Hooman Parvardeh

Mechatronic Systems Design for an Autonomous Robotic System for High-Efficiency Bridge Deck Inspection and Evaluation

The condition of bridges is critical for the safety of the traveling public. Bridges deteriorate with time as a result of material aging, excessive loading, environmental effects, and inadequate maintenance. The current practice of nondestructive evaluation (NDE) of bridge decks cannot meet the increasing demands for highly efficient, cost-effective, and safety-guaranteed inspection and evaluation. In this paper, a mechatronic systems design for an autonomous robotic system for highly efficient bridge deck inspection and evaluation is presented. An autonomous holonomic mobile robot is used as a platform to carry various NDE sensing systems for simultaneous and fast data collection. The robots NDE sensor suite includes ground penetrating radar arrays, acoustic/seismic arrays, electrical resistivity sensors, and video cameras. Besides the NDE sensors, the robot is also equipped with various onboard navigation sensors such as global positioning system (GPS), inertial measurement units (IMU), laser scanner, etc. An integration scheme is presented to fuse the measurements from the GPS, the IMU and the wheel encoders for high-accuracy robot localization. The performance of the robotic NDE system development is demonstrated through extensive testing experiments and field deployments.

Automated Crack Detection on Concrete Bridges

Detection of cracks on bridge decks is a vital task for maintaining the structural health and reliability of concrete bridges. Robotic imaging can be used to obtain bridge surface image sets for automated on-site analysis. We present a novel automated crack detection algorithm, the STRUM (spatially tuned robust multifeature) classifier, and demonstrate results on real bridge data using a state-of-the-art robotic bridge scanning system. By using machine learning classification, we eliminate the need for manually tuning threshold parameters. The algorithm uses robust curve fitting to spatially localize potential crack regions even in the presence of noise. Multiple visual features that are spatially tuned to these regions are computed. Feature computation includes examining the scale-space of the local feature in order to represent the information and the unknown salient scale of the crack. The classification results are obtained with real bridge data from hundreds of crack regions over two bridges. This comprehensive analysis shows a peak STRUM classifier performance of 95% compared with 69% accuracy from a more typical image-based approach. In order to create a composite global view of a large bridge span, an image sequence from the robot is aligned computationally to create a continuous mosaic. A crack density map for the bridge mosaic provides a computational description as well as a global view of the spatial patterns of bridge deck cracking. The bridges surveyed for data collection and testing include Long-Term Bridge Performance programs (LTBP) pilot project bridges at Haymarket, VA, USA, and Sacramento, CA, USA.

Autonomous robotic system for high-efficiency non-destructive bridge deck inspection and evaluation

Bridges are one of the critical civil infrastructure for safety of traveling public. The conditions of bridges deteriorate with time as a result of material aging, excessive loading, and inadequate maintenance, etc. In this paper, the development of an autonomous robotic system is presented for highly-efficient bridge deck inspection and evaluation. An autonomous mobile robot is used as a platform to carry various non-destructive evaluation (NDE) sensing systems for simultaneous and fast data collection. Besides the NDE sensors, the robot is also equipped with various onboard navigation sensors. A sensing integration scheme is presented for high-accuracy robot localization and navigation. The effectiveness of the autonomous robotic NDE system is demonstrated through extensive experiments and field deployments.

Nondestructive evaluation inspection of the Arlington Memorial Bridge using a robotic assisted bridge inspection tool (RABIT)

The information presented in this report provides a detailed assessment of the condition of the Arlington Memorial Bridge (AMB) deck. The field-data collection was obtained by both the RABIT™ Bridge Inspection Tool and a number of semi-automated non-destructive evaluation (NDE) tools. The deployment of the semi-automated NDE tools was performed to inspect the AMB deck condition and also to validate data obtained by the RABIT™ Bridge Inspection Tool. Data mining and analysis were accomplished through enhanced data interpretation and visualization capabilities using advanced data integration, fusion, and 2D rendering. One of the major challenges that the research team had to overcome in assessing the condition of the AMB deck was the presence of an asphalt overlay on the entire bridge deck.

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.

Life Cycle Assessment Framework for the U.S. Bridge Inventory

The effective management of bridges requires a good understanding of their life expectancies. Improved prediction of bridge service life is required to be developed in order to better understand bridge deterioration and to find more effective maintenance and repair strategies. These models are integral components of the Long-Term Bridge Performance Program (LTBP), a 20-year research effort initiated by the U.S. Federal Highway Administration (FHWA) to improve the understanding of bridge performance. In this paper, the development of a life expectancy model framework, as part of the research effort in this program, is presented. The framework is established based on a semi-probabilistic approach to adherently maintain the advantages of both deterministic and probabilistic techniques. The modeling follows a step-by-step process which incorporates data collected from historical records, training the data, creating a model based on the most suitable approach, and reducing the associated uncertainties. The basic model is first trained by the network of bridge inventory and the uncertainties are reflected by determining lower and upper margins. Then the model is improved by introducing the new knowledge gained from the external attributes influencing the structure. Finally, the condition states of the bridge components are employed directly to refine the model for realistic assessment. The developed model is later automated into the Bridge Portal, the main core of the bridge-performance data warehouse. A detailed example using the Mid-Atlantic cluster bridge inventory data is presented in this paper to illustrate the application of the method described above.