Post-fire reliability analysis of concrete beams retrofitted with CFRPs: a new approach

Author:

Cai Bin12,Zhang Bo3,Fu Feng4ORCID

Affiliation:

1. Head of Department of mechanics, School of Civil Engineering, Jilin Jianzhu University, Changchun, China

2. School of Mathematics, Computer Science and Engineering, City, University of London, London, UK

3. Student, School of Civil Engineering, Jilin Jianzhu University, Changchun, China

4. Senior Lecturer, School of Mathematics, Computer Science and Engineering, City, University of London, London, UK (corresponding author: )

Abstract

A new reliability analysis approach for the flexural capacity of post-fire reinforced concrete (RC) beams retrofitted with carbon-fibre-reinforced polymers/plastics (CFRPs) is proposed. In this approach, the thermal parameters of RC beams are first determined to enable heat transfer analysis in Abaqus. Based on the thermal response obtained from the heat transfer analysis, a section method is used to calculate the post-fire residual flexural capacity of RC beams. Reliability analysis of the beams retrofitted with CFRPs is subsequently performed using the Monte Carlo method. The effects of fire exposure time, concrete cover thickness and CFRP usage on the reliability of the flexural capacity of RC beams after fire exposure were studied. This new approach was found to be accurate and effective. It provides an effective reliable model for the evaluation of the mechanical properties of post-fire RC beams strengthened with CFRPs.

Publisher

Thomas Telford Ltd.

Subject

Building and Construction,Civil and Structural Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Environmentally friendly fire retardant natural fibre composites: A review;International Materials Reviews;2024-08-02

2. Flexural Performance of Polypropylene Fiber Reinforced Scoria Aggregate Concrete Beams after Exposure to Elevated Temperatures;Journal of Performance of Constructed Facilities;2022-06

3. Flame-retardant strategy and mechanism of fiber reinforced polymeric composite: A review;Composites Part B: Engineering;2022-03

4. Prediction of the Postfire Flexural Capacity of RC Beam Using GA-BPNN Machine Learning;Journal of Performance of Constructed Facilities;2020-12

5. Editorial;Proceedings of the Institution of Civil Engineers - Structures and Buildings;2020-11

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