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

The reliability of condition assessment of bridges using 3D imagery data, such as 3D laser scanning point clouds, relies on inspectors’ structural engineering knowledge and skills of 3D data processing. A challenge of 3D-data-based structural condition assessment lies in the difficulties of reliably comparing 3D imagery data sets collected at different times for analyzing spatial changes of the structures and finding anomalous deformations. Spatial changes of structures could occur at multiple levels of details and be of different types: (1) rigid body motions (e.g., translations and rotations) at the structure or structural element levels; (2) deformations (e.g., bending of girders) at the levels of structural elements. Unfortunately, existing 3D imagery data-based change analysis methods only produce deviations between two 3D data sets without distinguishing deviations caused by various changes at multiple levels. Significant rigid body motions of structures and structural elements often cause large deviations that “overwhelm” deviation patterns caused by smaller element-level deformations so that engineers could hardly recognize local deformations. Unreliable deformation analysis of structural elements can lead to incorrect condition assessments. 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.