Using artificial neural network and non‐destructive test for crack detection in concrete surrounding the embedded steel reinforcement-Reference-Cited by-同舟云学术

Using artificial neural network and non‐destructive test for crack detection in concrete surrounding the embedded steel reinforcement

Published:2021-06-14 Issue:5 Volume:22 Page:2849-2867
ISSN:1464-4177
Container-title:Structural Concrete
language:en
Short-container-title:Structural Concrete

Author:

Saleem Muhammad¹^ORCID,Gutierrez Hector²

Affiliation:

1. Department of Mechanical and Energy Engineering College of Engineering, Imam Abdulrahman Bin Faisal University Dammam Eastern Province Kingdom of Saudi Arabia

2. Department of Mechanical and Civil Engineering Florida Institute of Technology Melbourne Florida USA

Funder

University of Dammam

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.202000767

Reference47 articles.

1. Seismic Retrofitting Methods Newly Developed for Railway Concrete Structures

2. NolanC.andAndres W.(2019).Investigation of the effects of corrosion on bond strength of steel in concrete using neural network.The 2019 World Congress on Advances in Structural Engineering and Mechanics (ASEM19) Jeju Island Korea September 17–21 2019.

3. The use of USPV to anticipate failure in concrete under compression

4. Latin Hypercube Sensitivity Analysis and Non-destructive Test to Evaluate the Pull-out Strength of Steel Anchor Bolts Embedded in Concrete

5. DEVELOPMENT AND TESTING OF GLOW-IN-THE-DARK CONCRETE BASED RAISED PAVEMENT MARKER FOR IMPROVED TRAFFIC SAFETY

Cited by 38 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. Optimizing pervious concrete with machine learning: Predicting permeability and compressive strength using artificial neural networks;Construction and Building Materials;2024-09

2. Damage evolution in asphalt mixtures based on in-situ CT scanning;Construction and Building Materials;2024-08

3. Internal Damage Detection in Reinforced Concrete Member Using Ultrasonic Pulse Velocity Nondestructive Testing;Journal of Construction Engineering and Management;2024-08

4. Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data;Ultrasonics;2024-07

5. Artificial neural network evaluation of concrete performance exposed to elevated temperature with destructive–non-destructive tests;Neural Computing and Applications;2024-06-04