Long-term monitoring of damage progression using multi-view images from routine inspections of bridges

Surface damage is an important indicator for evaluating the safety and durability of bridges. As manual visual inspections are often time-consuming and risky, alternative approaches are essential for effective assessments. Computer vision-based methods have shown the potential for improved inspection utilizing a set of images acquired during a bridge inspection. However, because the position and orientation of a camera are different even for the same damage in routine inspections, tracking the progression of damage over time is challenging in practice. This study presents an integrated framework for the long-term monitoring of structural damage based on multi-view images captured from routine inspections of bridges. In terms of qualitative assessment, a time series of images are grouped for individual damages based on a similarity index. Afterward, a group of pixels for structural damage in each image are mapped onto a common coordinate system, tracking the progression of the same damage over time. In the case of quantitative assessment, a scale conversion factor is calculated using the global navigation satellite system data, in which a plane fitting algorithm is employed to calculate the damage area in physical units. The proposed method is validated using an in-service bridge over a period of 120 days, enabling the long-term assessment of crack, spalling, and water leakage with a maximum error of 4.61%.