Implementation of Machine Learning in Predicting Pin-Bearing Strength of Aged and Nonaged Pultruded GFRP Composites-Reference-Cited by-同舟云学术

Implementation of Machine Learning in Predicting Pin-Bearing Strength of Aged and Nonaged Pultruded GFRP Composites

Published:2024-06 Issue:3 Volume:28 Page:
ISSN:1090-0268
Container-title:Journal of Composites for Construction
language:en
Short-container-title:J. Compos. Constr.

Author:

Alshannaq Ammar A.¹^ORCID,Awawdeh Abdel Rahman²^ORCID

Affiliation:

1. Assistant Professor, Dept. of Civil Engineering, Yarmouk Univ., P.O. Box 566, Irbid 21163, Jordan (corresponding author). ORCID: .

2. Ph.D. Student, Dept. of Civil Engineering, Univ. of Mississippi, Oxford, MS 38677. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCOF2.CCENG-4483

Reference59 articles.

1. ACMA (American Composites Manufacturers Association). 2010. Pre-standard for load & resistance factor design (LRFD) of pultruded fiber reinforced polymer (FRP) structures. Arlington, VA: ASCE.

2. AISC. 2022. Specification for structural steel buildings (ANSI/AISC 360-22). Chicago, IL: AISC.

3. Bandwidth control mechanism and extreme gradient boosting algorithm for protecting software-defined networks against DDoS attacks;Alamri H. A.;IEEE Access,2020

4. Amelia, D. R., A. Asrial, and M. H. Effendi Hasibuan. 2020. “The effectiveness of argument-driven inquiry in promoting students” argumentation skills about colloids.” In Proc., 3rd Green Development International Conf., 328–335. Dordrecht, The Netherlands: Atlantis Press.

5. Anwar J. 2017. “Time-dependent behavior of pin-bearing bolted connections in pultruded fiber reinforced polymer composites under normal and elevated temperatures.” Ph.D. thesis School of Civil and Environmental Engineering Georgia Institute of Technology.