| dc.description.abstract | The collapse of the Nanfang′ao Bridge in 2019 was caused by insufficient load-bearing capacity due to corrosion of the suspension system. However, steel reinforcement corrosion typically occurs within the structure and is difficult to detect externally. Following this incident, Taiwan began to emphasize the importance of detecting steel reinforcement corrosion for structural safety. This event also highlighted a challenge in Taiwan′s rapidly developing society and economy, where urban population density is increasing, and limited land resources restrict the construction of new buildings. In order to effectively utilize land resources and reinforce existing structures to extend their lifespan, a need arises for a fast and convenient method to assess structural conditions.Hence, there is a need for a fast and convenient inspection method for assessing structural conditions. This research proposes a novel structural inspection approach based on passive wireless sensing technology and machine learning. Low-cost sensors are installed inside the structure, and machine learning techniques are used for data analysis to determine the presence of corrosion in the steel reinforcement, achieving a low-cost, rapid, and labor-saving structural inspection process. The study′s findings indicate that the machine learning model, trained on filtered data, achieves a high accuracy rate of up to 90.9% for identifying steel reinforcement corrosion, with a recall rate of 93.3%, demonstrating accurate corrosion detection.The experiments also show that beams with and without steel reinforcement corrosion exhibit different crack patterns. This difference allows the beams to display varying water absorption abilities, and by using intelligent sensing tags to detect the moisture difference between different specimens, the identification of steel reinforcement corrosion can be accomplished.The results of this study are expected to detect early signs of steel reinforcement corrosion and provide a basis for structural reinforcement, thereby enhancing structural safety. Compared to traditional electrochemical testing methods, the proposed new inspection approach is not only more cost-effective but also more feasible and practical. | en_US |