Structural Health Monitoring of Corrosion in Reinforced Concrete: A Key Component for Smart Cities
Purpose: The purpose of this study is to develop and validate a scientific framework for detecting and monitoring reinforcement corrosion in reinforced concrete structures through the integration of the Magnetic Force Induced Vibration Evaluation (M5) method, Artificial Intelligence (AI), and Internet of Things (IoT) technologies. The research aims to demonstrate that this approach can serve as a foundation for proactive and sustainable infrastructure management in smart cities. Design/Methodology/Approach: The study employed a systematic literature review (SLR) following PRISMA standards to identify state-of-the-art methods for corrosion diagnostics in reinforced concrete. Based on the SLR results, the Magnetic Force Induced Vibration Evaluation (M5) method was selected and experimentally validated as a core component of a Structural Health Monitoring (SHM) system. The research combined AI-based Association Rules Analysis (ARA) for signal interpretation with IoT integration to enable real-time, non-destructive monitoring of corrosion processes. Findings: Research shows that reinforcement corrosion in reinforced concrete (RC) structures is a major challenge for the construction industry, significantly hindering smart city development. One of the few effective methods for detecting corrosion in structural health monitoring (SHM) is the Magnetic Force Induced Vibration Evaluation (M5) method. The newly developed Association Rules Analysis (ARA) technique reveals that M5 frequency characteristics can indicate corrosion by damping specific resonant frequencies of the structure. Integrating M5-based SHM systems with the IoT can prevent structural failures and extend the lifespan of RC structures. This integration not only helps avoid construction disasters but also achieves cost savings, reduces material usage, and lowers CO2 emissions, fostering the growth of smart, sustainable cities. Practical Implications: The outcomes highlight opportunities to extend the service life of reinforced concrete structures, reduce inspection costs, and align infrastructure management with sustainability goals by lowering material consumption and emissions. Implementing these in smart city contexts could enable proactive maintenance and early warning systems, reducing costs and risks, and promoting sustainable urban development. Originality/Value: The study introduces an innovative integration of modal analysis diagnostics, AI machine learning, and IoT communication to develop an advanced SHM system.