Chul Min Yeum

Vision‐Based Automated Crack Detection for Bridge Inspection

The visual inspection of bridges demands long inspection time and also makes it difficult to access all areas of the bridge. This paper presents a visual-based crack detection technique for the automatic inspection of bridges. The technique collects images from an aerial camera to identify the presence of damage to the structure. The images are captured without controlling angles or positioning of cameras so there is no need for calibration. This allows the extracting of images of damage sensitive areas from different angles to increase detection of damage and decrease false-positive errors. The images can detect cracks regardless of the size or the possibility of not being visible. The effectiveness of this technique can be used to successfully detect cracks near bolts.

Acceleration-Based Automated Vehicle Classification on Mobile Bridges

Mobile bridges have been used for a broad range of applications including military transportation or disaster restoration. Because mobile bridges are rapidly deployed under a wide variety of conditions, often remaining in place for just minutes to hours, and have irregular usage patterns, a detailed record of usage history is important for ensuring structural safety. To facilitate usage data collection in mobile bridges, a new acceleration-based vehicle classification technique is proposed to automatically identify the class of each vehicle. Herein we present a new technique that is based on the premise that each class of vehicles produces distinctive dynamic patterns while crossing this mobile bridge, and those patterns can be extracted from the systems acceleration responses. Measured acceleration signals are converted to time-frequency images to extract two-dimensional patterns. The Viola-Jones object detection algorithm is applied here to extract and classify those patterns. The effectiveness of the technique is investigated and demonstrated using laboratory and full-scale mobile bridges by simulating realistic scenarios.

Automated region-of-interest localization and classification for vision-based visual assessment of civil infrastructure:

Complementary advances in computer vision and new sensing platforms have mobilized the research community to pursue automated methods for vision-based visual evaluation of our civil infrastructure. Spatial and temporal limitations typically associated with sensing in large-scale structures are being torn down through the use of low-cost aerial platforms with integrated high-resolution visual sensors. Despite the enormous efforts expended to implement such technology, practical real-world challenges still hinder the application of these methods. The large volumes of complex visual data, collected under uncontrolled circumstances (e.g. varied lighting, cluttered regions, occlusions, and variations in environmental conditions), impose a major challenge to such methods, especially when only a tiny fraction of them are used for conducting the actual assessment. Such difficulties induce undesirable high rates of false-positive and false-negative errors, reducing both trustworthiness and efficiency in the methods. To overcome these inherent challenges, a novel automated image localization and classification technique is developed to extract the regions of interest on each of the images, which contain the targeted region for inspection. Regions of interest are extracted here using structure-from-motion algorithm. Less useful regions of interest, such as those corrupted by occlusions, are then filtered effectively using a robust image classification technique, based on convolutional neural networks. Then, such highly relevant regions of interest are available for visual assessment. The capability of the technique is successfully demonstrated using a full-scale highway sign truss with welded connections.

Computer-Aided Approach for Rapid Post-Event Visual Evaluation of a Building Façade

After a disaster strikes an urban area, damage to the façades of a building may produce dangerous falling hazards that jeopardize pedestrians and vehicles. Thus, building façades must be rapidly inspected to prevent potential loss of life and property damage. Harnessing the capacity to use new vision sensors and associated sensing platforms, such as unmanned aerial vehicles (UAVs) would expedite this process and alleviate spatial and temporal limitations typically associated with human-based inspection in high-rise buildings. In this paper, we have developed an approach to perform rapid and accurate visual inspection of building façades using images collected from UAVs. An orthophoto corresponding to any reasonably flat region on the building (e.g., a façade or building side) is automatically constructed using a structure-from-motion (SfM) technique, followed by image stitching and blending. Based on the geometric relationship between the collected images and the constructed orthophoto, high-resolution region-of-interest are automatically extracted from the collected images, enabling efficient visual inspection. We successfully demonstrate the capabilities of the technique using an abandoned building of which a façade has damaged building components (e.g., window panes or external drainage pipes).

A Researcher-oriented Automated Data Ingestion Tool for rapid data Processing, Visualization and Preservation

Abstract A select number of scientific communities have been quite successful in evolving the culture within their community to encourage publishing and to provide resources for re-using well-documented data. These data have great potential for analysis and knowledge generation beyond the purposes for which they were collected and intended. However, there are still barriers in this process. To explore this problem, we have developed a prototype tool: the Experiment Dashboard (ED), with the objective of demonstrating the ability and potential of enabling automated data ingestion from typical research laboratories. This innovative prototype was developed to create a novel system and artifact to explore the possibilities of allowing researchers in laboratories across the nation to link their data acquisition systems directly to structured data repositories for data and metadata ingestion. The prototype functions with commonly used data acquisition software at the data source and the HUBzero scientific gateway at the data sink. ED can be set up with minimal effort and expertise. In this paper, we describe the motivation and purposes for the prototype, the architecture we devised and functionality of this tool, and provide a demonstration of the tool for optical measurements in a structural engineering laboratory. The goal of this paper is to articulate and show through our prototype a vision for future cyberinfrastructure for empirical disciplines that rely on the rapid collection, analysis, and dissemination of valuable experimental data. We also discuss lessons learned that may be useful for others seeking to solve similar problems.