Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete-Reference-Cited by-同舟云学术

Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete

Published:2022-12 Issue: Volume:359 Page:129504
ISSN:0950-0618
Container-title:Construction and Building Materials
language:en
Short-container-title:Construction and Building Materials

Author:

Cavaleri Liborio,Barkhordari Mohammad Sadegh,Repapis Constantinos C.,Armaghani Danial Jahed,Ulrikh Dmitrii Vladimirovich,Asteris Panagiotis G.

Publisher

Elsevier BV

Subject

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

Reference89 articles.

1. Stress-strain response of corroded reinforcing bars under monotonic and cyclic loading;Kashani,2012

2. Probabilistic prediction model for RC bond failure mode;Soraghi;Eng. Struct.,2021

3. Mechanical model for the shear strength prediction of corrosion-damaged reinforced concrete slender and non slender beams;Cladera;Eng. Struct.,2021

4. Prediction of chloride diffusion in cement mortar using multi-gene genetic programming and multivariate adaptive regression splines;Hoang;Measurement,2017

5. Global needs for knowledge dissemination, research, and development in materials deterioration and corrosion control;Schmitt;World Corros. Organ.,2009

Cited by 56 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. Determination of concrete compressive strength from surface images with the integration of CNN and SVR methods;Measurement;2024-10

2. Finite element data-driven deep learning-based tensile failure analysis of precast bridge slab joint;Engineering Failure Analysis;2024-10

3. A kind of multi-dot ensemble regression AI detector for lubricating oil additive content based on lambert-beer law;Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy;2024-10

4. A new method for CBR prediction using fuzzy set theory;Construction and Building Materials;2024-10

5. Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches;Structures;2024-10