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

Short and Long-Term Structural Health Monitoring of Highway Bridges

Structural Health Monitoring (SHM) is a promising tool for condition assessment of bridge structures. SHM of bridges can be performed for different purposes in long or short-term. A few aspects of short- and long-term monitoring of highway bridges are addressed in this research. Without quantifying environmental effects, applying vibration-based damage detection techniques may result in false damage identification. As part of a long-term monitoring project, the effect of temperature on vibrational characteristics of two continuously monitored bridges are studied. Natural frequencies of the structures are identified from ambient vibration data using the Natural Excitation Technique (NExT) along with the Eigen System Realization (ERA) algorithm. Variability of identified natural frequencies is investigated based on statistical properties of identified frequencies. Different statistical models are tested and the most accurate model is selected to remove the effect of temperature from the identified frequencies. After removing temperature effects, different damage cases are simulated on calibrated finite-element models. Comparing the effect of simulated damages on natural frequencies showed what levels of damage could be detected with this method. Evaluating traffic loads can be helpful to different areas including bridge design and assessment, pavement design and maintenance, fatigue analysis, economic studies and enforcement of legal weight limits. In this study, feasibility of using a single-span bridge as a weigh-in-motion tool to quantify the gross vehicle weights (GVW) of trucks is studied. As part of a short-term monitoring project, this bridge was subjected to four sets of high speed, live-load tests. Measured strain data are used to implement bridge weigh-in-motion (B-WIM) algorithms and calculate the corresponding velocities and GVWs. A comparison is made between calculated and static weights, and furthermore, between supposed speeds and estimated speeds of the trucks. Vibration-based techniques that use finite-element (FE) model updating for SHM of bridges are common for infrastructure applications. This study presents the application of both static and dynamic-based FE model updating of a full scale bridge. Both dynamic and live-load testing were conducted on this bridge and vibration, strain, and deflections were measured at different locations. A FE model is calibrated using different error functions. This model could capture both global and local response of the structure and the performance of the updated model is validated with part of the collected measurements that were not included in the calibration process

An Enhanced Bridge Weigh-in-motion Methodology and A Bayesian Framework for Predicting Extreme Traffic Load Effects of Bridges

In the past few decades, the rapid growth of traffic volume and weight, and the aging of transportation infrastructures have raised serious concerns over transportation safety. Under these circumstances, vehicle overweight enforcement and bridge condition assessment through structural health monitoring (SHM) have become critical to the protection of the safety of the public and transportation infrastructures. The main objectives of this dissertation are to: (1) develop an enhanced bridge weigh-in-motion (BWIM) methodology that can be integrated into the SHM system for overweight enforcement and monitoring traffic loading; (2) present a Bayesian framework to predict the extreme traffic load effects (LEs) of bridges and assess the implication of the growing traffic on bridge safety. Firstly, an enhanced BWIM methodology is developed. A comprehensive review on the BWIM technology is first presented. Then, a novel axle detection method using wavelet transformation of the bridge global response is proposed. Simulation results demonstrate that the proposed axle detection method can accurately identify vehicle axles, except for cases with rough road surface profiles or relatively high measurement noises. Furthermore, a two-dimensional nothing-on-road (NOR) BWIM algorithm that is able to identify the transverse position (TP) and axle weight of vehicles using only weighing sensors is proposed. Results from numerical and experimental studies show that the proposed algorithm can accurately identify the vehicle’s TP under various conditions and significantly improve the identification accuracy of vehicle weight compared with the traditional Moses’s algorithm. Secondly, a Bayesian framework for predicting extreme traffic LEs of bridges is presented. The Bayesian method offers a natural framework for uncertainty quantification in parameter estimation and thus can provide more reliable predictions compared with conventional methods. A framework for bridge condition assessment that utilizes the predicted traffic LEs is proposed and a case study on the condition assessment of an instrumented field bridge is presented to demonstrate the proposed methodology. Moreover, the non-stationary Bayesian method is adopted to predict the maximum traffic LEs during the lifetime of bridges subject to different types of traffic growth and the influence of the traffic growth on the bridge safety is investigated

ICWIM8 - 8th Conference on Weigh-in-Motion - Book of proceedings

ICWIM8, 8th International Conference on Weigh-in-Motion, PRAGUE, TCHÈQUE, RÉPUBLIQUE, 20-/05/2019 - 24/05/2019The conference addresses the broad range of topics related to on-road and in-vehicle WIM technology, its research, installation and operation and use of mass data across variable end-uses. Innovative technologies and experiences of WIM system implementation are presented. Application of WIM data to infrastructure, mainly bridges and pavements, is among the main topics. However, the most demanding application is now WIM for enforcement, and the greatest challenge is WIM for direct enforcement. Most of the countries and road authorities should ensure a full compliance of heavy vehicle weights and dimensions with the current regulations. Another challenging objective is to extend the lifetimes of existing road assets, despite of increasing heavy vehicle loads and flow, and without compromising with the structural safety. Fair competition and road charging also require accurately monitoring commercial vehicle weights by WIM. WIM contributes to a global ITS (Intelligent Transport System) providing useful data on heavy good vehicles to implement Performance Based Standards (PBS) and Intelligent Access Programme (IAP, Australia) or Smart Infrastructure Access Programme (SIAP). The conference reports the latest research and developments since the last conference in 2016, from all around the World. More than 150 delegates from 33 countries and all continents are attending ICWIM8, mixing academics, end users, decision makers and WIM vendors. An industrial exhibition is organized jointly with the conference

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges

Truck Loading Simulation for the Fatigue Assessment of Steel Highway Bridges

Fatigue is a common failure mode for steel bridges. About 80-90% of failures in steel structures are related to fatigue and fracture. Despite the deterioration caused by environmental factors, the increasing traffic volume and weight pose a premier threat of steel highway bridges. The total number of truck passages in the 75-year life of a highway bridge could exceed 100 million. With the aging of existing steel highway bridges and the accumulated damage under truck loading, the fatigue assessment for continuing service has become important for decisions making on the structure maintenance, component replacement, and other major retrofits. This research seeks to develop a framework for the fatigue assessment of steel highway bridges based on simulated truck loading. The I-270 Bridge over Middlebrook Road was numerically studied with the proposed methodology. With the help of the available long-term monitoring traffic data and information, truck loading was obtained through the probability-based full velocity difference model. Then, the three-dimensional finite element (FE) global and local bridge models were studied subjected to the simulated truck loading. Meanwhile, the preliminary field test and the long-term monitoring test were also been conducted. The FE models were calibrated by the collected field measurements through monitoring systems, and the simulated numerical structural responses were validated. Lastly, Miner's rule and the rainflow counting algorithm were used in the analysis of simulated numerical structural responses to estimating the fatigue life. Thus, the proposed methodology could be used to realistically simulate the fatigue behavior of steel highway bridges under current or future truck loading, to direct the experimental designs and instrumentation plans before performing experiments on laboratory or on site, and to better understand the fatigue mechanism and prevent the fatigue damage of steel highway bridges

PORTABLE WEIGH-IN-MOTION FOR PAVEMENT DESIGN - PHASES 1 AND 2

Keeping Oklahoma's roadways, highways, and bridges in good condition is necessary to our state’s safety and to avoid expenditures in billions of dollars each year for road repairs and replacement. According to a study done by state of Oregon in 2009, heavy vehicles account for 79% (or $60 million) of annual expenditures required for roadway repaving. Likewise, they were also responsible for 66.8$27 million) of pavement and shoulder reconstruction; 65.1% (or $145 million) of pavement and shoulder rehabilitations; and 61.5$140 million) of pavement maintenance. To weigh traveling trucks, the state of Oklahoma has installed 20 permanent Weigh-in-Motion (WIM) sites. Expanding site coverage to include additional roadways and highways improves data accuracy; however, it requires significant roadside construction and costly infrastructure support. This report presents deployment results of a novel portable WIM system and compares captured data with that collected at a nearby permanent WIM system. Design, development, and road-installation details of the heavy-vehicle centric, portable WIM system are also provided. Outcomes demonstrate that the portable system maintains data quality but for short intervals and provides a viable alternative to permanent systems at merely 10 percent of the cost. The portable WIM system uses off-the-shelf components and commercially available WIM controllers. The WIM controller used was IRD iSINC Lite. The fabricated portable system could be promoted as an alternative WIM monitoring solution to permanent WIM systems and/or static scale stations, both of which are extremely expensive to install on highways. The portable WIM uses RoadTrax BL piezoelectric class-1 sensors, galvanized metal fixtures equipped with pocket tapes to house the sensors, and a trailer with cabinet to house WIM electronics, batteries, and REECE device for real-time monitoring. The system is solar powered with three 100-Watt panels, and it costs roughly $20,000.Final report, October 2011-October 2014N

Advancing Bridge Load Rating: State of Practice and Frameworks

693JJ319D000020 TO693JJ320F000170The U.S. has more than 600,000 bridges, making the distributed load rating and posting processes across the nation a significant effort that does and can benefit from improvements in efficiency. Bridge load rating, posting, and overweight permitting processes evolve due to the regulatory requirements regarding the frequency of inspections and relevant changes to bridges that necessitate re-rating them. These factors include changes to the dead load, strength of members, and any maintenance or rehabilitation work. As such, States are interested in modifying their procedures to implement technology and improved means and methods to reduce the time associated with load rating. Being able to load rate bridges efficiently and accurately is a necessity, particularly in the use case of permit load routing. Based on the extensive findings during the information collection processes for this project, frameworks for future bridge load rating, posting, and overweight permitting were developed to improve productivity, efficiency, and consistency by closing process gaps and through the application of newer technologies. The newer technologies include digital twin concepts; integrating various (new) data; creating, updating, and reusing models; integrating sensing data (bridge, traffic, weigh-in-motion); and better analysis methods. This work may help develop the state of practice

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

In the past, when elements in sructures were composed of perishable materials, such as wood, the maintenance of houses, bridges, etc., was considered of vital importance for their safe use and to preserve their efficiency. With the advent of materials such as reinforced concrete and steel, given their relatively long useful life, periodic and constant maintenance has often been considered a secondary concern. When it was realized that even for structures fabricated with these materials that the useful life has an end and that it was being approached, planning maintenance became an important and non-negligible aspect. Thus, the concept of structural health monitoring (SHM) was introduced, designed, and implemented as a multidisciplinary method. Computational mechanics, static and dynamic analysis of structures, electronics, sensors, and, recently, the Internet of Things (IoT) and artificial intelligence (AI) are required, but it is also important to consider new materials, especially those with intrinsic self-diagnosis characteristics, and to use measurement and survey methods typical of modern geomatics, such as satellite surveys and highly sophisticated laser tools