Transverse Shear Capacity Predictions of GFRP Bars Subjected to Accelerated Aging Using Artificial Neural Networks-Reference-Cited by-同舟云学术

Transverse Shear Capacity Predictions of GFRP Bars Subjected to Accelerated Aging Using Artificial Neural Networks

Published:2023-04 Issue:4 Volume:35 Page:
ISSN:0899-1561
Container-title:Journal of Materials in Civil Engineering
language:en
Short-container-title:J. Mater. Civ. Eng.

Author:

Fasil Mohammed¹^ORCID,Al-Zahrani Mesfer M.²^ORCID

Affiliation:

1. Ph.D. Student, Civil and Environmental Engineering Dept., College of Design and Built Environment (CDBE), King Fahd Univ. of Petroleum and Minerals, Dhahran, Eastern Province 31261, Saudi Arabia. ORCID: .

2. Associate Professor, Civil and Environmental Engineering Dept., College of Design and Built Environment (CDBE), King Fahd Univ. of Petroleum and Minerals, Dhahran, Eastern Province 31261, Saudi Arabia (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanics of Materials,General Materials Science,Building and Construction,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29MT.1943-5533.0004686

Reference51 articles.

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3. Performance of glass fiber reinforced polymer bars under elevated temperatures

4. Al-Zahrani M. M. S. U. Al-Dulaijan A. Sharif and M. Maslehuddin. 2002. “Durability performance of glass fiber reinforced plastic reinforcement in harsh environments.” In Vol. 3 of Proc. 6th Saudi Engineering Conf. 307. Dhahran Saudi Arabia: King Fahd Univ. of Petroleum and Minerals.

5. A Gaussian process regression model to predict energy contents of corn for poultry

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