Novel Optical-Inspired Rain Forest for the Explainable Prediction of Geopolymer Concrete Compressive Strength-Reference-Cited by-同舟云学术

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Novel Optical-Inspired Rain Forest for the Explainable Prediction of Geopolymer Concrete Compressive Strength

Published:2024-11 Issue:6 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Cheng Min-Yuan¹^ORCID,Khitam Akhmad F. K.²

Affiliation:

1. Professor, Dept. of Civil and Construction Engineering, National Taiwan Univ. of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan (corresponding author). ORCID: .

2. Ph.D. Candidate, Dept. of Civil and Construction Engineering, National Taiwan Univ. of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5956

Reference85 articles.

1. Unsupervised local cluster-weighted bootstrap aggregating the output from multiple stochastic simulators

2. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques;Ahmad A.;Case Stud. Constr. Mater.,2022

3. Prediction of Geopolymer Concrete Compressive Strength Using Novel Machine Learning Algorithms

4. Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

5. Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

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