B. Basily

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.

A continuous folding process for sheet materials

In this paper, we present a new and innovative sheet material folding technology and the associated advances in folding different patterns using continuous manufacturing techniques. A novel approach is developed for the continuous folding process where sheet material is progressively folded in two dimensions, through a set of rollers, followed by a configured roller for the final folding in the third dimension. The final roller can be designed for longitudinal folding, cross-folding and angular folding to produce the desired folded pattern. This process is more economical than the traditional forming processes. An application of this process to the production of impact energy absorption structures is presented.

Dynamic axial crushing of multilayer core structures of folded Chevron patterns

The objective of this paper is to investigate our recently developed innovative sheet folding theory and manufacturing processes in designing impact energy absorbing structures with superior properties to existing structures, such as honeycomb, while achieving a volume reduction of between 40 and 50%. Initial results indicate that we can mathematically generate three-dimensional patterns and use our folding technology to produce such patterns by simply folding flat sheets of materials, resulting in significant cost savings. The three-dimensional patterns, folded from different sheet materials, can be used as cores for laminated structures for impact energy absorption applications, such as in high speed airdrops of heavy items and in improving crash worthiness of vehicle body and bumpers. The results of testing samples of the Chevron patterns (the simplest to fold from flat sheets) indicate that core structures made from this pattern will serve as absorbers of high velocity impact energy per unit volume when compared with the well known and typically used honeycomb structures.

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.

Computer-vision based crack detection and analysis

Cracks on a bridge deck should be ideally detected at an early stage in order to prevent further damage. To ensure safety, it is necessary to inspect the quality of concrete decks at regular intervals. Conventional methods usually include manual inspection of concrete surfaces to determine defects. Though very effective, these methods are time-inefficient. This paper presents the use of computer-vision techniques in detection and analysis of cracks on a bridge deck. High quality images of concrete surfaces are captured and subsequently analyzed to build an automated crack classification system. After feature extraction using the training set images, statistical inference algorithms are employed to identify cracks. The results demonstrate the feasibility of the proposed crack observation and classification system.

Implementation of a Fully Autonomous Platform for Assessment of Concrete Bridge Decks RABIT

RABIT (Robotics Assisted Bridge Inspection Tool) provides rapid and automated condition assessment of concrete bridge decks using multiple nondestructive evaluation (NDE) technologies integrated into a robotic platform. In particular, the system is designed to characterize the three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. For that purpose, the system uses four technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW). In addition, the RABIT has two cameras for high resolution bridge deck imaging to provide a permanent record of the visible deterioration and surface features. The autonomous data collection is complemented by an advanced data analysis, interpretation and visualization. Most of the data analysis is conducted in real or near real time. The results are presented in terms of condition maps and condition indices. The data visualization platform facilitates an intuitive 3-dimensional presentation of the main three deterioration types and deck surface features. The data collection, analysis, and results presentation by the RABIT system are described and illustrated.

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.

Evaluation of a novel piezoelectric bridge transducer

A novel piezoelectric bridge transducer was developed for an energy harvesting application from vehicle induced loading on pavement. A unique electrode design enables the PZT to be poled horizontally, enabling the d33 piezoelectric coefficient to be utilized by the transducer, enhancing energy output. The transducers were fabricated and assembled in an energy harvesting module and the output energy and power was measured under simulated vehicle loading. In this work, the effective piezoelectric coefficient of the transducer has been measured using the direct piezoelectric effect as well as the converse piezoelectric effect to evaluate the transducer for an actuator or sensor application. The effective d33 measured by the direct method is 19,000 pC/N and the g33 is 2150 × 10−3 Vm/N. The reliability and cycles to failure of the transducer design is studied and the transducers are evaluated after 50,000 loading cycles. Inconsistency in the epoxy layer thickness has been identified as the cause of premature failure.