Automated Change Diagnosis of Single-Column-Pier Bridges Based on 3D Imagery Data

Conventional structural defect detection methods, such as visual nspection, in-situ testing and Bayesian model updating, etc., have been widely applied in the condition assessments of bridge structures. However, spatial data collection and processing required by these methods are time-consuming and error-prone, and subject to human experiences. To address these challenges, the paper presents an automated bridge condition assessment method based on 3D laser scanning technique, which can rapidly detail the 3D geometry of bridge structures. With a computer-assisted automatic data analysis algorithm, the geometric variations of bridges can be real-timely traced and quantitatively digitized. Then a structural system identification method is proposed to automatically reveal the developing trend of the collapse mechanism behind these measured geometric variations. By doing this, a timely warning can be given of the possible structural failure. A case study on single-column-pier bridges was given by tracing the variations of bending, torsional and lateral deformations of girders, and correlated deformations of bearings to demonstrate the 3D laser scanning technique for diagnosis of the structural condition and prediction of failure modes. This study concluded that the proposed method is of great potential to identification, prediction, and assessment for the possible defects of existing bridges.