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

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

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

Best Practices for Commercial Vehicle Monitoring Facilities Design

The great technological advances that were made over the last decade in monitoring traffic and the increased emphasis on highway safety for truck traffic have prompted a need to determine more effective ways to monitor and inspect truck traffic. Commercial Vehicle Monitoring (CVM) facilities provide the highway community with the means of supervising truck traffic. However, in an era with limited funds and space for roadway expansion. some consideration must be given to the types of facilities needed and the most efficient way to spend the available funds. Hence, a research study was initiated to determine the successful practices for designing a new CVM facility or retrofitting or upgrading an existing facility; the findings are presented here. A questionnaire was distributed to all 50 states to identify the state of the nation with respect to the newly constructed and lately upgraded CVM facilities. This report focuses on the presentation of issues that need to be considered and addressed when designing or upgrading a CVM facility and provides a checklist of critical factors, considerations for facility components, and typical facility layouts

Method for setting weight tolerance limits in high-speed weigh-in-motion systems : a case study in Brazil

High-speed weighing systems are considered a potential tool to restrain overloaded vehicles traffic, since this practice promotes threats to the infrastructure’s durability and to citizens’ safety. In view of the discussion on the certification of such systems, a method to define the weight tolerance limits of heavy goods vehicles was proposed in this work, using statistical experimentation methods. The method was applied to a case study in Brazil, which considered a high-speed weighing system and three known vehicles (3C, 2S3, and 3S3). The vehicles were loaded with gravel and the bending plate scale was used to set the weight. The test was conducted by checking the weight of the vehicles in the HS-WIM system and composed by the observance of three mandatory speeds (60, 70, and 80 km/h), in three different lateral positions in relation to the lane (center, left, and right), and at various times during the collection days. As result, maximum weights accuracy limits were obtained for gross vehicle weight (GVW) and for axes groups (G1, G2, and G3) as 13% (GVW), 20% (G1), 20% (G2), and 17% (G3)

Kentucky\u27s PRISM-Based Automated Ramp Screening System Evaluation

In 2010, Kentucky implemented a Performance Registration Information Systems and Management (PRISM) based automated ramp screening system (PARSS) at the Boone County inspection station on southbound I-71. The purpose of the PARSS is to identify and screen every vehicle that enters the Boone County inspection station. The system provides automated screening of trucks based on the license plate number and the USDOT number displayed on the vehicle. If it is determined that the vehicle should be stopped for inspection, that decision is communicated to the truck driver via the existing directional arrows that direct drivers to the static scale for inspection. A thorough evaluation was conducted to assess the performance of the system (i.e., does it do what it was intended to do?), the value of the system in identifying vehicles for inspection with PRISM or CVISN-related issues, and the potential for more widespread deployment of this type of screening system. In addition, the evaluation also included a side-by-side comparison of the two automated license plate reader (ALPR) systems

The Potential for Performance-Based Standards as the Basis for Truck Size and Weight Regulation in the United States

This research project examines truck size (dimensions) and weight regulation in other countries, worldwide, to identify size and weight regulations that are based on standards of truck performance. Such standards, known as performance-based regulations, are intended to ensure that the allowable size and weight of trucks is governed by safety standards and/or by standards for infrastructure (pavement and bridges) wear. The primary goals of this study were to identify performance-based size and weight regulatory practices from a widely diverse group of 32 jurisdictions worldwide, to determine the applicability of such standards in the United States (U.S.) size and weight regulatory framework, and to identify the barriers to adopting this type of approach to truck size and weight regulation. Even though adopting some performance-based standards may be difficult, the concepts underlying them offer too many advantages not to move forward and use a performance-based framework as a foundation for future size and weight reform. The experiences in other countries such as New Zealand and the European Union indicate that performance-based size and weight regulations can be successfully developed and enforced. However, due to the size of the U.S. road network and the population of the U.S. heavy vehicle fleet not all standards used in other countries may be successfully implemented in the U.S. size and weight regulatory framework

Analysis of Truck Weight Limit Regulations

In the United States vehicle weight limits are set by laws and regulations enacted at the state and federal levels. On interstates the maximum allowable gross vehicle weight is 80,000 lbs. States use different rules for permitting overdimensional and overweight (OD/OW) vehicles, and most have carve outs that exempt specific commodities from standard weight limits. This results in a complex legal and regulatory landscape that enforcement personnel can find difficult to negotiate. This report discusses strategies that can be adopted in the state of Kentucky to improve enforcement and mitigate infrastructure damage caused by OD/OW loads. After presenting a thorough review of laws pertaining to vehicle weight limits at the national and state levels, the report presents the results of a nationwide survey administered to agency staff directly involved in weight limit enforcement. Survey respondents reported that OW trucks inflict a disproportionate amount of damage on pavements and bridges that permitting fees and fuel taxes are insufficient to ameliorate roadway damage caused by these vehicles, and that commodity exemptions and staff shortages make enforcement a challenging proposition. In addition to sharing many of the opinions of agency staff elsewhere, Kentucky personnel said that many bridges and roadways are not designed to withstand repeated loads of 80,000 lbs. of gross vehicle weight, heavier vehicles with commodity exemptions are especially damaging to collector and local roads, and that enforcement efforts need to be redoubled. Recommendations for improving weight limit enforcement in Kentucky cover areas such as legislation (e.g., reducing the number of commodity exemptions, using axle-based weight limits), highway design, enforcement and judicial practices, and permitting and fees. Implementing these recommendations can help Kentucky modernize and standardize its enforcement efforts