Optimizing Corrosion Prediction Initiation Time for Embedded Steel in Concrete with Shell Powders Using Deep Learning Techniques

The embedded steel is integrated with concrete material, primarily used in buildings and infrastructure projects. "Embedded steel" refers to steel reinforcement bars or mesh embedded in concrete structures. Steel is added to the concrete to strengthen and support the structure. One of the primary challenges associated with embedded-based steel is anticipating its corrosion once it has been incorporated into building structures. It is necessary to monitor the initiation time of corrosion on the steel in the concrete, which is considered crucial to the environment. Early corrosion detection is challenging, and its accuracy helps design durable concrete. This process reduces the time and cost of embedded steel manufacturing. This research focuses on applying embedded deep-learning models to test the accuracy of the algorithms suggested for embedded steel. A-state of art technique reveals that convolutional neural network (CNN), Long short-term memory (LSTM), and Deep neural network (DNN) models can perform accurate predictions. In this study, the above deep learning models are embedded to validate the accuracy of the different algorithms. The study aimed to determine the corrosion initiation time on steel, which is Incorporated within concrete via corrosion potential measurement. To achieve this, concrete samples were arranged with conch shell powder as a partial replacement to Portland cement and exposed in 5% sodium chloride with following the requirements of ASTM C876 – 15. During the exposure time, the steel embedded's corrosion potential was measured, and the resulting dataset was utilized for training three deep-learning models. These models were developed using input variables such as cement, conch shell powder, fine aggregate, coarse aggregate, exposure period, and water to estimate the corrosion initiation time on the embedded- steel based on the potential corrosion measurements.