Trung H. Duong

RABIT: implementation, performance validation and integration with other robotic platforms for improved management of bridge decks

Accurate condition assessment and monitoring of concrete bridge deck deterioration progression requires both use of multiple nondestructive evaluation (NDE) technologies and automation in data collection and analysis. RABIT (robotics assisted bridge inspection tool) for bridge decks enables fully autonomous data collection at rates three or more times higher than it is typically done by a team of five inspectors using manual NDE technologies. The system concentrates on the detection and characterization of three most common internal deterioration and damage types: rebar corrosion, delamination, and concrete degradation. For that purpose, RABIT implements four NDE technologies: electrical resistivity (ER), ground-penetrating radar (GPR), impact echo (IE) and ultrasonic surface waves (USW) method. High productivity and higher spatial data resolution are achieved through the use of large sensor arrays or multiple probes for the four NDE methods. RABIT surveys also complement visual inspection by collecting high resolution images of the deck surface, which can be used for crack mapping and documentation of deck spalling, previous repairs, etc. The NDE technologies are used in a complementary way to enhance the overall condition assessment, certainty regarding the detected deterioration and better identification of the primary cause of deterioration. RABIT’s components, operation, field implementation and validation, as well as future integration with a robotic platform for minimally invasive rehabilitation, are described.

Concrete bridge deck early problem detection and mitigation using robotics

More economical management of bridges can be achieved through early problem detection and mitigation. The paper describes development and implementation of two fully automated (robotic) systems for nondestructive evaluation (NDE) and minimally invasive rehabilitation of concrete bridge decks. The NDE system named RABIT was developed with the support from Federal Highway Administration (FHWA). It implements multiple NDE technologies, namely: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW). In addition, the system utilizes advanced vision to substitute traditional visual inspection. The RABIT system collects data at significantly higher speeds than it is done using traditional NDE equipment. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. The interpretation and visualization platform specifically addresses data integration and fusion from the four NDE technologies. The data visualization platform facilitates an intuitive presentation of the main deterioration due to: corrosion, delamination, and concrete degradation, by integrating NDE survey results and high resolution deck surface imaging. The rehabilitation robotic system was developed with the support from National Institute of Standards and Technology-Technology Innovation Program (NIST-TIP). The system utilizes advanced robotics and novel materials to repair problems in concrete decks, primarily early stage delamination and internal cracking, using a minimally invasive approach. Since both systems use global positioning systems for navigation, some of the current efforts concentrate on their coordination for the most effective joint evaluation and rehabilitation.

Attenuation-Based Methodology for Condition Assessment of Concrete Bridge Decks Using GPR

Reflection amplitude at top rebar layer has been used as a main criterion for evaluating attenuation of ground-penetrating radar (GPR) data from concrete bridge decks. However, a recent study has pointed out the limitation of this practice. Motivated by that same research, the current paper presents a robust method for performing GPR attenuation analysis. Also based on correlation between A-scans, however, instead of baseline data, semi-simulated waveforms are used in this approach. With only one reflection representing direct coupling, these waveforms mimic A-scans collected from completely damaged location. The output obtained is then plotted in form of a contour map of correlation coefficient in which higher value indicates more deteriorated concrete. As a validation, the method was implemented for two bare concrete bridge decks. The result indicates that while the maps provided by other technologies and GPR are geometrically correlated, in comparison with conventional amplitude analysis, the proposed model provides better vision on overall deterioration of

Three-Dimensional Visualization and Presentation of Bridge Deck Condition Based on Multiple NDE Data

AbstractA method is developed for presentation of a concrete bridge deck condition assessed by multiple nondestructive evaluation (NDE) technologies using a three-dimensional (3D) visualization pro...

Automatic Dishware Inspection: Applications and Comparisons of Two New Methods

Commercial dishwashing systems currently involve human loading, sorting, inspecting, and unloading dishes and silverware pieces before and after washing in hot and humid environments. In such difficult working conditions, leading to high turn-over of low-paid employees, automation is desirable, especially in large-scale kitchens of hospitals, navy ships, schools, hotels and other dining facilities. Our project is a part of developing an integrated machine vision sorting and inspecting system for mixed dish pieces and silverware exiting a flight-type commercial dishwashing machine, coupled with automatic loading and unloading. We propose two new methods for automatically inspecting dish cleanliness, namely adaptive thresholding and maximum saliency map. On the first method, a new technique using partitioning and adaptive thresholding, combined with global thresholding are introduced. On the second method, we propose a new normalization technique. Both algorithms are fast, simple, and produce results invariant with lighting conditions and dish rotation about the camera-dish axis. Algorithms are written and tested by MatlabÒ R14 and Image Processing Toolbox V5.0 to 110 dish images taken in different lighting condition using different position of 51 separate dishes (either clean or dirty) of our dish set, in which 77 images are from dirty dishes with 799 dirty points in these dishes. The adaptive thresholding method produces 95.0% and 96.5% accuracies in discriminating clean from dirty dishes and dirty spot detection, respectively. While the maximum saliency map method produces 100% accuracies in discriminating clean from dirty dishes and 93.5% accuracies in and dirty spot detection.

Advanced Integration and 3-D Visualization of Data from Multiple Nondestructive Evaluation Technologies

This paper presents a novel method for presenting the concrete bridge deck condition assessed by multiple nondestructive evaluation (NDE) technologies using a three-dimensional (3D) visualization program, NDEFuse. Six types of NDE data can be visualized: (1) impact-echo data for mapping and describing the depth and severity level of concrete delamination, (2) ultrasonic surface wave data for concrete quality (elastic modulus) assessment, (3) electrical resistivity for the description of corrosive environment and estimate of the corrosion rate of steel reinforcement, (4) mapping of top rebar layer and (5) ground penetrating radar condition assessment data, and (6) high-resolution deck surface image for documenting signs of deterioration, previous repairs, and surface wear. The 3D visualization platform integrates and visualizes the six NDE results in a very intuitive way. Correlations between the different types of NDE survey results were established visually in a 3D space utilizing the developed program.