A Statistical Approach to Point Cloud Analysis for Infrastructure Assessment
Abstract
Engineers implement structural health monitoring and nondestructive evaluation techniques to effectively assess the status and safety of civil infrastructure. In addition to the financial cost, the logistical burden involved with traditional techniques includes a large suite of hardware and sensors for data collection, and this data is typically carried forward into computer simulations using finite element models to fully understand the behavior of the structure. However, with the continued emergence of computer vision techniques, engineers are looking at new methods for data collection to support infrastructure assessment. The use of these techniques enables the collection of point cloud data, which is a compilation of spatial data points (typically defined in the Cartesian coordinate system) that are located on the surface of the target structure, through sensor packages that can be as simple as a single digital camera. The point cloud data, which can also be collected through laser scanners, is unique in that it can capture the full 3D geometry and deformations of a structure while other data typically provides information only at individual sensor locations. However, the nature of point clouds is usually unstructured and noisy, requiring statistical techniques for its analysis. As such, this research investigates a new pathway for evaluation and assessment of civil infrastructure using point cloud data in a manner that provides accurate and organized representation of surface deformations, gives explicit measurement uncertainty quantification, and uses the data in a unique manner to update computer simulation models. This approach is unique because the performance metrics of the target structure are therefore provided as a range of values that reflect the uncertainty in the collected data. Such a pathway is significant not only because it demonstrates the implementation of data that is collected with a lower cost and lighter logistical burden than traditional methods, but also because it provides information that allows decision-makers to quantify risk when determining future steps for infrastructure maintenance or remediation. This research pathway is divided into three tasks: point cloud registration and deformation measurement, surface definition and uncertainty quantification, and finite element model updating. The data sets for this research include point clouds at both the laboratory scale and the field scale. The laboratory scale data includes point clouds of 3D printed shapes that represent different deformation patterns and buckling modalities of structural components. The field scale data includes point clouds of a highway bridge in Delaware and point clouds of a stiffened floor panel used in the decks of large maritime vessels.
Description
Keywords
Computer vision, Deformation tracking, Finite element model, Kriging, Point cloud