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

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

Bridge Structrural Health Monitoring Using a Cyber-Physical System Framework

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

Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors

Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe

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

INE/AUTC 10.0

Simplified Vehicle-Bridge Interaction for Medium to Long-span Bridges Subject to Random Traffic Load

This study introduces a simplified model for bridge-vehicle interaction for medium- to long-span bridges subject to random traffic loads. Previous studies have focused on calculating the exact response of the vehicle or the bridge based on an interaction force derived from the compatibility between two systems. This process requires multiple iterations per time step per vehicle until the compatibility is reached. When a network of vehicles is considered, the compatibility equation turns to a system of coupled equations which dramatically increases the complexity of the convergence process. In this study, we simplify the problem into two sub-problems that are decoupled: (a) a bridge subject to random Gaussian excitation, and (b) individual sensing agents that are subject to a linear superposition of the bridge response and the road profile roughness. The study provides sufficient evidence to confirm the simulation approach is valid with a minimal error when the bridge span is medium to long, and the spatio-temporal load pattern can be modeled as random Gaussian. Quantitatively, the proposed approach is over 1,000 times more computationally efficient when compared to the conventional approach for a 500 m long bridge, with response prediction errors below $0.1\%$.Comment: submitted to the Journal of Civil Structural Health Monitorin

Wireless Sensing System for Load Testing and Rating of Highway Bridges

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

Damage identification of supporting structures with a moving sensory system

© 2017 Elsevier Ltd An innovative approach to identify local anomalies in a structural beam bridge with an instrumented vehicle moving as a sensory system across the bridge. Accelerations at both the axle and vehicle body are measured from which vehicle-bridge interaction force on the structure is determined. Local anomalies of the structure are estimated from this interaction force with the Newton's iterative method basing on the homotopy continuation method. Numerical results with the vehicle moving over simply supported or continuous beams show that the acceleration responses from the vehicle or the bridge structure are less sensitive to the local damages than the interaction force between the wheel and the structure. Effects of different movement patterns and moving speed of the vehicle are investigated, and the effect of measurement noise on the identified results is discussed. A heavier or slower vehicle has been shown to be less sensitive to measurement noise giving more accurate results

Real life Applications of Internet of Things

The Internet of Things is the next technological revolution after the revolution of computer and internet. IoT integrates the new technologies of computing and communication (e.g. Sensor networks, RFID, Mobile communication and IPV6 etc). The Internet of Things is an emerging topic of technical, social, and economic significance. The term Internet of Things generally refers to scenarios where network connectivity and computing capability extends to objects, sensors and everyday items not normally considered computers, allowing these devices to generate exchange and consume data with minimal human intervention. Internet connect “all people”, Internet of Things connect “all things”. Interconnection of Things or Objects or Machines, e.g., sensors, actuators, mobile phones, electronic devices, home appliances, any existing items and interact with each other via Interne

Wireless Monitoring Systems for Long-Term Reliability Assessment of Bridge Structures based on Compressed Sensing and Data-Driven Interrogation Methods.

The state of the nation’s highway bridges has garnered significant public attention due to large inventories of aging assets and insufficient funds for repair. Current management methods are based on visual inspections that have many known limitations including reliance on surface evidence of deterioration and subjectivity introduced by trained inspectors. To address the limitations of current inspection practice, structural health monitoring (SHM) systems can be used to provide quantitative measures of structural behavior and an objective basis for condition assessment. SHM systems are intended to be a cost effective monitoring technology that also automates the processing of data to characterize damage and provide decision information to asset managers. Unfortunately, this realization of SHM systems does not currently exist. In order for SHM to be realized as a decision support tool for bridge owners engaged in performance- and risk-based asset management, technological hurdles must still be overcome. This thesis focuses on advancing wireless SHM systems. An innovative wireless monitoring system was designed for permanent deployment on bridges in cold northern climates which pose an added challenge as the potential for solar harvesting is reduced and battery charging is slowed. First, efforts advancing energy efficient usage strategies for WSNs were made. With WSN energy consumption proportional to the amount of data transmitted, data reduction strategies are prioritized. A novel data compression paradigm termed compressed sensing is advanced for embedment in a wireless sensor microcontroller. In addition, fatigue monitoring algorithms are embedded for local data processing leading to dramatic data reductions. In the second part of the thesis, a radical top-down design strategy (in contrast to global vibration strategies) for a monitoring system is explored to target specific damage concerns of bridge owners. Data-driven algorithmic approaches are created for statistical performance characterization of long-term bridge response. Statistical process control and reliability index monitoring are advanced as a scalable and autonomous means of transforming data into information relevant to bridge risk management. Validation of the wireless monitoring system architecture is made using the Telegraph Road Bridge (Monroe, Michigan), a multi-girder short-span highway bridge that represents a major fraction of the U.S. national inventory.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116749/1/ocosean_1.pd

A Model-driven Architecture for Multi-protocol OBD Emulator

The Internet of Things (IoT) might be the next revolutionary technology to mark a generation. It could have a particularly strong influence on the automotive industry, changing people’s perception of what a vehicle can do. By connecting several things in a car, IoT empowers it to sense and communicate. Furthermore, this technology clearly opens the way to emerging applications such as automated driving, Vehicle-to-Vehicle and Vehicleto-Infrastructure communication. Vehicle’s information about its environment and surroundings is crucial to the development of existing and emerging applications. It is already possible to communicate directly (on-site) with vehicles through a built-in On Board Diagnostics (OBD), making it possible to obtain crucial information about the state of the vehicle in real environments. However, there is zero tolerance for error when developing new applications for vehicles that are, a priori, extremely costly and that must also safeguard human lives. Therefore, there is an increasing need for OBD emulators which can allow the development of new applications. This Thesis proposes a model-driven architecture for multi-protocol OBD emulator, encouraging the development of new emerging OBD systems in a safety environment, to promote the creation of applications to interact or use vehicles’ data. In this sense, the addressed specifications are: Less expensive comparing with today’s solutions; Compatible with different OBD protocols communication; Open Source Hardware and Software suitable for Do-It-Yourself (DIY) development