Abstract
Chloride-induced steel corrosion seriously affects the durability of reinforced concrete structures. Rubber concrete, an environmentally friendly construction material in which waste rubber is recycled as a concrete component, has demonstrated superior resistance to chloride-induced steel corrosion and the subsequent concrete deterioration. However, quantitative evaluation of the degree of deterioration in rubber concrete based on nondestructive detection is challenging due to the complexity of the material. In this paper, reinforced concrete specimens with rubber contents of 0, 10% and 20% are subjected to the electrochemically accelerated corrosion experiments and monitored by ultrasonic testing. Six machine learning models are trained by the data derived from the ultrasonic testing to predict the corrosion degree based on ultrasonic traits. The results show that the machine learning models except for the linear model can accurately and robustly predict the corrosion degree under the interference of outlier amplitude and size of training set. Furthermore, the corrosion-induced deterioration process is computed by mesoscale simulation based on the corrosion degree, so that the damages of specimens with different rubber contents are quantitatively evaluated. The experimental and computational studies prove that the addition of rubber into concrete effectively retards the corrosion of steel and the deterioration of concrete.
Recommended Citation
J. Zhang et al., "Quantitative Evaluation of Steel Corrosion Induced Deterioration in Rubber Concrete by Integrating Ultrasonic Testing, Machine Learning and Mesoscale Simulation," Cement and Concrete Composites, vol. 128, article no. 104426, Elsevier, Apr 2022.
The definitive version is available at https://doi.org/10.1016/j.cemconcomp.2022.104426
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Corrosion degree; Machine learning; Mesoscale simulation; Robustness validation; Rubber concrete; Ultrasonic signals
International Standard Serial Number (ISSN)
0958-9465
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Elsevier, All rights reserved.
Publication Date
01 Apr 2022
Comments
Shenzhen University, Grant 2020B1212060074