 2403–2425 (2020). https://doi.org/10.1007/s10706-020-01208-6. Springer

3. Lamrani, M., Mansour, M., Laaroussi, N., Khalfaoui, M.: “Thermal study of clay bricks reinforced by three ecological materials in south of Morocco,” in Energy Procedia, pp 273–277. Elsevier Ltd (2019). https://doi.org/10.1016/j.egypro.2018.11.141

4. Mahamat, A.A., Linda Bih, N., Ayeni, O., AzikiweOnwualu, P., Savastano, H., OluwoleSoboyejo, W.: Development of sustainable and eco-friendly materials from termite hill soil stabilized with cement for low-cost housing in Chad. Buildings 11(3), 86 (2021). https://doi.org/10.3390/buildings11030086

5. Zhang, J., Liu, G., Chen, B., Song, D., Qi, J., Liu, X.: “Analysis of CO2 emission for the cement manufacturing with alternative raw materials: a LCA-based framework,” In Energy Procedia, pp 2541–2545. Elsevier Ltd (2014). https://doi.org/10.1016/j.egypro.2014.12.041

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber;Applied Sciences;2024-08-26