A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete-Reference-Cited by-同舟云学术

A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete

Published:2023-05-09 Issue:1 Volume:118 Page:209-238
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Bassi Akshita,Manchanda Aditya,Singh Rajwinder,Patel Mahesh^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology

Link

https://link.springer.com/content/pdf/10.1007/s11069-023-05998-9.pdf

Reference140 articles.

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2. Agarwal A (2019) Support vector machine — Formulation and derivation. In: Towar. Data Sci. https://towardsdatascience.com/support-vector-machine-formulation-and-derivation-b146ce89f28. Accessed 20 Jun 2022

3. Agrawal SK (2021) Understanding the basics of artificial neural network,. In: Data Sci. Blogathon, Anal. Vidhya,. https://www.analyticsvidhya.com/blog/2021/07/understanding-the-basics-of-artificial-neural-network-ann/

4. Ahmad M, Hu J-L, Ahmad F et al (2021) Supervised learning methods for modeling concrete compressive strength prediction at high temperature. Materials (basel) 14:1983. https://doi.org/10.3390/ma14081983

5. Ahmed HU, Mostafa RR, Mohammed A et al (2022) Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07724-1

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