This paper presents a novel method for identifying damage in reinforced concrete (RC) bridges, utilizing macro-strain data from distributed long-gauge sensors installed on the concrete surface.
The method relies on the principle that heavy vehicles induce larger dynamic vibrations, leading to increased strain and crack formation compared to lighter vehicles. By comparing the absolute macro-strain ratio (AMSR) of a reference sensor with a network of distributed sensors, damage locations can be effectively pinpointed from a single data collection session. Finite-element modeling was employed to validate the method's efficacy, demonstrating that the AMSR ratio increases significantly in the presence of cracks. Experimental validation was conducted on a real-world bridge in Japan, confirming the method's reliability under normal traffic conditions.
This approach offers a practical and efficient means of detecting bridge damage, potentially enhancing the safety and longevity of infrastructure systems.
Original research paper.
