Modeling of Steel-Reinforced Grout Composite System-to-Concrete Bond Capacity Using Artificial Neural Networks-Reference-Cited by-同舟云学术

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Modeling of Steel-Reinforced Grout Composite System-to-Concrete Bond Capacity Using Artificial Neural Networks

Published:2024-10 Issue:5 Volume:28 Page:
ISSN:1090-0268
Container-title:Journal of Composites for Construction
language:en
Short-container-title:J. Compos. Constr.

Author:

Ombres Luciano¹^ORCID,Aiello Maria Antonietta²,Cascardi Alessio³^ORCID,Verre Salvatore⁴^ORCID

Affiliation:

1. Professor, Dept. of Civil Engineering, Univ. of Calabria, 87036 Cosenza, Italy (corresponding author). ORCID: .

2. Professor, Dept. of Innovation Engineering, Univ. of Salento, 73100 Lecce, Italy.

3. Assistant Professor, Dept. of Civil Engineering, Univ. of Calabria, 87036 Cosenza, Italy. ORCID: .

4. Assistant Professor, Faculty of Engineering, Univ. eCampus, 22060 Novedrate, Como, Italy. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCOF2.CCENG-4453

Reference39 articles.

1. Masonry columns confined with fabric reinforced cementitious matrix (FRCM) systems: A round robin test

2. Experimental bond behavior of Steel Reinforced Grout systems for strengthening concrete elements

3. Bond-slip models for the interface between steel fabric reinforced cementitious matrix and concrete substrate

4. A qualification method for externally bonded Fibre Reinforced Cementitious Matrix (FRCM) strengthening systems

5. ASTM. 2016. Standard test method for compressive strength of concrete cylinder specimens. ASTM C39. West Conshohocken, PA: ASTM.

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1. Accurate Prediction of Compression Index of Normally Consolidated Soils Using Artificial Neural Networks;Buildings;2024-08-28

2. Parametric Investigation of Corner Effect on Soil Nailed Walls and Prediction Using Machine Learning Methods;Applied Sciences;2024-08-20

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