Strain Gauge Calibration for High Speed Weight-in-Motion Station.

The development of systems for weighing vehicles in motion aims to introduce systems allowing automatic enforcement of regulations. HSWIM (high speed weight-in-motion) systems enable measurement of a mass of vehicles passing through a measurement station without disturbing the traffic flow. This article focuses on the calibration of a weighing station for moving vehicles, where strain gauge sensors are used to measure pressures. A solution was proposed to replace the calibration coefficients with calibration functions. The analysis was performed for two methods of determining wheel loads: based on the maximum of the signal from strain gauge sensors and on a method using the field under the signal and the vehicle's speed. Calibration functions were determined jointly for all test vehicles and separately for each of them. The use of a calibration function for a specific vehicle type made it possible to determine wheel pressure and gross weight with a level of accuracy that allowed the weigh-in-motion station to be classified as a direct enforcement system. The achieved improvement in the accuracy of weighing in motion did not require any interference with the measurement station. The proposed change in the method of calibration and, ultimately, determination of wheel loads required only a change in the algorithm for determining wheel loads.

Prediction of the severity of exceeding design traffic loads on highway bridges.

Being a driver of failure consequences, forecasting the severity of events where design traffic load limits on bridges have been exceeded (DLEEs) is fundamental for road safety. Previous research has focused on estimating failure consequences by direct and indirect cost metrics. Only recently has research assessed severity unconventionally, in which the type of DLEEs was predicted by applying econometric models through Binomial Logistic Regression (BLR). Since machine learning models using Artificial Neural Networks (ANN) have not yet been explored, this study will enhance the literature as follows. First, two different 'severity' models were set up as a function of bridge-side, temporal-context, and traffic load hazard variables. Whilst the former relied on a BLR, the latter used an ANN. Second, the performance of these models was assessed using confusion matrixes, some performance indicators, and a cross-entropy parameter. Raw Weigh-In-Motion data on 7.4 M+ individual vehicle transits on a bridge along a primary roadway in Brescia (Italy) were processed. Although a similarly strong performance was achieved for BLR and ANN, the results indicated that ANN was able to predict severity records with a higher level of confidence than BLR on the case study dataset, with the cross-entropy of the ANN less than one third of that of the BLR. These analyses can support road authority traffic management to safeguard bridges from traffic load hazards. Finally, this study recommends future developments, such as considering the structural effects of traffic loads in the modelling, prioritizing traffic management actions among bridges at network level, and exploring the impact of ANN models in risk assessment.

Assessing Vehicle Wandering Effects on the Accuracy of Weigh-in-Motion Measurement Based on In-Pavement Fiber Bragg Sensors through a Hybrid Sensor-Camera System.

Weigh-in-motion (WIM) systems are essential for efficient transportation and monitoring parameters such as vehicle number, speed, and weight to ensure regulatory compliance and enhance road safety. Recently, WIM measurements using the Glass Fiber Reinforced Polymer Fiber Bragg Grating (GFRP-FBG) sensors have shown robustness and effectiveness. However, the accuracy of weight evaluation using the WIM systems based on GFRP-FBG sensors can be significantly influenced by the vehicle-wandering effect, which introduces uncertainties in wheel position determination and weight calculations. This paper assessed the impact of vehicle wandering on the accuracy of a WIM measurement system based on GFRP-FBG sensors by utilizing a new hybrid sensor-camera system that integrates roadside cameras and in-pavement GFRP-FBG sensors. The detailed methodology and framework of the developed hybrid system are introduced, followed by field testing on Highway I-94 in the United States. The field testing results indicate that by using the hybrid system, the wheel load detection accuracy of the WIM system based on GFRP-FBG sensors can be controlled to be a Type I or Type III WIM according to the ASTM 1318E-09 standard, with an average accuracy ranging from 87.83% to 94.65%. At the same time, when the wander distance is less than or equal to 9 cm, the developed WIM system proves to be very cost-effective as it only comprises two GFRP-FBG sensors, one temperature FBG sensor, and one camera. These findings indicate the practical potential to enhance the accuracy of WIM systems based on GFRP-FBG sensors designed for highways for low-coast, reliable, and accurate measurements by addressing vehicle wandering effects.

Novel Weigh-in-Motion Pavement Sensor Based on Self-Sensing Nanocomposites for Vehicle Load Identification: Development, Performance Testing, and Validation.

The development of the transportation industry has led to an increasing number of overloaded vehicles, which reduces the service life of asphalt pavements. Currently, the traditional vehicle weighing method not only involves heavy equipment but also has a low weighing efficiency. To deal with the defects in the existing vehicle weighing system, this paper developed a road-embedded piezoresistive sensor based on self-sensing nanocomposites. The sensor developed in this paper adopts an integrated casting and encapsulation technology, in which an epoxy resin/MWCNT nanocomposite is used for the functional phase, and an epoxy resin/anhydride curing system is used for the high-temperature resistant encapsulation phase. The compressive stress-resistance response characteristics of the sensor were investigated by calibration experiments with an indoor universal testing machine. In addition, the sensors were embedded in the compacted asphalt concrete to validate the applicability to the harsh environment and back-calculate the dynamic vehicle loads on the rutting slab. The results show that the response relationship between the sensor resistance signal and the load is in accordance with the GaussAmp formula. The developed sensor not only survives effectively in asphalt concrete but also enables dynamic weighing of the vehicle loads. Consequently, this study provides a new pathway to develop high-performance weigh-in-motion pavement sensors.

Development and Temperature Correction of Piezoelectric Ceramic Sensor for Traffic Weighing-In-Motion.

Weighing-In-Motion (WIM) technology is one of the main tools for pavement management. It can accurately describe the traffic situation on the road and minimize overload problems. WIM sensors are the core elements of the WIM system. The excellent basic performance of WIMs sensor and its ability to maintain a stable output under different temperature environments are critical to the entire process of WIM. In this study, a WIM sensor was developed, which adopted a PZT-5H piezoelectric ceramic and integrated a temperature probe into the sensor. The designed WIM sensor has the advantages of having a small size, simple structure, high sensitivity, and low cost. A sine loading test was designed to test the basic performance of the piezoelectric sensor by using amplitude scanning and frequency scanning. The test results indicated that the piezoelectric sensor exhibits a clear linear relationship between input load and output voltage under constant environmental temperature. The linear correlation coefficient R2 of the fitting line is up to 0.999, and the sensitivity is 4.04858 mV/N at a loading frequency of 2 Hz at room temperature. The sensor has good frequency-independent characteristics. However, the temperature has a significant impact on it. Therefore, the output performance of the piezoelectric ceramic sensor is stabilized under different temperature conditions by using a multivariate nonlinear fitting algorithm for temperature compensation. The fitting result R2 is 0.9686, the root mean square error (RMSE) is 0.2497, and temperature correction was achieved. This study has significant implications for the application of piezoelectric ceramic sensors in road WIM systems.

Random Traffic Flow Simulation of Heavy Vehicles Based on R-Vine Copula Model and Improved Latin Hypercube Sampling Method.

The rationality of heavy vehicle models is crucial to the structural safety assessment of bridges. To establish a realistic heavy vehicle traffic flow model, this study proposes a heavy vehicle random traffic flow simulation method that fully considers the vehicle weight correlation based on the measured weigh-in-motion data. First, a probability model of the key parameters in the actual traffic flow is established. Then, a random traffic flow simulation of heavy vehicles is realized using the R-vine Copula model and improved Latin hypercube sampling (LHS) method. Finally, the load effect is calculated using a calculation example to explore the necessity of considering the vehicle weight correlation. The results indicate that the vehicle weight of each model is significantly correlated. Compared to the Monte Carlo method, the improved LHS method better considers the correlation between high-dimensional variables. Furthermore, considering the vehicle weight correlation using the R-vine Copula model, the random traffic flow generated by the Monte Carlo sampling method ignores the correlation between parameters, leading to a weaker load effect. Therefore, the improved LHS method is preferred.

Model Updating Concept Using Bridge Weigh-in-Motion Data.

Finite element (FE) model updating of bridges is based on the measured modal parameters and less frequently on the measured structural response under a known load. Until recently, the FE model updating did not consider strain measurements from sensors installed for weighing vehicles with bridge weigh-in-motion (B-WIM) systems. A 50-year-old multi-span concrete highway viaduct, renovated between 2017 and 2019, was equipped with continuous monitoring system with over 200 sensors, and a B-WIM system. In the most heavily instrumented span, the maximum measured longitudinal strains induced by the full-speed calibration vehicle passages were compared with the modelled strains. Based on the sensitivity study results, three variables that affected its overall stiffness were updated: Young's modulus adjustment factor of all structural elements, and two anchorage reduction factors that considered the interaction between the superstructure and non-structural elements. The analysis confirmed the importance of the initial manual FE model updating to correctly reflect the non-structural elements during the automatic nonlinear optimisation. It also demonstrated a successful use of pseudo-static B-WIM loading data during the model updating process and the potential to extend the proposed approach to using random B-WIM-weighed vehicles for FE model updating and long-term monitoring of structural parameters and load-dependent phenomena.

Inverse modeling and experimental validation for reconstructing wave sources on a 2D solid from surficial measurement.

This paper discusses a source inversion method for the reconstruction of moving or stationary wave sources on the top surface of a two-dimensional (2D) linear elastic solid. This adjoint-gradient-based source inversion method uses vibrational measurements from sensors at the top surface of the solid, which can be heterogeneous and damped, to reconstruct temporal and spatial distributions of the wave sources. The finite element method (FEM) is used to obtain wave solutions with the high-resolution discretization of source functions in space and time leading the number of inversion parameters to range in the millions. Numerical experiments, in which the iterative inversion procedure begins with an initial guess of zero loading at all points in space and time, show that the presented approach is effective at reconstructing horizontal and vertical components of force (i.e., normal and shear tractions) for multiple simultaneous moving dynamic distributed loads without any prior knowledge about the loads except that all loading is applied along the top surface of the solid. Provided that moving loads on roadways are applied to the top surface, it is shown that updating the guessed loading at just surface nodes, rather than at all nodes in space, greatly improves the inversion results. The inversion is shown to be as effective at reconstructing loads on the top surface of a solid when the solid is horizontally layered with multiple materials as when the solid it is homogeneous. Reducing the distance between sensors improves the accuracy of the inversion while reducing the width of distributed loads leads to less accurate results. The authors also validate the presented inversion method by using experimental data obtained from lab-scale tests at a high frequency (100 kHz) for a stationary load on a homogeneous solid.

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network.

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

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.

A novel traffic conflict risk measure considering the effect of vehicle weight.

INTRODUCTION: Vehicle weight is deterministic to the impact force in collision, and thus the injury risk of vehicle occupants. In China, involvement of heavy vehicles in overall and fatal crashes are prevalent, even though heavy vehicles only constitute a small proportion of overall registered motor vehicles. However, vehicle weight is rarely considered in the existing traffic conflict risk prediction and assessment models because of the unavailability of required data. METHOD: Novel risk indicators for the diagnosis of traffic conflict risk map, considering the effect of vehicle weight, are proposed, with the advantage of comprehensive traffic flow characteristics and vehicle weight data using Weigh-in-Motion (WIM) technique. Weight-incorporated risk level (WRL) and weight integrated risk level (WIRL) are established to quantify the traffic conflict risk, at an instant and over a specified time period, respectively, by extending the conventional traffic conflict risk measures including time-to-collision (TTC) and modified potential collision energy (PCE). Then, a microscopic traffic simulation model is adopted to estimate the traffic conflict risk map along a highway segment that has partial lane closure. The traffic conflict risk performances, between the risk indicators with and without considering the vehicle weight, are compared. RESULTS: The traffic conflict risks estimated using conventional risk indicators without considering the vehicle weight are generally lower than that based on WRL and WIRL. The difference is more profound when the proportion of heavy vehicles in the traffic stream increases. CONCLUSIONS: The finding is indicative to remedial engineering measures including variable message sign, speed limit, and ramp metering that can mitigate the real-time crash risks on highways, especially in adverse environmental and weather conditions, with due consideration of vehicle composition and crash worthiness of vehicles.

Train Classification Using a Weigh-in-Motion System and Associated Algorithms to Determine Fatigue Loads.

This paper presents a methodology for classifying train passages into different types with a weigh-in-motion (WIM) system to allow the calibration of railway fatigue load models and identify individual vehicles from the measurements for the continuous calibration of railway WIM stations from in-service trains. The quality assurance of the measured responses is demonstrated using statistical methods. This paper discusses the measurement station, the method used for processing the raw data, the algorithm used to identify the train types and vehicles automatically, and the limits of the obtained load spectra. The measurement errors are demonstrated to be satisfying for use in fatigue load model calibration. Furthermore, this paper proposes actions for accurately obtaining the actual traffic conditions and describes the future work required in this area.

Development and Validation of a Weigh-in-Motion Methodology for Railway Tracks.

In railways, weigh-in-motion (WIM) systems are composed of a series of sensors designed to capture and record the dynamic vertical forces applied by the passing train over the rail. From these forces, with specific algorithms, it is possible to estimate axle weights, wagon weights, the total train weight, vehicle speed, etc. Infrastructure managers have a particular interest in identifying these parameters for comparing real weights with permissible limits to warn when the train is overloaded. WIM is also particularly important for controlling non-uniform axle loads since it may damage the infrastructure and increase the risk of derailment. Hence, the real-time assessment of the axle loads of railway vehicles is of great interest for the protection of railways, planning track maintenance actions and for safety during the train operation. Although weigh-in-motion systems are used for the purpose of assessing the static loads enforced by the train onto the infrastructure, the present study proposes a new approach to deal with the issue. In this paper, a WIM algorithm developed for ballasted tracks is proposed and validated with synthetic data from trains that run in the Portuguese railway network. The proposed methodology to estimate the wheel static load is successfully accomplished, as the load falls within the confidence interval. This study constitutes a step forward in the development of WIM systems capable of estimating the weight of the train in motion. From the results, the algorithm is validated, demonstrating its potential for real-world application.

Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks.

In the application of a bridge weigh-in-motion (WIM) system, the collected data may be temporarily or permanently lost due to sensor failure or system transmission failure. The high data loss rate weakens the distribution characteristics of the collected data and the ability of the monitoring system to conduct assessments on bridge condition. A deep learning-based model, or generative adversarial network (GAN), is proposed to reconstruct the missing data in the bridge WIM systems. The proposed GAN in this study can model the collected dataset and predict the missing data. Firstly, the data from stable measurements before the data loss are provided, and then the generator is trained to extract the retained features from the dataset and the data lost in the process are collected by using only the responses of the remaining functional sensors. The discriminator feeds back the recognition results to the generator in order to improve its reconstruction accuracy. In the model training, two loss functions, generation loss and confrontation loss, are used, and the general outline and potential distribution characteristics of the signal are well processed by the model. Finally, by applying the engineering data of the Hangzhou Jiangdong Bridge to the GAN model, this paper verifies the effectiveness of the proposed method. The results show that the final reconstructed dataset is in good agreement with the actual dataset in terms of total vehicle weight and axle weight. Furthermore, the approximate contour and potential distribution characteristics of the original dataset are reproduced. It is suggested that the proposed method can be used in real-life applications. This research can provide a promising method for the data reconstruction of bridge monitoring systems.

Weigh-in-Motion System Based on an Improved Kalman and LSTM-Attention Algorithm.

A weigh-in-motion (WIM) system continuously and automatically detects an object's weight during transmission. The WIM system is used widely in logistics and industry due to increasing labor and time costs. However, the accuracy and stability of WIM system measurements could be affected by shock and vibration under high speed and heavy load. A novel six degrees-of-freedom (DOF), mass-spring damping-based Kalman filter with time scale (KFTS) algorithm was proposed to filter noise due to the multiple-input noise and its frequency that is highly coupled with the basic sensor signal. Additionally, an attention-based long short-term memory (LSTM) model was built to predict the object's mass by using multiple time-series sensor signals. The results showed that the model has superior performance compared to support vector machine (SVM), fully connected network (FCN) and extreme gradient boosting (XGBoost) models. Experiments showed this improved deep learning model can provide remarkable accuracy under different loads, speed and working situations, which can be applied to the high-precision logistics industry.

Influence of Trajectory and Dynamics of Vehicle Motion on Signal Patterns in the WIM System.

This paper presents the analyses of the signals recorded by the main sensors of a WIM test station in the cases of abnormal runs (i.e., runs with the changes of trajectory or the dynamics of vehicle motion). The research involved strain gauges which are used for measuring the weight of vehicles, inductive loops, as well as piezoelectric sensors used, inter alia, to detect twin wheels and to determine where a vehicle passes through a station. Since the designers intend the station to be able to implement the direct enforcement function, the selection of runs deviating from the normative ones constitutes an important issue for the assessment of the measurement reliability. The study considered the location of the trajectory of the runs, the dynamics (acceleration/braking) and the trajectory changes. The change in the amplitude and the value of the signal recorded by the strain gauges as a function of the location (position) of the contact between sensor and tires is a noteworthy observation which indicates the need to monitor this parameter in automatic WIM systems. Other tests also demonstrated the influence of the analysed driving parameters on the recorded results. However, by equipping the WIM station with a set of duplicate strain gauges, the measurement errors of the gross weight and axle loads are normally within the accuracy limits of class A(5) stations. Only in the case of accelerating/decelerating, does the error in measuring the load of a single axle reach several per cent.

High Accuracy Weigh-In-Motion Systems for Direct Enforcement.

In many countries, work is being conducted to introduce Weigh-In-Motion (WIM) systems intended for continuous and automatic control of gross vehicle weight. Such systems are also called WIM systems for direct enforcement (e-WIM). The achievement of introducing e-WIM systems is conditional on ensuring constant, known, and high-accuracy dynamic weighing of vehicles. WIM systems weigh moving vehicles, and on this basis, they estimate static parameters, i.e., static axle load and gross vehicle weight. The design and principle of operation of WIM systems result in their high sensitivity to many disturbing factors, including climatic factors. As a result, weighing accuracy fluctuates during system operation, even in the short term. The article presents practical aspects related to the identification of factors disturbing measurement in WIM systems as well as methods of controlling, improving and stabilizing the accuracy of weighing results. Achieving constant high accuracy in weighing vehicles in WIM systems is a prerequisite for their use in the direct enforcement mode. The research results presented in this paper are a step towards this goal.

BwimNet: A Novel Method for Identifying Moving Vehicles Utilizing a Modified Encoder-Decoder Architecture.

Traffic loading monitoring plays an important role in bridge structural health monitoring, which is helpful in overloading detection, transportation management, and safety evaluation of transportation infrastructures. Bridge weigh-in-motion (BWIM) is a method that treats traffic loading monitoring as an inverse problem, which identifies the traffic loads of the target bridge by analyzing its dynamic strain responses. To achieve accurate prediction of vehicle loads, the configuration of axles and vehicle velocity must be obtained in advance, which is conventionally acquired via additional axle-detecting sensors. However, problems arise from additional sensors such as fragile stability or expensive maintenance costs, which might plague the implementation of BWIM systems in practice. Although data-driven methods such as neural networks can estimate traffic loadings using only strain sensors, the weight data of vehicles crossing the bridge is difficult to obtain. In order to overcome these limitations, a modified encoder-decoder architecture grafted with signal-reconstruction layer is proposed in this paper to identify the properties of moving vehicles (i.e., velocity, wheelbase, and axle weight) using merely the bridge dynamic response. Encoder-decoder is an unsupervised method extracting higher features from original data. The numerical bridge model based on vehicle-bridge coupling vibration theory is established to illustrate the applicability of this new encoder-decoder method. The identification results demonstrate that the proposed approach can predict traffic loadings without using additional sensors and without requiring vehicle weight labels. Parametric studies also show that this new approach achieves better stability and reliability in identifying the properties of moving vehicles, even under the circumstances of large data pollution.

Smart Graphite-Cement Composite for Roadway-Integrated Weigh-In-Motion Sensing.

Smart multifunctional composites exhibit enhanced physical and mechanical properties and can provide structures with new capabilities. The authors have recently initiated a research program aimed at developing new strain-sensing pavement materials enabling roadway-integrated weigh-in motion (WIM) sensing. The goal is to achieve an accurate WIM for infrastructure monitoring at lower costs and with enhanced durability compared to off-the-shelf solutions. Previous work was devoted to formulating a signal processing algorithm for estimating the axle number and weights, along with the vehicle speed based on the outputs of a piezoresistive pavement material deployed within a bridge deck. This work proposes and characterizes a suitable low-cost and highly scalable cement-based composite with strain-sensing capabilities and sufficient sensitivity to meet WIM signal requirements. Graphite cement-based smart composites are presented, and their electromechanical properties are investigated in view of their application to WIM. These composites are engineered for scalability owing to the ease of dispersion of the graphite powder in the cement matrix, and can thus be used to build smart sections of road pavements. The research presented in this paper consists of electromechanical tests performed on samples of different amounts of graphite for the identification of the optimal mix in terms of signal sensitivity. An optimum inclusion level of 20% by weight of cement is obtained and selected for the fabrication of a plate of 30 × 15 × 5 cm3. Results from load identification tests conducted on the plate show that the proposed technology is capable of WIM.

Sensor Data Fusion in Multi-Sensor Weigh-In-Motion Systems.

In this paper, we present the results of a comparison of two estimators of the gross vehicle weight (GVW) and the static load of individual axles of vehicles. The estimators were used to process measurement data derived from Multi-Sensor Weigh-In-Motion systems (MS-WIM). The term estimator is understood as an algorithm according to which the dynamic axle load measurement results are processed in order to determine the static load. The result obtained is called static load estimate. As a measure of measurement uncertainty, we adopted the standard deviation of the static load estimate. The mean value and the maximum likelihood estimators were compared. Studies were conducted using simulation methods based on synthetic data and experimental data obtained from a WIM system equipped with 16 lines of polymer axle load sensors. We have shown a substantially lower uncertainty of estimates determined using the maximum likelihood estimator. The results obtained have considerable practical significance, particularly during long-term usage of multi-sensor WIM systems.