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Altaf Ahmad,
Shaikh Sanobar,
- Professor, Department of Engineering Chemistry, Al-Kabir Polytechnic, Jamshedpur, India
- Professor, Department of Engineering Chemistry, Al-Kabir Polytechnic, Jamshedpur, India
Abstract
Load bearing concrete structures need steel reinforcement bar (rebar), which are prone to attack by the corrosive environment inside the concrete due to constant ingress of moisture, pollutant gases and anions (mainly Cl−, SO42−). In recent models for the potential life span, the causes of failure of concrete structures have been established principally due to the chloride (Cl−) ion, because of the ease in transportation of Cl− in concrete and its damaging effect. Corrosion of reinforced steel in particular remains a significant challenge, causing great economic losses and safety concerns. However, these methods can be time- consuming, labour intensive and are sometime based on personal judgment, which can lead to inconsistencies. Artificial intelligence (AI) has become a powerful tool for improving corrosion prediction models. It helps in managing data more effectively, detecting corrosion early, forecasting its progress and developing better mitigation strategies. Real-time date on crack development and corrosion rate can be gathered through sensors and imaging technologies. This date is then analysed by machine learning algorithms to spot early warning signs. This proactive approach leads to more accurate results, fewer errors and better decision making.
Keywords: Concrete, Rebar, Corrosion management, Artificial intelligence, Imaging technology
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Journal of Artificial Intelligence Research & Advances
| Volume | 13 | |
| 03 | ||
| Received | 18/04/2026 | |
| Accepted | 04/08/2026 | |
| Published | 30/09/2026 | |
| Publication Time | 165 Days |