Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: A state-of-the-art review-Reference-Cited by-同舟云学术

Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: A state-of-the-art review

Published:2023-12 Issue: Volume:408 Page:133692
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Machello Chiara,Bazli Milad^ORCID,Rajabipour Ali^ORCID,Rad Hooman Mahdizadeh,Arashpour Mehrdad,Hadigheh Ali^ORCID

Publisher

Elsevier BV

Subject

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

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3. Feasibility of using Static-Cast Concrete Transmission Poles fully reinforced with glass-fibre reinforced polymer bars and stirrups: A case study;Ahmadi;Case Stud. Constr. Mater.,2023

4. Bond-slip performance of GFRP and steel reinforced beams under wet-dry and freeze-thaw cycles: The effect of concrete type;Shakiba;Constr. Build. Mater.,2022

5. Using four-point flexure test to investigate effects of temperature and bar size on the tensile properties of GFRP bars;Hosseini;Polym. Test.,2022

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1. Study on the effect of multi-factor compound action on long-term tensile performance of GFRP composite pipe and life prediction analysis;Composite Structures;2024-11

2. Characteristics and Models of Moisture Uptake in Fiber-Reinforced Composites: A Topical Review;Polymers;2024-08-09

3. Using machine learning and experimental study to correlate and predict accelerated aging with natural aging of GFRP composites in hygrothermal conditions;Construction and Building Materials;2024-08

4. Enhancing machining accuracy of banana fiber-reinforced composites with ensemble machine learning;Measurement;2024-08

5. Optimising compressive strength properties of seawater sea sand concrete-filled hybrid carbon-glass fibre-reinforced tubes: A study on the impact of layer sequencing;Construction and Building Materials;2024-08