Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions-Reference-Cited by-同舟云学术

Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions

Published:2024-03 Issue: Volume:17 Page:100311
ISSN:2666-1659
Container-title:Developments in the Built Environment
language:en
Short-container-title:Developments in the Built Environment

Author:

Kumar Aman^ORCID,Arora Harish Chandra,Kumar Prashant^ORCID,Kapoor Nishant Raj^ORCID,Nehdi Moncef L.^ORCID

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Computer Science Applications,Materials Science (miscellaneous),Building and Construction,Civil and Structural Engineering,Architecture

Reference41 articles.

1. Durability assessment of FRP-to-concrete bonded connections under moisture condition using data-driven machine learning-based approaches;Aghabalaei Baghaei;Compos. Struct.,2021

2. A machine learning approach to modelling the bond strength of adhesively bonded joints under water immersion condition;Aghabalaei Baghaei,2022

3. Effect of shear-span/depth ratio on debonding failures of FRP-strengthened RC beams;Al-Saawani;J. Build. Eng.,2020

4. Influence of hygrothermal ageing on the mechanical properties of CFRP-concrete joints and of their components;Al-Lami;Compos. Struct.,2020

5. Random forests;Breiman;Mach. Learn.,2001

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1. Enhancing FRP-concrete interface bearing capacity prediction with explainable machine learning: A feature engineering approach and SHAP analysis;Engineering Structures;2024-11

2. Automated explainable ensemble learning prediction of FRP-bar-to-concrete bond strength and failure pattern under diverse exposure scenarios;Construction and Building Materials;2024-09

3. Estimating the axial strain of circular short columns confined with CFRP under centric compressive static load using ANN and GRA techniques;Heliyon;2024-07

4. Performance Comparison of Artificial Neural Network and Random Forest Models for Predicting the Compressive Strength of Fibre-Reinforced GGBS-Based Geopolymer Concrete Composites;Materials Circular Economy;2024-06-28