Integrating laser-induced breakdown spectroscopy and non-linear random forest-based algorithms to predict soil unconfined compressive strength-Reference-Cited by-同舟云学术

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Integrating laser-induced breakdown spectroscopy and non-linear random forest-based algorithms to predict soil unconfined compressive strength

Published:2024-02-29 Issue:5 Volume:83 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Wudil Yakubu Sani,Al-Najjar O. A.,Al-Osta Mohammed A.,Al-Amoudi Omar S. Baghabra,Gondal M. A.,Kunwar S.,Almohammedi Abdullah

Funder

King Fahd University of Petroleum and Minerals

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12665-023-11386-0.pdf

Reference56 articles.

1. Akinpelu AA, Md.E. Ali, Owolabi TO, Johan MR, Saidur R, Olatunji SO, Chowdbury Z (2020) A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution, Neural Comput Appl 32:18 14899–14908. https://doi.org/10.1007/S00521-020-04845-3.

2. Almansour NA, Syed HF, Khayat NR, Altheeb RK, Juri RE, Alhiyafi J, Alrashed S, Olatunji SO (2019) Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med 109:101–111. https://doi.org/10.1016/J.COMPBIOMED.2019.04.017

3. Al-Najjar OA, Wudil YS, Ahmad UF, Al-Amoudi OSB, Al-Osta MA, Gondal MA (2022) Applications of laser induced breakdown spectroscopy in geotechnical engineering: a critical review of recent developments, perspectives and challenges 1–37. https://doi.org/10.1080/05704928.2022.2136192.

4. Al-Najjar OA, Wudil YS, Al-Osta MA, Imam A, Al-Amoudi OSB, Gondal M (2023) A Laser-induced breakdown spectroscopy-based assessment of unconfined compressive strength of normal and chemically stabilized soils, Arab J Sci Eng. 1–15. https://doi.org/10.1007/S13369-023-08055-6/METRICS.

5. Alrebdi TA, Wudil YS, Ahmad UF, Yakasai FA, Mohammed J, Kallas FH (2022) Predicting the thermal conductivity of Bi2Te3-based thermoelectric energy materials: a machine learning approach. Int J Therm Sci 181:107784. https://doi.org/10.1016/J.IJTHERMALSCI.2022.107784

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. Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tools: A Scientometrics Review;Studies in Systems, Decision and Control;2024

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