A multi-level 3D data registration approach for supporting reliable spatial change classification of single-pier bridges

This paper presents a new multi-level 3D data registration and spatial change classification approach that automate the analysis of both element-level deformations and interactions between the motions of multiple elements based on deviations calculated between two 3D data sets. This approach uses a multi-level data registration method augmented by formalized knowledge for representing spatial changes using deviation maps between two 3D datasets. This knowledge will guide pattern analysis methods to reveal how various changes of structures collectively lead to structural systems behaviors. More specifically, this 3D data registration and spatial change classification approach eliminates deviations caused by rigid body motions before assessing deformations of structural elements. The authors conducted annual 3D imagery data collection for two single pier bridges in July 2015 and June 2016, and use those 3D data to characterize the performance of the new approach in identifying relative motions between and deformations of structural elements. The results indicate that the new approach can reliably identify relative motions between and deformations of bridge elements, such as angular changes between elements, and torsions of girders. Finally, the authors validated the change analysis results generated by the developed approach against the traditional change analysis results obtained by a knowledgeable structural engineering researcher and change analyses in multiple single-pier bridge research studies.