This paper explores the intersection between forensic science and Structural Health Monitoring (SHM), focusing on the pivotal role of visual indicators. These indicators are crucial in both contexts - from discerning injuries on humans to identifying structural defects. We present a novel approach utilizing computer vision-based diagnostics to aid victims of violence through advanced bruise detection, thus enhancing post-trauma care. Leveraging a specialized dataset, our study confronts the challenges inherent in data preparation and organization, as well as achieving expert consensus. We modify lightweight deep learning algorithms originally developed for engineered system diagnostics for application in the medical forensics domain. This adaptation aims to detect bruise areas under varying conditions, such as differences in skin color and lighting. A key question we address is the generalizability of these methods in diverse medical bruising scenarios, a fundamental challenge shared with SHM. Our research highlights the importance of domain knowledge transfer, drawing parallels between SHM and forensic science, and underscores the potential of this interdisciplinary approach.
Video monitoring of public spaces is becoming increasingly ubiquitous, particularly near essential structures and facilities. During any hazard event that dynamically excites a structure, such as an earthquake or hurricane, proximal video cameras may inadvertently capture the motion time-history of the structure during the event. If this dynamic time-history could be extracted from the repurposed video recording it would become a valuable forensic analysis tool for engineers performing post-disaster structural evaluations. The difficulty is that almost all potential video cameras are not installed to monitor structure motions, leading to camera perspective distortions and other associated challenges. This paper presents a method for extracting structure motions from videos using a combination of computer vision techniques. Images from a video recording are first reprojected into synthetic images that eliminate perspective distortion, using as-built knowledge of a structure for calibration. The motion of the camera itself during an event is also considered. Optical flow, a technique for tracking per-pixel motion, is then applied to these synthetic images to estimate the building motion. The developed method was validated using the experimental records of the NEESHub earthquake database. The results indicate that the technique is capable of estimating structural motions, particularly the frequency content of the response. Further work will evaluate variants and alternatives to the optical flow algorithm, as well as study the impact of video encoding artifacts on motion estimates.
Modern remote sensing technologies have enabled the creation of high-resolution 3D point clouds of infrastructure systems. In particular, photogrammetric reconstructions using Dense-Structure-from-Motion algorithm can now yield point clouds with the necessary resolution to capture small-strain displacements. By tracking changes in these point clouds over time, displacements can be measured, leading to strain and stress estimates for long-term structural evaluations. This study determines the accuracy of a comparative point cloud analysis technique for measuring deflections in high-resolution point clouds of structural elements. Utilizing a combination of a recently developed point cloud generation process and localized nearest-neighbors cloud comparisons, the analytical technique is designed for long-term field scenarios and requires no artificial tracking, targets, and camera calibrations. A series of flexural laboratory experiments were performed in order to test the approach. The results indicate sub-millimeter accuracy in measuring the vertical deflection, making it suitable for the small-displacement analysis of a variety of large-scale infrastructure systems. Ongoing work seeks to extend this technique for comparison with as-built and finite element models.
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