Weigh-in-Motion Auto-Calibration Using Automatic Vehicle Identification

Weigh-in-Motion (WIM) sensors are installed on mainline lanes at highway locations to record vehicle weights, axle spacing, vehicle class, travel speed, vehicle length, and traffic volume. These data elements support effective transportation planning, infrastructure design, and policy development. Therefore, it is important that WIM sensors supply accurate data. After initial installation and calibration, WIM systems may experience measurement drifts in weight and axle detection. Recalibration takes two general forms: (a) On-site calibration involving running trucks of known weight over WIM scales and (b) Auto-calibration methods involving comparisons to assumed reference weights. Auto-calibration can be more cost and time effective than on-site calibration. This paper leverages the increasing prevalence of truck tracking technologies like Global Positioning Systems (GPS) to improve auto-calibration methods and was divided into three aims: (i) data collection, (ii) data processing and (iii) model development. Truck GPS data from a national provider, WIM recorded truck weights, and static weights collected at weight enforcement station were gathered at several highway locations in Arkansas. A “matching” algorithm was developed to automatically match each GPS record to a WIM record based on timestamp and vehicle configuration. Algorithm performance was assessed via manual video verification of matches. Approximately, 75% of WIM and truck GPS records were correctly paired. Lastly, an auto-calibration model was developed to estimate lane and site specific calibration factors. The algorithm estimates hourly calibration factors by comparing the front axle weight of the same truck as it passes multiple WIM sites. Algorithm performance was measured by comparing estimated front axle and gross vehicle weights to known weights of the same truck measured at a static enforcement scale. The algorithm achieved Median Absolute Percent Error (MdAPE) of 11-23% for front axle weight and 15-45% for gross vehicle weight. These results can be improved by increasing the number of trucks that are able to be tracked across WIM sites with Automatic Vehicle Identification

Simulation of Traffic Loading on Highway Bridges

This work is based on weigh-in-motion measurements for approximately three million trucks obtained from sites in five European countries. Techniques have been developed, supported by photographic evidence, for filtering the measurements to identify and remove unreliable values, and for the classification of extremely heavy vehicles. The collected measurements have been used as the basis for building and calibrating a Monte Carlo simulation model for bridge loading. Two-lane traffic is simulated – either two lanes in the same direction or one lane in each direction. The model allows for vehicles that are both heavier and have more axles than in the measured data. Careful program design and optimisation have made it practical to simulate thousands of years of traffic. This has a number of advantages – the variability associated with extrapolation is greatly reduced, rare events are modelled, and the simulation output identifies the typical loading scenarios which produce the lifetime maximum loading. Analysis of the measured data shows subtle patterns of correlation in vehicle weights and gaps, both within lanes and between adjacent lanes in same-direction traffic. A new approach has been developed for simulating traffic in two same-direction lanes using flow-dependent traffic scenarios. The measured weights and gaps in the scenarios are modified using variable-bandwidth kernel density estimators. This method is relatively simple to apply and can be extended to more than two lanes. It is shown that the correlation structure in the traffic has a small but significant effect on characteristic maximum loading

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

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

Evaluation of Pavement Performance Due to Overload Single Trip Permit Truck Traffic in Wisconsin

This study researched the impacts of overweight permit vehicle traffic on flexible pavement performance in Wisconsin using field investigations and AASHTOWare MEPDG analyses. A database of Oversize/Overweight (OSOW) single trip permit truck records was analyzed and provided a network of Wisconsin corridors heavily trafficked by OSOW trucks. Four Wisconsin state trunk highways were selected for investigation due to a high level of OSOW truck traffic. The research included traffic counts to confirm the levels of truck traffic on these segments and to verify the high numbers of permits issued for OSOW trucks. Furthermore, the field work included the identification and quantification of pavement surface distresses by executing visual distress surveys allowing for the current pavement surface conditions to be rated using the pavement condition index. Comprehensive analyses were conducted to evaluate pavement performance due to normal traffic loads as well as normal traffic loads plus the OSOW truck traffic loads. The use of AASHTOWare MEPDG analyses presented a potential methodology for determining the proportion of pavement deterioration attributable to OSOW truck traffic. OSOW axle load distributions were integrated with baseline truck traffic levels to develop axle load spectra and other traffic input parameters for the MEPDG analysis. Visual distress surveys conducted at the selected segments of state trunk highways (STH) 140, 11, and 26 rated the pavement surface conditions as serious to poor, ranging from a PCI value of 13 on STH 140 to a PCI value of 52 on STH 11. Across these three segments, the maximum measured rutting depth along the outer wheel paths ranged from 0.82 in to 1.25 in, which exceeded WisDOT\u27s threshold for acceptable rutting of 0.50 in. Only the segment of STH 23 exhibited a fair pavement surface condition due to PCI values of 63 and 66 in the two lanes, with a maximum rutting depth of 0.50 in. The generally poor pavement conditions across the sampled segments included significant pavement surface damage and distresses such as rutting, longitudinal and transverse cracking, significant fatigue cracking, and potholes. The predicted total pavement deterioration levels from the AASHTOWare MEPDG software were generally consistent with the levels of deterioration observed during the site investigations. However, the proportion of pavement damage and deterioration attributable to OSOW truck traffic was predicted to be fairly insignificant, with most distress indices showing relative increases of approximately 0.5% to 4%, with a few outliers. The addition of OSOW truck traffic to the baseline truck traffic volumes resulted in a small increase in the amount of pavement damage, rutting depths, and loss of ride quality compared with the predicted deterioration levels due to only the baseline traffic

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

Bridge Rating Based on In-Situ Weigh-In-Motion and Health Monitoring Data

Parallel to the bridge design methodology changes from the Allowable Stress Design to the Load Factor Design, and then to the reliability based Load and Resistance Factor Design (LRFD), bridge load rating method has also been evolving. Applying the reliability theory to the bridge load rating is more complex than applying to the LRFD since any conservatism can have a significant effect on the assessment of bridge capacity, particularly in load posting and bridge replacement. Although the current Load and Resistant Factor Rating (LRFR) method applying the concept of reliability analyses, it uses very limited site-specific data due to practical constraints and the limited availability of site-specific data. The objective of this study is to develop a reliability based rating approach, grounded in in-situ responses from long-term structural health monitoring systems and actual unbiased traffic data from weigh-in-motion stations. Rating bridges that use actual bridge in-service measurements and site-specific traffic can remove conservatism and uncertainties in association with load distribution factors, dynamic impact, and secondary and non-structural element effects. The end goal is to achieve a continuous bridge evaluation model for real-time vehicle loads, which in turn can be used for speedy truck permitting, bridge management, and identifying sudden condition changes to ensure public safety. The bridge site-specific truck data and bridge peak strains under ambient traffic for the instrumented bridge have been continuously collected for over a year. The time dependent values of the maximum live load effects are obtained from the statistical analysis of the in-service responses and traffic data. The site-specific live load distribution factors are developed and live load factors are re-calibrated based on reliability analysis. Statistical distribution and projection methods have been compared and validated. This study suggests that the Gumbel distribution and the Parent Tail projection method will be the most suitable methods for the live load distribution and maximum live load effect projection. The reliability-based in-service traffic rating result is compared to three other rating methods: the simplified distribution method, the finite element method, and the live load testing method. The load rating results based on the updated load and load distribution have improved tremendously compared with other rating methods. This systematic rating approach can provide essential information for future bridge maintenance and replacement prioritization. Additionally, a more accurate posting sign is recommended for future bridge load limits

Issued as the Final Report on the Life Expectancy of Highway Bridges -- Stress History Studies; Project IHR-301, Illinois Cooperative Highway Research Program

Increased volumes of truck traffic, pressure for increased axle and gross weight limits, and the use of new materials and structural details make the study of live-load stresses induced in bridges subjected to heavy truck traffic of increasing importance. The present study is directed to the collection, analysis, and interpretation of data on stresses at critical locations in bridges. It is focused on the development of a probabilistic technique for forecasting the stress-range environment, and ultimately the mean fatigue life of the bridge. The study makes use of a comprehensive, computer based, data acquisition, analysis and interpretation system.State of Illinois Department of TransportationU.S. Department of Transportation. Federal Highway Administratio

Auto-Calibration of WIM Using Traffic Stream Characteristics

This project evaluates the performance of Weigh-in-Motion (WIM) auto-calibration methods used by the Arkansas Department of Transportation (ARDOT). Typical auto-calibration algorithms compare the WIM measured weights of vehicles from the traffic stream to reference values, using five-axle tractor-trailer configured trucks for comparisons, e.g. Federal Highway Administration (FHWA) Class 9. Parameters of the existing algorithms including the Front Axle Weight (FAW) reference value, the sampling frequency required to update the calibration factor, and thresholds for Gross Vehicle Weight (GVW) bins were evaluated. The primary metric used to estimate algorithm performance was Mean Absolute Percent Error (MAPE) between the WIM and static scale GVW measurements. Two altered auto-calibration algorithms based on methodologies utilized by ARDOT and the Minnesota DOT (MNDOT) were developed. Parameters for the algorithms are based on combinations that produced minimal MAPE at the study sites. WIM data from two sites (Lamar and Lonoke) and static scale data from one site (Alma) were collected along Eastbound I-40 in Arkansas during March 2018. The updated MNDOT auto-calibration algorithm reduced the MAPE by 2.5% compared to the baseline method at the Lamar site (n = 77 trucks) and by 1.1% for the Lonoke site (n = 44 trucks). The updated ARDOT algorithm reduced MAPE by 1.6% at the Lamar site and 0.6% at the Lonoke. Due to limitations of the field data collection methodology, the thresholds defining FAW reference values and the FAW reference values themselves were not able to be tested for spatial transferability, e.g. the samples of trucks at the Lonoke WIM site were a subsample of the trucks at the Lamar WIM site. Improvements in auto-calibration accuracy at low volume sites but was not tested due to the small number of confirmed vehicle matches at a third WIM site (Bald Knob, n = 2 trucks). Overall, site specific tuning of auto-calibration algorithms will improve the accuracy of WIM data which is used for pavement design, maintenance programming, and traffic forecasting. For example, improvements of 2.5% MAPE of WIM measured GVW results in a 39% difference in the estimated Equivalent Single Axle Load (ESAL) factors used for pavement design