Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged areas. Two synthetic datasets with planar and cylindrical geometries with known ground truth damage were created and used to test the developed workflow. In addition, the developed method was further validated on three real-world point cloud datasets using lidar and structure-from-motion techniques, which represented different underlying geometries and exhibited varying severity and mechanisms of damage. The analysis of the synthetic datasets demonstrated the robustness of the proposed damage detection method to classify vertices as surface damage with high recall and precision rates and a low false-positive rate. The real-world datasets illustrated the scalability of the damage detection method and its ability to classify areas as damaged and undamaged at the centimeter level. Moreover, the output classification of the damage detection method automatically bins the damaged vertices into different confidence intervals for further classification of detected likely damaged areas. Moving forward, the presented workflow can be used to bolster structural inspections by reducing subjectivity, enhancing reliability, and improving quantification in surface-evident damage.

Unmanned aerial vehicle (UAV) imagery has recently emerged as a promising alternative for operational condition inspection and postdisaster damage assessment of civil structures (e.g., bridges and buildings). However, the use of such sensing techniques for quantitatively tracking the subtle (centimeter-level) variations of the responses of structures has been limited. This is largely due to the difficulties related to obtaining accurate location measurements of the cameras aboard small UAV platforms. To address this research gap, we propose a video analysis methodology for tracking the displacement response of buildings subject to dynamic loads using camera-equipped UAV platforms. The movement of the image sensor on-board the UAV platform is corrected to allow image-by-image natural feature detection and tracking. In this methodology, the image processing procedure does not rely on the camera position and orientation. As such, the approach first corrects for image distortion introduced by UAV drift and subsequently extracts the dynamic displacements of the building by tracking its natural features at the pixel level. Motion-tracking errors are investigated by analyzing the building displacements using pre- and postevent videos. The proposed methodology is validated by monitoring the dynamic response of a full-scale building during a shake table test program. Uniquely, global positioning system (GPS) displacement measurements are independently utilized to validate the proposed UAV video-based method and assess its effectiveness for capturing the dynamic responses of full-scale structures with a level of precision that is sufficient for engineering applications (less than 2 cm root-mean-square errors).

The dynamic response of freestanding structures is poorly predicted due to known sensitivity to geometry and input motion. To further elucidate the salient features of this dynamic response, the aim of this study is to investigate the response of complex freestanding systems under real-world conditions. Two statue-pedestal systems that overturned during the 2014 South Napa Earthquake are employed in this study. The results of the numerical simulation are analyzed in comparison to the observed response which unlocks key details regarding the impact of soil at the base, contact stiffness, statue orientation, and ground motion directionality.

Understanding the dynamics of precariously balanced rocks (PBRs) is important for seismic hazard analysis and rockfall prediction. Utilizing a physics engine and robotic tools, we develop a virtual shake robot (VSR) to simulate the dynamics of PBRs during overturning and large-displacement processes. We present the background of physics engines and technical details of the VSR, including software architecture, mechanical structure, control system, and implementation procedures. Validation experiments show the median fragility contour from VSR simulation is within the 95% prediction intervals from previous physical experiments, when PGV/PGA is greater than 0.08 s. Using a physical mini shake robot, we validate the qualitative consistency of fragility anisotropy between the VSR and physical experiments. By overturning cuboids on flat terrain, the VSR reveals the relationship between fragility and geometric dimensions (e.g., aspect and scaling ratios). The ground motion orientation and lateral pedestal support affect PBR fragility. Large-displacement experiments estimate rock trajectories for different ground motions, which is useful for understanding the fate of toppled PBRs. Ground motions positively correlate with large displacement statistics such as mean trajectory length, mean largest velocity, and mean terminal distance. The overturning and large displacement processes of PBRs provide complementary methods of ground motion estimation.

The goal of this investigation is to assess the performance of rural structures in natural hazard events in 2018. Digital reconnaissance was implemented to retrieve data on damage-inflicting events, where collected data included a general summary of the event, the severity of damage inflicted, and the types of structures affected. To better understand the benefits and shortcomings of digital reconnaissance, findings are compared with those of traditional, field reconnaissance for two hazard events. The 2018 digital reconnaissance database is analyzed to discern regional, structural, and event-type trends and the fragility of specific rural structural systems. The investigation identified that, in 2018, damaged rural structures substantially outnumbered damaged urban structures, and the vast majority of damaging events occurred in the South and Midwest regions of the United States. Structures typical of rural areas, particularly barns, grain bins/silos, and manufactured homes, proved especially vulnerable, with many of these structures suffering complete failure in events causing only minor damage to structures more typical of urban areas. However, a comparison with field reconnaissance indicated that quantities of damaged structures are likely underestimated through digital reconnaissance, particularly in rural areas.