Prediction of the Postfire Flexural Capacity of RC Beam Using GA-BPNN Machine Learning-Reference-Cited by-同舟云学术

Prediction of the Postfire Flexural Capacity of RC Beam Using GA-BPNN Machine Learning

Published:2020-12 Issue:6 Volume:34 Page:04020105
ISSN:0887-3828
Container-title:Journal of Performance of Constructed Facilities
language:en
Short-container-title:J. Perform. Constr. Facil.

Author:

Cai Bin¹,Pan Guo-liang²,Fu Feng³^ORCID

Affiliation:

1. Professor, School of Civil Engineering, Jilin Jianzhu Univ., Changchun 130118, China; Visiting Scholar, School of Mathematics, Computer Science and Engineering, City, Univ. of London, London EC1V 0HB, UK.

2. Research Student, School of Civil Engineering, Jilin Jianzhu Univ., Changchun 130118, China.

3. Senior Lecturer (Associate Professor), School of Mathematics, Computer Science and Engineering, Dept. of Civil Engineering, Northampton Square, London EC1V 0HB, UK (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CF.1943-5509.0001514

Reference42 articles.

1. Prediction of the Failure Time of Glass Fiber Reinforced Plastic Reinforced Concrete Beams under Fire Conditions

2. Neural networks for predicting compressive strength of structural light weight concrete

3. Evolution of the strains of traditional and self-compacting concrete during and after fire

4. Cai B. B. Zhang and F. Fu. 2019. “A new reliability analysis approach for the flexural capacity of postfire reinforced concrete beams retrofitted with CFRPs.” In Proc. Institution of Civil Engineers: Structures and Buildings 1–40. London: Thomas Telford. https://doi.org/10.1680/jstbu.19.00037.

5. Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks

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