6,241 research outputs found

    Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

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    INE/AUTC 10.0

    Technology transfer-transportation

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    Problems in the public transportation industry and refining methods for decreasing the time gap between the development and the marketing of new technology are considered. Eight NASA innovations are either being adapted for use on highways, railways, or rapid transit, or are already entering the marketplace. Chronologies for three of these programs are provided

    Formulating a Strategy for Securing High-Speed Rail in the United States, Research Report 12-03

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    This report presents an analysis of information relating to attacks, attempted attacks, and plots against high-speed rail (HSR) systems. It draws upon empirical data from MTI’s Database of Terrorist and Serious Criminal Attacks Against Public Surface Transportation and from reviews of selected HSR systems, including onsite observations. The report also examines the history of safety accidents and other HSR incidents that resulted in fatalities, injuries, or extensive asset damage to examine the inherent vulnerabilities (and strengths) of HSR systems and how these might affect the consequences of terrorist attacks. The study is divided into three parts: (1) an examination of security principles and measures; (2) an empirical examination of 33 attacks against HSR targets and a comparison of attacks against HSR targets with those against non-HSR targets; and (3) an examination of 73 safety incidents on 12 HRS systems. The purpose of this study is to develop an overall strategy for HSR security and to identify measures that could be applied to HSR systems currently under development in the United States. It is hoped that the report will provide useful guidance to both governmental authorities and transportation operators of current and future HSR systems

    Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring

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    The objective of this dissertation is to develop a comprehensive Structural Identification (St-Id) framework with damage for bridge type structures by using cameras and computer vision technologies. The traditional St-Id frameworks rely on using conventional sensors. In this study, the collected input and output data employed in the St-Id system are acquired by series of vision-based measurements. The following novelties are proposed, developed and demonstrated in this project: a) vehicle load (input) modeling using computer vision, b) bridge response (output) using full non-contact approach using video/image processing, c) image-based structural identification using input-output measurements and new damage indicators. The input (loading) data due vehicles such as vehicle weights and vehicle locations on the bridges, are estimated by employing computer vision algorithms (detection, classification, and localization of objects) based on the video images of vehicles. Meanwhile, the output data as structural displacements are also obtained by defining and tracking image key-points of measurement locations. Subsequently, the input and output data sets are analyzed to construct novel types of damage indicators, named Unit Influence Surface (UIS). Finally, the new damage detection and localization framework is introduced that does not require a network of sensors, but much less number of sensors. The main research significance is the first time development of algorithms that transform the measured video images into a form that is highly damage-sensitive/change-sensitive for bridge assessment within the context of Structural Identification with input and output characterization. The study exploits the unique attributes of computer vision systems, where the signal is continuous in space. This requires new adaptations and transformations that can handle computer vision data/signals for structural engineering applications. This research will significantly advance current sensor-based structural health monitoring with computer-vision techniques, leading to practical applications for damage detection of complex structures with a novel approach. By using computer vision algorithms and cameras as special sensors for structural health monitoring, this study proposes an advance approach in bridge monitoring through which certain type of data that could not be collected by conventional sensors such as vehicle loads and location, can be obtained practically and accurately

    Precision slew/settle technologies for flexible spacecraft

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    Many spacecraft missions in the next decade will require both a high degree of agility and precision pointing. Agility includes both rotational maneuvering for retargeting and translational motion for orbit adjustment and threat avoidance. The major challenge associated with such missions is the need for control over a wide range of amplitudes and frequencies, ranging from tens of degrees at less than 1 Hz to a few micron radians at hundreds of Hz. TRW's internally funded Precision Control of Agile Spacecraft (PCAS) project is concerned with developing and validating in hardware the tools necessary to successfully complete the combined agile maneuvering/precision pointing missions. Development has been undertaken on a number of fronts for quietly slewing flexible structures. Various methods for designing slew torque profiles have been investigated. Prime candidates for slew/settle scenarios include Inverse Dynamics and Parameterized Function Space. Joint work with Processor Bayo at the University of California, Santa Barbara and Professor Flashner at the University of Southern California has led to promising torque profile design methods. Active and passive vibration suppression techniques also play a key role for rapid slew/settle mission scenarios. Active members with local control loops and passive members with high loss factor viscoelastic material have been selected for hardware verification. Progress in each of these areas produces large gains in the quiet slewing of flexible spacecraft. The main thrust of the effort to date has been the development of a modular testbed for hardware validation of the precision control concepts. The testbed is a slewing eighteen foot long flexible truss. Active and passive members can be interchanged with the baseline aluminum members to augment the inherent damping in the system. For precision control the active members utilize control laws running on a high speed digital structural control processor. Tip and midspan motions of the truss are determined using optical sensors while accelerometers can be used to monitor the motions of other points of interest. Preliminary results indicate that a mix of technologies produces the greatest benefit. For example, shaping the torque profile produces large improvements in slew/settle performance, but without added damping settling times may still be excessive. With the introduction of moderate amounts of damping, slew/settle performance is vastly improved. On the other hand, introducing damping without shaping the torque profile may not yield the desired level of performance

    APPLICATIONS OF MACHINE LEARNING AND COMPUTER VISION FOR SMART INFRASTRUCTURE MANAGEMENT IN CIVIL ENGINEERING

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    Machine Learning and Computer Vision are the two technologies that have innovative applications in diverse fields, including engineering, medicines, agriculture, astronomy, sports, education etc. The idea of enabling machines to make human like decisions is not a recent one. It dates to the early 1900s when analogies were drawn out between neurons in a human brain and capability of a machine to function like humans. However, major advances in the specifics of this theory were not until 1950s when the first experiments were conducted to determine if machines can support artificial intelligence. As computation powers increased, in the form of parallel computing and GPU computing, the time required for training the algorithms decreased significantly. Machine Learning is now used in almost every day to day activities. This research demonstrates the use of machine learning and computer vision for smart infrastructure management. This research’s contribution includes two case studies – a) Occupancy detection using vibration sensors and machine learning and b) Traffic detection, tracking, classification and counting on Memorial Bridge in Portsmouth, NH using computer vision and machine learning. Each case study, includes controlled experiments with a verification data set. Both the studies yielded results that validated the approach of using machine learning and computer vision. Both case studies present a scenario where in machine learning is applied to a civil engineering challenge to create a more objective basis for decision-making. This work also includes a summary of the current state-of-the -practice of machine learning in Civil Engineering and the suggested steps to advance its application in civil engineering based on this research in order to use the technology more effectively

    Structural health monitoring of bridges for improving transportation security

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    Structural health monitoring (SHM) is a promising technology for determining the condition of significant transportation structures objectively for efficient management and preservation of transportation assets. In addition to identifying, locating, and quantifying damage and deterioration due to effects of operation, aging, and natural hazards, the need for taking terrorism-related hazards into account has become evident after 9/11 terrorist attacks. Key transportation facilities like major bridges were identified by Department of Homeland Security (DHS) as possible terrorist targets since their loss or even temporary deficiency could lead to major impacts on economy and mobility. Several governmental, local, and private organizations have been working on identifying possible modes of threats, determining and sorting vulnerable structures, and establishing ways to prevent, detect and respond to such attacks. Authorities are also investigating ways to integrate current and future bridge management systems with security surveillance systems. Highway bridges are key links of the transportation system. This paper reviews security measures for bridges and discuss possible integration of structural health and security monitoring for improving security and safety of bridges and emergency management after a natural or man-made disaster
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