scholarly journals Comparative Study of Pure Mg and AZ91D as Sacrificial Anodes for Reinforced Cement Concrete Structures in a Chloride Atmosphere

2020 ◽  
Vol 38 (4) ◽  
pp. 243-252
Author(s):  
Y I Murthy ◽  
S Gandhi ◽  
A Kumar
Keyword(s):  
Comparative Study ◽  
Concrete Structures ◽  
Cement Concrete ◽  
Sacrificial Anodes ◽  
Reinforced Cement

scholarly journals Comparative Study of Pure Mg and AZ91D as Sacrificial Anodes for Reinforced Cement Concrete Structures in Chloride Atmosphere

2018 ◽  
Vol 4 (8) ◽  
pp. 1750
Author(s):  
Yogesh Iyer Murthy ◽  
Sumit Gandhi ◽  
Abhishek Kumar
Keyword(s):  
Comparative Study ◽  
Potential Drop ◽  
Ultimate Tensile Stress ◽  
Cement Concrete ◽  
Percentage Elongation ◽  
Half Cell ◽  
Cell Potential ◽  
Reinforced Cement ◽  
Bare Steel ◽  
High Chloride

Comparative study of the corrosion behavior of pure Magnesium and AZ91D anodes in reinforced cement concrete was undertaken in the present work. The steel reinforcements were kept in contact with these anodes electrochemically in chloride atmosphere and the half-cell potential drop was observed. Bare steel reinforcements were tied to the anodes and were also kept in high chloride atmosphere to test the mechanical properties. The yield stress and ultimate tensile stress were found to decrease by approximately 50MPa while the reduction in percentage elongation is approximately 25% for reinforcements tied to AZ91D and pure Mg at the end of 80 days compared to fresh steel reinforcement. The rate of corrosion of pure Mg was reportedly slightly higher compared to AZ91D due to the presence of inter-metallics as inferred through micro-graphs.

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

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