Predicting the Residual Compressive Strength of Concrete Exposed to Elevated Temperatures Using Interpretable Machine Learning-Reference-Cited by-同舟云学术

Predicting the Residual Compressive Strength of Concrete Exposed to Elevated Temperatures Using Interpretable Machine Learning

Published:2024-11 Issue:4 Volume:29 Page:
ISSN:1084-0680
Container-title:Practice Periodical on Structural Design and Construction
language:en
Short-container-title:Pract. Period. Struct. Des. Constr.

Author:

Noman Muhammad¹,Khattak Afaq²,Alam Zeshan¹,Yaqub Muhammad³,Noroozinejad Farsangi Ehsan⁴^ORCID

Affiliation:

1. Assistant Professor, Dept. of Civil Engineering, International Islamic Univ., Islamabad 44000, Pakistan.

2. Postdoc Fellow, College of Transportation Engineering, Tongji Univ., Jiading Campus, Shanghai 201804, China.

3. Professor, Dept. of Civil Engineering, Univ. of Engineering and Technology, Taxila 47080, Pakistan.

4. Senior Researcher, Urban Transformations Research Centre, Western Sydney Univ., Sydney, NSW 2150, Australia (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/PPSCFX.SCENG-1536

Reference163 articles.

1. A generalised model for direct prediction of stresses in concrete at high temperatures

2. ACI (American Concrete Institute). 2019. Building code requirements for structural concrete (ACI 318-19) and commentary. Farmington Hills, MI: ACI.

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

4. Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature

5. Mechanical and Impact Properties of Engineered Cementitious Composites Reinforced with PP Fibers at Elevated Temperatures