Accurate and rapid condition assessment of in-service structural components is critical to ensure safety and serviceability. One major assessment consideration is the detection and quantification of structural section loss due to deterioration, for instance, from corrosion. Modern three-dimensional (3D) imaging techniques, which generate high-resolution 3D point clouds, are capable of detecting and measuring these deteriorations. However, despite advancements in the fields of automated point cloud analysis for as-built modeling and structural inspection, the potential use of spatial 3D data for updating numerical finite-element (FE) models of structures is still an emergent topic. This paper presents a localized methodology for the automatic and systematic detection and quantification of damages in structural components using high-fidelity 3D point cloud data, followed by a corresponding local update to an FE model. In this study, 3D point cloud data of a targeted structure were first obtained by using dense structure from motion (DSfM) algorithms. Section loss damage was then identified and located through computer vision and 3D data processing techniques. In order to preserve data integrity and resolve localized high-fidelity details, direct 3D point cloud comparisons were performed. An experimental study validating the developed approach is presented as well. The results indicate that the presented methodology will enable engineers to use the updated structural model to determine the reserved capacity and remaining service life of structural elements, though further studies on methods to improve mesh generation and defect quantification are warranted.

The evaluation of geometric defects is necessary in order to maintain the integrity of structures over time. These assessments are designed to detect damages of structures and ideally help inspectors to estimate the remaining life of structures. Current methodologies for monitoring structural systems, while providing useful information about the current state of a structure, are limited in the monitoring of defects over time and in linking them to predictive simulation. This paper presents a new approach to the predictive modeling of geometric defects. A combination of segments from point clouds are parametrized using the convex hull algorithm to extract features from detected defects, and a stochastic dynamic model is then adapted to these features to model the evolution of the hull over time. Describing a defect in terms of its parameterized hull enables consistent temporal tracking for predictive purposes, while implicitly reducing data dimensionality and complexity as well. In this study, two-dimensional (2D) point clouds analogous to information derived from point clouds were firstly generated over simulated life cycles. The evolutions of point cloud hull parameterizations were modeled as stochastic dynamical processes via autoregressive integrated moving average (ARIMA) and vectorized autoregression (VAR) and compared against ground truth. The results indicate that this convex hull approach provides consistent and accurate representations of defect evolution across a range of defect topologies and is reasonably robust to noisy measurements; however, assumptions regarding the underlying dynamical process play a significant the role in predictive accuracy. The results were then validated on experimental data from fatigue testing with high accuracy. Longer term, the results of this work will support finite element model updating for predictive analysis of structural capacity.

The evaluation of geometric defects is necessary in order to maintain the integrity of structures over time. These assessments are designed to detect damages of structures and ideally help inspectors to estimate the remaining life of structures. Current methodologies for monitoring structural systems, while providing useful information about the current state of a structure, are limited in the monitoring of defects over time and in linking them to predictive simulation. This paper presents a new approach to the predictive modeling of geometric defects. A combination of segmented from point clouds are parametrized using the convex hull algorithm to extract features from detected defects, and a stochastic dynamic model is then adapted to these features to model the evolution of the hull over time. Describing a defect in terms of its parameterized hull enables consistent temporal tracking for predictive purposes, while implicitly reducing data dimensionality and complexity as well. In this study, 2D point clouds analogous to information derived from point clouds were first generated over simulated life-cycles. The evolutions of point cloud hull parameterizations were modeled as stochastic dynamical processes via autoregressive integrated moving average (ARIMA) and vectorized autoregression (VAR) and compared against ground truth. The results indicate that this convex hull approach provides consistent and accurate representations of defect evolution across a range of defect topologies and is reasonably robust to noisy measurements, however assumptions regarding the underlying dynamical process play a significant the role in predictive accuracy. The results were then validated on experimental data from fatigue testing with high accuracy. Longer term, the results of this work will support finite element model updating for predictive analysis of structural capacity.