7,509 research outputs found

    Bridge Structrural Health Monitoring Using a Cyber-Physical System Framework

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    Highway bridges are critical infrastructure elements supporting commercial and personal traffic. However, bridge deterioration coupled with insufficient funding for bridge maintenance remain a chronic problem faced by the United States. With the emergence of wireless sensor networks (WSN), structural health monitoring (SHM) has gained increasing attention over the last decade as a viable means of assessing bridge structural conditions. While intensive research has been conducted on bridge SHM, few studies have clearly demonstrated the value of SHM to bridge owners, especially using real-world implementation in operational bridges. This thesis first aims to enhance existing bridge SHM implementations by developing a cyber-physical system (CPS) framework that integrates multiple SHM systems with traffic cameras and weigh-in-motion (WIM) stations located along the same corridor. To demonstrate the efficacy of the proposed CPS, a 20-mile segment of the northbound I-275 highway in Michigan is instrumented with four traffic cameras, two bridge SHM systems and a WIM station. Real-time truck detection algorithms are deployed to intelligently trigger the SHM systems for data collection during large truck events. Such a triggering approach can improve data acquisition efficiency by up to 70% (as compared to schedule-based data collection). Leveraging computer vision-based truck re-identification techniques applied to videos from the traffic cameras along the corridor, a two-stage pipeline is proposed to fuse bridge input data (i.e. truck loads as measured by the WIM station) and output data (i.e. bridge responses to a given truck load). From August 2017 to April 2019, over 20,000 truck events have been captured by the CPS. To the author’s best knowledge, the CPS implementation is the first of its kind in the nation and offers large volume of heterogeneous input-output data thereby opening new opportunities for novel data-driven bridge condition assessment methods. Built upon the developed CPS framework, the second half of the thesis focuses on use of the data in real-world bridge asset management applications. Long-term bridge strain response data is used to investigate and model composite action behavior exhibited in slab-on-girder highway bridges. Partial composite action is observed and quantified over negative bending regions of the bridge through the monitoring of slip strain at the girder-deck interface. It is revealed that undesired composite action over negative bending regions might be a cause of deck deterioration. The analysis performed on modeling composite action is a first in studying composite behavior in operational bridges with in-situ SHM measurements. Second, a data-driven analytical method is proposed to derive site-specific parameters such as dynamic load allowance and unit influence lines for bridge load rating using the input-output data. The resulting rating factors more rationally account for the bridge's systematic behavior leading to more accurate rating of a bridge's load-carrying capacity. Third, the proposed CPS framework is shown capable of measuring highway traffic loads. The paired WIM and bridge response data is used for training a learning-based bridge WIM system where truck weight characteristics such as axle weights are derived directly using corresponding bridge response measurements. Such an approach is successfully utilized to extend the functionality of an existing bridge SHM system for truck weighing purposes achieving precision requirements of a Type-II WIM station (e.g. vehicle gross weight error of less than 15%).PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163210/1/rayhou_1.pd

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

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    INE/AUTC 10.0

    Truck-based mobile wireless sensor networks for the experimental observation of vehicle–bridge interaction

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    Heavy vehicles driving over a bridge create a complex dynamic phenomenon known as vehicle–bridge interaction. In recent years, interest in vehicle–bridge interaction has grown because a deeper understanding of the phenomena can lead to improvements in bridge design methods while enhancing the accuracy of structural health monitoring techniques. The mobility of wireless sensors can be leveraged to directly monitor the dynamic coupling between the moving vehicle and the bridge. In this study, a mobile wireless sensor network is proposed for installation on a heavy truck to capture the vertical acceleration, horizontal acceleration and gyroscopic pitching of the truck as it crosses a bridge. The vehicle-based wireless monitoring system is designed to interact with a static, permanent wireless monitoring system installed on the bridge. Specifically, the mobile wireless sensors time-synchronize with the bridge's wireless sensors before transferring the vehicle response data. Vertical acceleration and gyroscopic pitching measurements of the vehicle are combined with bridge accelerations to create a time-synchronized vehicle–bridge response dataset. In addition to observing the vehicle vibrations, Kalman filtering is adopted to accurately track the vehicle position using the measured horizontal acceleration of the vehicle and positioning information derived from piezoelectric strip sensors installed on the bridge deck as part of the bridge monitoring system. Using the Geumdang Bridge (Korea), extensive field testing of the proposed vehicle–bridge wireless monitoring system is conducted. Experimental results verify the reliability of the wireless system and the accuracy of the vehicle positioning algorithm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90810/1/0964-1726_20_6_065009.pd

    Wireless Sensing System for Load Testing and Rating of Highway Bridges

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    Structural capacity evaluation of bridges is an increasingly important topic in the effort to deal with the deteriorating infrastructure. Most bridges are evaluated through subjective visual inspection and conservative theoretical rating. Diagnostic load test has been recognized as an effective method to accurately assess the carrying capacity of bridges. Traditional wired sensors and data acquisition (DAQ) systems suffer drawbacks of being labor intensive, high cost, and time consumption in installation and maintenance. For those reasons, very few load tests have been conducted on bridges.;This study aims at developing a low-cost wireless bridge load testing & rating system that can be rapidly deployed on bridges for structural evaluation and load rating. Commercially available wireless hardware is integrated with traditional analogue sensors and the appropriate rating software is developed. The wireless DAQ system can work with traditional strain gages, accelerometers as well as other voltage producing sensors. A wireless truck position indicator (WVPI) is developed and used for measuring the truck position during load testing. The software is capable of calculating the theoretical rating factors based on AASHTO Load Resistance Factor Rating (LRFR) codes, and automatically produces the adjustment factor through load testing data. A simplified finite element model was used to calculate deflection & moment distribution factors in order to reduce the amount of instrumentation used in field tests. The system was used to evaluate the structural capacity of Evansville Bridge in Preston County, WV. The results show that the wireless bridge load testing & rating system can effectively be implemented to evaluate the real capacity of bridges with remarkable advantages: low-cost, fast deployment and smaller crew

    Using Low-cost IoT-based inclinometers for damage detection of a Bridge model

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    Nowadays, researchers are paying close attention to using inclinometers for Structural Health Monitoring (SHM) applications. Moreover, the applications based on using inclinometers can detect the magnitude and location of bridge pathologies. However, as these applications are based on expensive commercial inclinometers, their use is typically exclusive to the SHM of structures with a high monitoring budget. There is a gap in the literature with the development and validation of low-cost accurate angular-meters for decreasing the monitoring cost of inclinometer-based damage detection applications. This work aims to develop low-cost IoT-based inclinometers for detecting damage in bridge structures. The Low-cost Adaptable Reliable Angle-meter (LARA) is a novel inclinometer that accurately measures an induced inclination by combining the measurements of five gyroscopes and five accelerometers. The accuracy, resolution, Allan variance, and standard deviation of LARA are examined through laboratory experiments and are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC). For further experimental validation, a robotic vehicle model is designed and developed to simulate a moving load over a bridge model. The vehicle model integrates IoT technology and can be utilized in different damage detection experiments. The outcomes of a load test experiment using a simple beam model demonstrate the high accuracy (0.003 degrees) of LARA measurements. LARA may be used for structural damage identification and location in bridges utilizing inclinometers because of its low cost and high accuracy

    A Systematic Literature Survey of Unmanned Aerial Vehicle Based Structural Health Monitoring

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    Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs based on their aerodynamics, payload, design of build, and its applications. Further, the thesis presents the payload product line to facilitate the SHM tasks, details the different applications of UAVs exploited in the last decade to support civil structures, and discusses the critical challenges faced in UASHM applications across various domains. Finally, the thesis presents two artificial neural network-based structural damage detection models and conducts a detailed performance evaluation on multiple platforms like edge computing and cloud computing

    Structural health monitoring of asphalt pavements using smart sensor networks: A comprehensive review

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    Abstract Early, effective and continuous monitoring allows to reduce costs and to extend life of road infrastructure. For this reason, over the years, more and more efforts have been made to implement more advanced and effective monitoring systems at ever more contained costs, going from impractical manual and destructive methods through automated in vehicle equipment to the most recent wireless sensor network (WSN) embedded into the pavement. The purpose of this paper is to provide a comprehensive, up-to-date critical literature review of wireless sensor networks for pavement health monitoring, considering, also, the experience gained for wired sensor as fundamental point of reference. This work presents both the methodology used to collect and analyse the current bibliography and provides a description and comments fundamental characteristics of wireless sensor networks for pavement monitoring for damage detection purposes, among which energy supply, the detection method, the hardware and network architecture and the performance validation procedures. A brief analysis of other possible complementary applications of smart sensor networks, such as traffic and surface condition monitoring, is provided. Finally, a comment is provided on the gaps and possible directions that future research could follow to allow the extensive use of wireless sensor networks for pavement health condition monitoring

    D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

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    This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin
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