AI-Powered Magnetic Inspection Robot for Advanced Structural Health Monitoring of Ferromagnetic Structures

This paper presents an innovative solution to the growing problem of infrastructure deterioration in the United States, where approximately one-third of facilities are in poor condition, and over 130,000 steel bridges have exceeded their intended lifespan. Aging infrastructure, particularly steel structures, is vulnerable to corrosion and hidden defects, which pose significant safety risks. The Silver Bridge collapse, where an undetected flaw resulted in the tragic loss of 46 lives, underscores the limitations of traditional inspection methods reliant on manual human observation. These methods often fail to detect subtle or concealed defects, leading to catastrophic failures. In response to this urgent need for improved inspection technologies, this work introduces an AI-powered magnetic inspection robot. The robot is equipped with magnetic wheels that enable it to adhere to and navigate complex ferromagnetic surfaces, including vertical inclines, internal corners, and curved elements. Its robust mobility allows it to access hard-to-reach areas and conduct comprehensive inspections, offering a versatile solution for largescale infrastructure assessment. Additionally, the system incorporates advanced machine learning techniques, utilizing MobileNetV2, a deep learning architecture, for defect detection. Trained on a large dataset of steel surface defects, the model achieved 85% precision, demonstrating strong performance across six distinct defect types. The robot's deep learning-driven inspection process significantly enhances the accuracy and reliability of structural assessments, surpassing traditional methods in terms of defect detection and operational efficiency. The findings of this project suggest that integrating robotic systems with AI-driven image analysis offers a transformative approach to infrastructure inspection, providing a scalable, automated solution that can reduce human labor, increase detection precision, and improve the safety and sustainability of critical infrastructure assets