Weigh-in-motion Sensors Research Articles

Enhancing Reliability Analysis with Multisource Data: Mitigating Adverse Selection Problems in Bridge Monitoring and Management

Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one of the most indeterminate variables for monitoring bridges. Such asymmetric information can lead to an adverse selection problem in making maintenance, rehabilitation, and repair decisions. This study proposes a data-driven reliability analysis to assess probabilities of bridge failure by synthesizing NBI data and Weigh-In-Motion (WIM) data for a large number of bridges in Georgia. On the resistance side, tree ensemble methods are employed to support the hypothesis that the NBI operating load rating represents the distribution of bridge resistance capacities which change over time. On the loading side, the live load distribution is derived from field data collected using WIM sensors. Our results show that the proposed WIM data-enabled reliability analysis substantially enhances information symmetry and provides a reliability index that supports monitoring of bridge conditions, depending on live loads and load-carrying capacities.

Self-sensing asphalt composite with carbon microfibers for smart weigh-in-motion

The need for ageing infrastructure monitoring has recently emerged as an urgent priority in many countries. Smart road infrastructure is a technology that could be used to address this issue by enabling on-time decisions such as condition-based maintenance. For example, automatic traffic monitoring could be beneficial by improving fatigue analysis, and maintenance priority planning and management of overloaded vehicles. This can be done through image-based traffic monitoring, weight measurements by static scales, and weigh-in-motion (WIM) stations. WIM can be used to identify and classify the type, the number, and the weight of vehicles passing over a given road segment without interrupting the traffic flow. The general drawbacks of existing WIM technologies are their high costs, low durability, and complex deployment. This paper proposes a new asphalt-like composite enabling self-sensing road pavements that can serve as a low-cost and durable WIM sensor. The proposed novel material consists of a commercial binder called EVIzero, doped with natural aggregates and carbon microfibers. These microfibers provide electrical conductivity and piezoresistive properties through electrical percolation. Here, both the material preparation for road applications and its electromechanical characterization are examined. Various cylindrical samples fabricated using different percentages of carbon microfibers were produced and investigated in order to evaluate their signal quality and strain sensing capabilities. It is found that the material mix fabricated with 1% carbon microfibers with respect to the binder weight has the best sensing performance due to electrical percolation. A mid-size slab sample is produced using this optimal mix in order to achieve a preliminary demonstration of material’s feasibility for WIM sensing. The results show that a linear relationship between the electrical response of the slab sample and the induced strain is established with an R^2 of 93%.

Investigation of the Temperature Compensation of Piezoelectric Weigh-In-Motion Sensors Using a Machine Learning Approach.

Piezoelectric ceramics have good electromechanical coupling characteristics and a high sensitivity to load. One typical engineering application of piezoelectric ceramic is its use as a signal source for Weigh-In-Motion (WIM) systems in road traffic monitoring. However, piezoelectric ceramics are also sensitive to temperature, which affects their measurement accuracy. In this study, a new piezoelectric ceramic WIM sensor was developed. The output signals of sensors under different loads and temperatures were obtained. The results were corrected using polynomial regression and a Genetic Algorithm Back Propagation (GA-BP) neural network algorithm, respectively. The results show that the GA-BP neural network algorithm had a better effect on sensor temperature compensation. Before and after GA-BP compensation, the maximum relative error decreased from about 30% to less than 4%. The sensitivity coefficient of the sensor reduced from 1.0192 × 10−2/°C to 1.896 × 10−4/°C. The results show that the GA-BP algorithm greatly reduced the influence of temperature on the piezoelectric ceramic sensor and improved its temperature stability and accuracy, which helped improve the efficiency of clean-energy harvesting and conversion.

Weigh-in-Motion Sensors and Traffic Monitoring Systems. State of the Art and Perspectives

Abstract Weigh-in-motion (WIM) sensors allow the control of vehicle weights without disruption of traffic. By monitoring traffic and by reducing the number of overweight vehicles, the WIM sensors bring very important savings. This paper discusses the present status and developmental trends of weigh-in-motion (WIM) technologies. Both commercial and new types of WIM sensors are presented. Strengths and weaknesses of different type of WIM sensors are discussed. It is also presented the tendency to equip the WIM systems with different types of sensors, in order to evaluate other effects: reducing the fuel consumption, emission of pollutants, noise and vibrations, etc. Possible trends for the further development of WIM sensors are anticipated.

New concept of WIM system for urban traffic monitoring

Weigh-in-motion (WIM) sensors allow to estimate the static force applied on each wheel, respectively on each axle of the vehicle, as well as the total weight of the vehicle, based on the measurements made in dynamic mode, usually without disruption to traffic. WIM stations are complex systems which can use a lot of sensors for: measuring speed – inductive loops and radars; detecting the presence of axles - electromagnetic sensors; worn bearings – using IR cameras; vehicles identifying – video cameras and so on. This paper discusses about a new concept of WIM sensor, designed in COMPETE project from “Gheorghe Asachi” Technical University of Iasi, developed for urban traffic monitoring, which can be integrated into Smart City structure and synchronise it with intelligent transport systems. This new concept can be useful for the local municipality, for traffic monitoring agencies and for avoid traffic gems.

Weigh-in-motion sensors and traffic monitoring systems - Sate of the art and development trends

Weigh-in-motion (WIM) sensors allow the control of vehicle weights without disruption of traffic. WIM systems bring very important savings by traffic monitoring and by reducing the number of overloaded vehicles. This paper discusses the present status and developmental trends of Haigh-speed weigh-in-motion (HS-WIM) technologies. Both commercial and new types of WIM sensors are presented. Strengths and weaknesses of different type of WIM sensors are discussed, as well as the economic aspects of the different technologies. Possible future developments of WIM technologies concerning the measurement performance are also described.

Assessment of Factors Affecting Measurement Accuracy for High-Quality Weigh-in-Motion Sites in the Long-Term Pavement Performance Database

Weigh-in-motion (WIM) is a primary technology used for monitoring and collecting vehicle weights and axle loads on roadways. Highway agencies collect WIM data for many reasons, including highway planning, pavement and bridge design, freight movement studies, motor vehicle enforcement, and regulatory studies. Therefore, the data collected at WIM systems must be accurate and represent actual field loadings. Several factors or field conditions can affect the WIM system accuracy (i.e., measurement error). The potential site-related factors include road geometry, pavement stiffness, pavement surface distresses, road roughness, and climate. The WIM calibration and equipment-related factors may include sensor type and array, calibration speed and speed points, and sensors’ age. The WIM data for Long-Term Pavement Performance (LTPP) research-quality sites were considered to estimate benchmark accuracies for different sensors and evaluate the effects of different factors on WIM measurement errors. These are the 35 sites with WIM calibration data that meet the ASTM E1318-09 error tolerances for Type I WIM systems and are consistently calibrated using the LTPP protocol with a complete set of supporting data about WIM site performance and WIM site conditions. The data for the LTPP research-quality sites showed that for the sensor arrays utilized, the best achievable total errors based on GVW are ±5% for load cell (LC), ±9% for bending plate (BP), and ±9.8% for the quartz piezo (QP) sensors. These accuracy levels for different sensor types provide highway agencies with the benchmark values demonstrating the practically achievable accuracy of WIM measurements after calibration for different WIM sensor types. Based on available data, WIM sensor accuracy can be significantly affected by climate, especially for QP and polymer piezo sensors. Also, the longitudinal roadway slope at a WIM site, sensor array, and speed points may significantly affect the WIM system accuracy.

Optimal System Design for Weigh-In-Motion Measurements Using In-Pavement Strain Sensors

Weigh-in-motion (WIM) systems are widely deployed to collect real-time traffic data. They enable multiple applications, such as truck weight enforcement, pavement design, freight management, traffic management, and intelligent transportation systems, such as smart tolling and traffic data supports to smart cities. Engineers currently depend on the experiences of practitioners in the field to select parameters for the sensor and system design. This paper introduces a comprehensive design framework that optimizes the system design of in-pavement WIM sensors to meet high-level application requirements. The framework establishes the relationships between the sampling rate, measurement resolution, signal-to-noise ratio, sensor sensitivity, and the measurement dynamic range. The simulations and case study presented demonstrates an example of a specific design to accommodate high-speed truck weight measurements.

Statistical analysis and probabilistic modeling of WIM monitoring data of an instrumented arch bridge

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike

Weigh-in-motion for Direct Enforcement of Overloaded Commercial Vehicles

Heavy commercial vehicle overloads contribute to premature deterioration of infrastructure and increase road unsafety and unfair competition between transport modes and operators. Public authorities and road operators must therefore implement an efficient checking system to enforce weights and dimensions at an affordable cost. A large scale project was launched in 2014 by the French Ministry of Ecology, Sustainable Development and Sea, in charge of Transport, carried out by IFSTTAR and CEREMA, to demonstrate the feasibility of using high speed weigh-in-motion (WIM) systems for direct enforcement of overloaded HCVs. This ambitious challenge requires overcoming technological and metrological gaps, and modifying the current legislation. The required tolerances are ±5% for the gross vehicle weight, and ±10% for axle loads. None of the existing WIM system matches this accuracy for 100% of the vehicles. Therefore the objective is to set up some sorting criteria and algorithms, eliminating the weighings outside these tolerances.The project is divided into 5 WPs, to characterize WIM sensor response under controlled environment and loading conditions and to develop fiber optic WIM sensors, to assess the capability of multiple sensor (MS-) and bridge (B-) WIM systems to meet the requirements, to carry long term road tests and to develop type approval procedures for direct enforcement. This paper presents results gathered on the accelerated pavement testing facility of IFSTTAR in 2014 with 10 WIM sensors. Some results of B-WIM systems are also reported. Perspectives are given how to achieve a WIM system type approval procedure for direct enforcement.

Recommended Procedure to Adjust Inaccurate Weigh-in-Motion Data

Weigh-in-motion (WIM) stations are used to monitor traffic volumes and loadings on roads and bridges. When a station is installed, a vehicle of known axle weights is typically used to calibrate the sensors. Because of environmental changes, pavement changes, sensor fatigue, and repeated loadings, the initial calibration doesn’t always maintain its validity. Therefore, the WIM station output must be monitored so that potential problems can be identified and corrected. It is common for analysts to discover that WIM data are inaccurate when they are attempting to use the data for design or planning. An analyst must then decide whether to conduct the analysis with inaccurate data or to not conduct the analysis at all. There is a need to develop a procedure that allows inaccurate WIM data to be adjusted and remain usable. Adjustment of WIM data after they have been collected may not be an acceptable practice to all agencies or for certain applications. A new accuracy metric was developed that relates Class 9 front axle weight with gross vehicle weight with data from the Long-Term Pavement Performance (LTPP) program. This new metric and other existing accuracy metrics in the literature will serve as baselines for adjusting the data. The adjustment procedure involves time series analysis, which eliminates temperature-induced variations in weight measurements that are known to occur with certain WIM sensors. The adjustment procedure is applied to an LTPP WIM station to illustrate the data before and after adjustment.

Weigh-in-Motion Equipment – Experiences and Challenges

The Norwegian Public Roads Administration focus on issues to improve weight controls, and the quality of statistic weigh data. An important part of the project is field tests of different methods and equipment for collecting Weigh-In-Motion (WIM) data, with high speed WIM equipment, and V2I communication where the vehicle reports its own weight. The tests are focusing on data quality (WIM vs static weight), user interface and usability, procedure and need for calibration and lifetime of sensors. Another important part of the project is the use of WIM data to pre-select vehicles at the control station. WIM data are combined with license plate numbers from ANPR cameras, and the measured weight is compared with the permitted weight. If a vehicle is overloaded then a warning is shown on the control station. This allows legal drivers to pass without being stopped.We have conducted several tests over a three years period. Two different WIM sensors technologies from four different manufacturers has been tested. The focus of the tests have been on accuracy, lifetime of sensors, calibration and usability of software and equipment. We have tested equipment for high speed WIM for pre selection of vehicles at control stations.Our results shows that lineas quarts sensors are better than piezo electric cables. The calibration procedures are time end cost consuming. Lifetime of the sensors are very short. This can be improved by deeper installation where it is possible. It is a problem that the accuracy of the sensors decrease over time. For one system the error in average gross weight increased to about 15% over a period of 6 months. The error in the other system had increased to 15% in three months, and after calibration the error increased to 15% in just over one month.A full scale test of a V2I system was conducted in Oslo in February 2015, as a proof of concept. One instrumented vehicle was used to verify the communication protocols. The information flow was steady and the focus was therefore on the reported weight. The internal difference in the self-declared truck weight for each axel was very low – with less than 1% difference between the test runs. Compared with the static weight the self-declared truck weight was approximately 5% too low. This was considered as a good start for the concept.A full scale tests with four vehicles are planned to start in September. The trucks will be equipped with communication units for sharing the current weight for a two month period. This setup will provide us with a wider basis for evaluating the data quality provided by the system. We believe that in the future WIM systems will be used together with V2I solutions, and that this will improve vehicle weight control further.The project is a cooperation between the Norwegian Public Roads Administration and the Traffic Engineering Research Centre.

Investigation of the Effect of Weight Factor on Performance of Piezoelectric Weigh-in-Motion Sensors

Trucking industry growth increases loads on existing roads and may raise the possibility of overloaded vehicles, which consequently cause significant damage to pavement. Weigh-in-motion (WIM) technology improves weight enforcement activities by increasing the capacity of static weigh stations and provides traffic monitoring and traffic data for pavement research and design. However, there is still a lack of knowledge about the behavior of WIM sensors installed in different environments, which affects reliable and precise data gathering. Therefore, different types of piezoelectric WIM systems, including quartz, polymer, and ceramic sensors, were installed at the Center for Pavement and Transportation Technology (CPATT) test sites of the University of Waterloo in Canada. The objective of this research is to explore the behavior of piezoelectric WIM sensors under different load conditions specifically for the polarized sensors, which illustrated heteroscedasticity in weight estimations caused by inconsistent variance of estimations. The issue was solved by transformation of variables, which resulted in constructing a compensation curve. The investigated effect of the weight factor will assist in the recommendation of improved benefits of the least expensive WIM technology.

Weigh-in-Motion to Estimate Vehicle Weight of Rigid Pavement Strain Signal

The aim of this research is to investigate the feasibility of developing a traffic load detector system for the purpose of accurate weighing vehicle to aid traffic management systems. The possibility of estimate the velocity, axle and gross weight of a moving vehicle from the surface of pavement tensile strain is investigated. The objective is to develop weigh-in-motion (WIM) technique to estimate the vehicle weight from the pavement responses with fewer disturbances to the vehicle’s vibration and traveling speed, and to provide more reliable and durable WIM systems and sensors. The repeatability, sources of error, and accuracy of weight prediction were discussed.

Use of Finite Elements Analysis for a Weigh-in-Motion Sensor Design

High speed weigh-in-motion (WIM) sensors are utilized as components of complex traffic monitoring and measurement systems. They should be able to determine the weights on wheels, axles and vehicle gross weights, and to help the classification of vehicles (depending on the number of axles). WIM sensors must meet the following main requirements: good accuracy, high endurance, low price and easy installation in the road structure. It is not advisable to use cheap materials in constructing these devices for lower prices, since the sensors are normally working in harsh environmental conditions such as temperatures between −40 °C and +70 °C, dust, temporary water immersion, shocks and vibrations. Consequently, less expensive manufacturing technologies are recommended. Because the installation cost in the road structure is high and proportional to the WIM sensor cross section (especially with its thickness), the device needs to be made as flat as possible. The WIM sensor model presented and analyzed in this paper uses a spring element equipped with strain gages. Using Finite Element Analysis (FEA), the authors have attempted to obtain a more sensitive, reliable, lower profile and overall cheaper elastic element for a new WIM sensor.

Detecting Differential Drift in Weigh-in-Motion Wheel Track Sensors

Weigh-in-motion (WIM) sensors are being used by state agencies for commercial vehicle weight enforcement applications that require a high level of accuracy for determining individual vehicle weights. Existing methods of evaluating WIM weight data accuracy have not been effective for detecting small but consistent calibration drifts. This paper proposes the use of a left–right differential metric that is computed as a percentage difference between the left and right wheel weights on the steer axle of Class 9 trucks for WIM sensors that measure and report individual left and right wheel weights. In this paper, the left–right differential is monitored over time by using statistical process control charts to identify when sensors are exhibiting statistically significant variations. The monitoring procedure can be integrated into existing quality control programs. Data from WIM sites in Indiana and California were used to evaluate the effectiveness of this metric.

A Novel Sensor System for Measuring Wheel Loads of Vehicles on Highways

With the development of the highway transportation and business trade, vehicle Weigh-In-Motion (WIM) technology has become a key technology for measuring traffic loads. In this paper a novel WIM system based on monitoring of pavement strain responses in rigid pavement was investigated. In this WIM system multiple low cost, light weight, small volume and high accuracy embedded concrete strain sensors were used as WIM sensors to measure rigid pavement strain responses. In order to verify the feasibility of the method, a system prototype based on multiple sensors was designed and deployed on a relatively busy freeway. Field calibration and tests were performed with known two-axle truck wheel loads and the measurement errors were calculated based on the static weights measured with a static weighbridge. This enables the weights of other vehicles to be calculated from the calibration constant. Calibration and test results for individual sensors or three-sensor fusions are both provided. Repeatability, sources of error, and weight accuracy are discussed. Successful results showed that the proposed method was feasible and proven to have a high accuracy. Furthermore, a sample mean approach using multiple fused individual sensors could provide better performance compared to individual sensors.

Weigh-In-Motion studies using strip-type sensors: the preliminary results

The various conditions of dynamic vehicle tire forces on pavement loading have been investigated by using strip type commercially available piezo-electric Weigh-In-Motion (WIM) sensors. Usually, the WIM load is found by empirically relating the peak voltage of the signal to the static wheel weight, and the area under the signal and the vehicle speed are not considered. A formula considering the area under the WIM signal and the moving load is presented. Whether or not static calibration factor of strip type sensors is sufficient to calculate the vehicle dynamic load is determined and a quasi-static vehicle pass over calibration methodology for future experiments is suggested. This paper also reviews the detailed description of the research carried out on existing WIM sensors and related systems.

Evaluation of Quartz Piezoelectric Weigh-in-Motion Sensors

Quartz piezoelectric weigh-in-motion (WIM) sensors are potentially appealing because of their low sensitivity to temperature fluctuations, according to information gathered from previous installations in other states. These installations also called into question the durability of the quartz sensors. Some sensors stopped producing a signal even though the sensor installation did not exhibit physical distress. After design changes were made, it was necessary to reevaluate the quartz sensor before any major investment in this technology in Texas. This research was undertaken to evaluate the performance and durability of quartz piezoelectric WIM sensors. To perform this evaluation, researchers instrumented two sites with quartz sensors and collected truck weight and sensor condition data to analyze the accuracy and durability of these sensors. The results of this research show that the sensors meet or exceed the weight accuracy specified by the ASTM specification for Type 1 highway WIM systems. The data further show that the truck weights produced by the WIM system are stable over time with minimal variation due to temperature change. There have been no sensor failures or degradation of the installations to date.

Evaluation of Quartz Piezoelectric Weigh-in-Motion Sensors

Quartz piezoelectric weigh-in-motion (WIM) sensors are potentially appealing because of their low sensitivity to temperature fluctuations, according to information gathered from previous installations in other states. These installations also called into question the durability of the quartz sensors. Some sensors stopped producing a signal even though the sensor installation did not exhibit physical distress. After design changes were made, it was necessary to reevaluate the quartz sensor before any major investment in this technology in Texas. This research was undertaken to evaluate the performance and durability of quartz piezoelectric WIM sensors. To perform this evaluation, researchers instrumented two sites with quartz sensors and collected truck weight and sensor condition data to analyze the accuracy and durability of these sensors. The results of this research show that the sensors meet or exceed the weight accuracy specified by the ASTM specification for Type 1 highway WIM systems. The data further show that the truck weights produced by the WIM system are stable over time with minimal variation due to temperature change. There have been no sensor failures or degradation of the installations to date.