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Author

Hooman Parvardeh

Bio: Hooman Parvardeh is an academic researcher from Rutgers University. The author has contributed to research in topics: Bridge (interpersonal) & Mobile robot. The author has an hindex of 6, co-authored 13 publications receiving 398 citations.

Papers
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Journal ArticleDOI
TL;DR: A novel automated crack detection algorithm, the STRUM (spatially tuned robust multifeature) classifier, is presented, and results on real bridge data using a state-of-the-art robotic bridge scanning system are demonstrated.
Abstract: 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 program's (LTBP) pilot project bridges at Haymarket, VA, USA, and Sacramento, CA, USA.

292 citations

Journal ArticleDOI
TL;DR: In this article, an autonomous holonomic mobile robot is used as a platform to carry various NDE sensing systems for simultaneous and fast data collection, including ground penetrating radar arrays, acoustic/seismic arrays, electrical resistivity sensors, and video cameras.
Abstract: 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 robot's 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.

121 citations

Proceedings ArticleDOI
07 Nov 2013
TL;DR: The development of an autonomous robotic system is presented for highly-efficient bridge deck inspection and evaluation and an autonomous mobile robot is used as a platform to carry various non-destructive evaluation (NDE) sensing systems for simultaneous and fast data collection.
Abstract: 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.

55 citations

01 Jan 2011
TL;DR: In this paper, the authors demonstrate the capabilities of ground-penetrating radar (GPR) and impact echo (IE) for detecting and characterizing deterioration in bridge decks.
Abstract: The primary objective of this research was to demonstrate the benefits of non-destructive testing (NDT) technologies for effectively detecting and characterizing deterioration in bridge decks. In particular, the objectives were to demonstrate the capabilities of ground-penetrating radar (GPR) and impact echo (IE), and to evaluate and describe the condition of nine bridge decks proposed by Iowa Department of Transportation (DOT). The first part of the report provides a detailed review of the most important deterioration processes in concrete decks, followed by a discussion of the five NDT technologies utilized in this project. In addition to GPR and IE methods, three other technologies were utilized, namely: half-cell (HC) potential, electrical resistivity (ER), and ultrasonic surface waves (USW) method. The review includes a description of the principles of operation, field implementation, data analysis, and interpretation; information regarding their advantages and limitations in bridge deck evaluations and condition monitoring are also implicitly provided.. The second part of the report provides descriptions and bridge deck evaluation results from the nine bridges. The results of the NDT surveys are described in terms of condition assessment maps and are compared with the observations obtained from the recovered cores or conducted bridge deck rehabilitation. Results from this study confirm that the used technologies can provide detailed and accurate information about a certain type of deterioration, electrochemical environment, or defect. However, they also show that a comprehensive condition assessment of bridge decks can be achieved only through a complementary use of multiple technologies at this stage,. Recommendations are provided for the optimum implementation of NDT technologies for the condition assessment and monitoring of bridge decks.

21 citations

Proceedings ArticleDOI
TL;DR: 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.
Abstract: 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.

12 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: A crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images and it was found that cracks are reasonably detected and crack density is also accurately evaluated.

624 citations

Journal ArticleDOI
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.

500 citations

Journal ArticleDOI
TL;DR: The state‐of‐the‐art methods have been presented by conducting a detailed literature review of the recent applications of smartphones, UAVs, cameras, and robotic sensors used in acquiring and analyzing the vibration data for structural condition monitoring and maintenance.

301 citations

Journal ArticleDOI
TL;DR: A novel automated crack detection algorithm, the STRUM (spatially tuned robust multifeature) classifier, is presented, and results on real bridge data using a state-of-the-art robotic bridge scanning system are demonstrated.
Abstract: 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 program's (LTBP) pilot project bridges at Haymarket, VA, USA, and Sacramento, CA, USA.

292 citations