Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete-Reference-Cited by-同舟云学术

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Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete

Published:2022-02-16 Issue:5 Volume:23 Page:3331-3358
ISSN:1464-4177
Container-title:Structural Concrete
language:en
Short-container-title:Structural Concrete

Author:

Kina Ceren¹^ORCID,Turk Kazim²^ORCID,Tanyildizi Harun³^ORCID

Affiliation:

1. Nature Sciences and Engineering Faculty, Department of Civil Engineering Malatya Turgut Ozal University Malatya Turkey

2. Engineering Faculty, Department of Civil Engineering Inonu University Malatya Turkey

3. Technology Faculty, Department of Civil Engineering Firat University Elazig Turkey

Publisher

Wiley

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/suco.202100756

Reference81 articles.

1. A comparative study of self-consolidating concretes incorporating high-volume natural pozzolan or high-volume fly ash

2. High-performance fiber-reinforced concrete: a review

3. Capillary absorption in concrete and the Lucas–Washburn equation

4. Mechanical and durability properties of high-strength concrete containing steel and polypropylene fibers

5. Evaluation of water absorption of concrete as a measure for resistance against carbonation and chloride migration

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4. Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms;Structural Concrete;2024-05-19

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