A model for time-to-cracking of concrete due to chloride induced corrosion using artificial neural network

• Main Authors: Concha, Nolan C., Oreta, Andres Winston C.
• Format: text
• Published: Animo Repository 2018
• Subjects: Concrete—Cracking; Concrete—Compression testing; Concrete—Testing; Neural networks (Computer science); Civil Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3722; https://animorepository.dlsu.edu.ph/context/faculty_research/article/4724/type/native/viewcontent/072009.html
• Institution: De La Salle University

To monitor the initiation of concrete cracking beyond the service life of the structure, a novel prediction model of time to cracking of concrete cover using artificial neural network (ANN) was developed in this study. Crack mitigation prevents corrosion and crack development to occur in a more rapid phase that is an essential component in performance-based durability design of reinforced concrete structures. Data available in various literatures were used in the development of the ANN model which is a function of compressive strength, tensile strength, concrete cover, rebar diameter, and current density. The neural network model was able to provide reasonable results in time predictions of cracking of concrete protective cover due to formations of corrosion products. The performance of ANN model was also compared to various analytical and empirical models and was found to provide better prediction results. Even with limitations in the available training data, the ANN model performed well in simulating cracking of concrete due to reinforcement corrosion. © 2018 Institute of Physics Publishing. All rights reserved.