Bridge Weigh-in-motion System Research Articles

A deep learning method for heavy vehicle load identification using structural dynamic response

The identification of dynamic vehicle loads is crucial for bridge health monitoring. Currently, bridge weigh-in-motion (BWIM) system and image recognition methods are extensively used for vehicle load identification. However, BWIM systems are costly, while single image recognition methods struggle to accurately identify vehicle weight. Hence, an improved approach is proposed to address the above issues, which employs a Bidirectional Long Short-Term Memory (BiLSTM) network model to establish the mapping relationship between vertical deflection of bridges and vehicle loads. Firstly, based on practical engineering, the Grey Wolf Optimization-Variational Mode Decomposition (GWO-VMD) method is employed to address the influence of temperature effects on deflection response data. Then, through a time matching algorithm, the sliced deflection data and the corresponding BWIM system vehicle load information are paired to form the dataset for the deep learning models. The results of the models demonstrate that the optimal BiLSTM model exhibits better robustness and generalization performance. It achieves high accuracy of 97.9% in load identification. The method significantly reduces the economic cost and improves the accuracy of vehicle load identification.

A non-contact vehicle weighing approach based on bridge weigh-in-motion framework and computer vision techniques

Bridge weigh-in-motion (BWIM) system plays increasingly important role in vehicle load monitoring and bridge maintenance due to advantages of high efficiency and little impact on traffic. However, the contact-type sensors in a traditional BWIM system should be installed in advance and is environment-sensitive, which hinders its application. To this end, a vision-based contactless BWIM (cBWIM) system is proposed. The core idea of the current method is to replace the traditional contact-type sensors by two consumer cameras to realize real-time monitoring of bridge displacement and vehicle trajectory. The cBWIM system consists of three modules, including the axle weight identification algorithm, the bridge displacement monitoring system and the vehicle trajectory tracking system. The influence line-based Moses algorithm is utilized for identification of axle weight. For bridge displacement monitoring, the minimum bounding rectangle-based similarity matching method is proposed to achieve high accuracy measurement of displacement and real-time data processing. For vehicle trajectory tracking, a deep learning-based YOLOv5s algorithm is adopted as a detector, combined with DeepSort algorithm to realize real-time tracking of vehicle position. The cBWIM system is applicable for non-uniform motion and vehicle occlusion cases. Laboratory tests covering various cases (i.e. uniform motion, non-uniform motion and visual occlusion) and field measurement on a full-scale bridge have demonstrated that the proposed method is effective and reliable and can provide alternative tool to the current vehicle weighing methods.

An Extended Bridge Weigh-in-Motion System without Vehicular Axles and Speed Detectors Using Nonnegative LASSO Regularization

The bridge weigh-in-motion (BWIM) technique uses the instrumented bridge on a large scale to identify the axle weight of a passing vehicle. Vehicle configurations, e.g., axle number and wheelbase, are crucial for the BWIM system, which require additional axle detectors. Free of axle (FAD) sensors are often used to obtain vehicle information, but they are only suitable for specific bridge types, such as slab-girder bridges. The concept of a virtual-axle-based algorithm, without requiring axle detectors, has been developed, and the validity of this algorithm has been verified numerically and experimentally. However, this algorithm assumes the vehicle speed as a known input, indicating that additional speed sensors/devices are still required in the BWIM system. Using this virtual-axle-based algorithm in a field test, it is found that the identification accuracy of the BWIM system is sensitive to the vehicle speed, and it shows poor recognition of vehicle configuration. To improve the recognition accuracy and remove vehicle speed detectors from the BWIM system, an extended BWIM system is proposed using the regularization technique and iterative approach. Both vehicular virtual axles and speeds are assumed in this approach. An error function based on the measured responses and theoretical ones is built to evaluate these assumed vehicle configurations and speeds. The effectiveness of the proposed approach is verified by the field tests. The results show that the proposed approach can obtain high recognition accuracy, which is close to Moses’s algorithm using FAD sensors. Compared with the previous virtual-axle-based algorithm, the recognition accuracy and robustness of the proposed approach are greatly improved. The proposed approach is still challenged by real-world traffic because this paper only considers the case when a single vehicle passes over the bridge. Nevertheless, the proposed extended BWIM system shows potential practical applications as it can further reduce costs and be applicable to more bridge types.

Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review

Bridge health monitoring (BHM) has grown as an essential component of the maintenance paradigm in any transportation network around the world. The mostly used visual inspection method has limitations such as inspector bias, inaccessibility in remote areas, need for human physical presence at the bridge location, etc. On the other hand, vibration-based BHM methods require artificial excitation to the bridge. This motivates researchers to explore potential alternatives for BHM. The bridge weigh-in-motion (B-WIM) system has emerged as a promising alternative with the potential to augment traditional BHM techniques. The advantages of B-WIM systems are durability, portability, easy installation, etc. In addition to weight estimation of passing vehicles, it provides other structural informations that are helpful for bridge assessment. It also overcomes the limitations of pavement-based WIM systems. Most importantly, a B-WIM system uses the same components as a standard BHM system, making BHM a by-product and economical compared to a standalone BHM system. With the development of B-WIM theory, recent researches have revealed the feasibility of B-WIM system for BHM. However, the applicability of the B-WIM system for BHM in any type of bridge and condition is limited. Through the fundamentals of B-WIM theory, this paper reviews the existing bridge response-based BHM methods that involved B-WIM algorithm. The findings are discussed in order to identify the remaining challenges and provide insight to the future aspects.

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.

Dual-purpose procedure for bridge health monitoring and weigh-in-motion used for multiple-vehicle events

Current literature on integrating structural health monitoring (SHM) systems with Bridge-Weigh-in-Motion (BWIM) does not extend to multiple-vehicle events. Also, in most of the SHM-BWIM systems, the trucks’ transverse position change is not appropriately considered as a potential source of false alarms. In this study, a multiple-presence dual-purpose (MPDP) SHM approach is proposed to monitor bridge integrity using the existing BWIM system sensors in single and multiple-truck events. This approach centers on the influence line (IL) change and applies a recently developed multiple-presence IL (MP-IL) technique to the SHM domain for the first time in the literature. This can effectively handle the multiple presence issue to make the integrated system more practical. Also, the proposed procedure studies the impact of transverse position changes to provide a more realistic bridge health monitoring approach. To show the approach applicability, an existing long-span concrete-box-girder bridge was modeled and validated against a set of experimental data using known large events. Eleven damage scenarios were simulated to evaluate the MPDP approach under single and multiple truck events. Based on the results, the MPDP SHM procedure coupled with the novel MP-IL could effectively detect the damage scenarios in both single and multiple-truck events, even when the transverse position changed.

Performance assessment of a bridge WIM system using optical strands

A bridge Weigh-in-Motion (WIM) system consists in estimating gross vehicle weight while they cross a bridge, without any sensor in the pavement. It comprises a set of sensors installed under the bridge deck and in bridge elements, and dedicated data analysis tools, allowing to assess the moving loads from the mechanical response of the bridge structure. In this paper, a novel Bridge WIM solution is described, which uses a low number of optical strands strain sensors, and has been implemented on 13 bridges worldwide. The accuracy of this system for gross vehicle weight is assessed on two bridges in France and Italy. The system met the accuracy class A(5) of the COST323 specifications on one bridge. Moreover, the data collected by the system is useful also for the structural health monitoring of the bridge.

A Bayesian algorithm with second order autoregressive errors for B-WIM weight estimation

Bridge weigh-in-motion (B-WIM) systems are employed for estimating axle weights of vehicles traveling over the bridge structure, providing useful information for many applications regarding structural health monitoring. In this regard, the improvement of single axle weight estimates is a major concern, since B-WIM systems in general have more difficulties with such quantities when compared to the prediction of the total weight, mainly for closely spaced axles. The main goal of the present work is to develop a weigh strategy for B-WIM systems that prevents the occurrence of spurious values, improving the overall accuracy of estimates for single axle weights. For reaching this goal, prior beliefs regarding axle weights, such as their order of magnitude and similarity for closely spaced axles, are employed. Bayesian modeling is well suited for the present problem, since it allows the suitable combination of prior beliefs and experimental data for providing proper weight estimates. In addition, a covariance matrix based on a second order autoregressive process is employed for modeling the error between theoretical and measured responses aiming to overcome the negative effects due to the presence of serial correlation in such errors. Both simulated signals and an example of B-WIM system calibration data are employed for assessing the suitability of the proposed approach. Moreover, sensitivity analyses are conducted aiming to check the robustness of the strategy to its own model parameters. For all analyses, the overall accuracy of the proposed approach, when considering both single axle as well as gross vehicle weight (GVW) estimates, outperforms the baseline algorithms. Furthermore, the sensitivity analyses indicate that the conclusions are the same for distinct prior distributions based on the same prior information.

Acceleration-Based Deep Learning Method for Vehicle Monitoring

An automatic vehicle monitoring system can provide supports not only for intelligent transportation systems, but also for bridge weigh-in-motion (BWIM) systems, which use structural response to identify vehicle weights. In this paper, we provide a vehicle monitoring solution for acceleration-based BWIM system, utilizing deep learning and wavelet transform methods. The monitoring task is divided into three subtasks, including valid sequence detection, valid axle location, and driving lane identification. In first procedure, a shallow convolutional neural network is trained using time-frequency spectrograms to discover valuable time series. After that, an adaptive wavelet transform method is employed to locate axles from each valid sequence. Finally, the driving lane can be determined by cross-comparing vibration responses. Comparing with solutions based on all-in-one deep networks, the proposed method is computationally efficient and has improved generalization capability owing to the three-step division of the task. Evaluation is conducted on a multi-lane highway bridge located in Tokyo. Results show that 97% of vehicles can be identified correctly. For all recognized vehicles, the accuracy of driving lane detection is 100%.

Bridge Weigh-in-Motion system for the identification of train loads using fiber-optic technology

This paper presents a cost-effective Bridge Weigh-in-Motion (B-WIM) system for the identification of train loads using fiber-optic technology. The system is capable of estimating the train’s speed, geometry and static axle loads, using the algorithm proposed by Moses. This algorithm involves the resolution of an inverse identification problem where the measured structural response is known and the loading scheme is unknown. The method relies on the concept of Influence Line (IL), which is estimated from the passage of a calibration vehicle with known characteristics. A numerical validation example, based on a simply supported bridge with a train passing at different speeds, demonstrated the functionality and potential accuracy of the implemented B-WIM system. For speeds up to 120 km/h, the maximum estimated errors of the wheelbase and axle loads were 10 cm and 2.5%, respectively. A B-WIM system was installed in an existing filler-beam bridge, located on the Portuguese Railways, consisting of a minimalist layout of fiber Bragg grating sensors, which guarantee higher-quality measurements, quick installation and long-term stability. The system allowed a precise characterization of several Alfa Pendular, Urban and Regional trains. This study constitutes a step forward in the development of online B-WIM systems capable of automatically estimating traffic characteristics. The accurate estimation of the traffic loads is a valuable information for the evaluation of the structural integrity and safety of railway bridges.

Moving point load approximation from bridge response signals and its application to bridge Weigh-in-Motion

This paper presents a revised theoretical interpretation for the analysis of bridge response signals arising during a vehicle passage. The derivation shows that it is possible to approximate the forcing function from bridge responses, by means of a finite difference scheme. This Point Load Approximation (PLA) is also formulated into explicit expressions following classic beam theory. This idea is then generalized to include other load effects and their corresponding time derivatives. These findings are particularly relevant for Bridge Weigh-in-Motion (BWIM) applications. The influence line for a BWIM system with multiple sensors can be explicitly defined using the presented formulation. The theory and applications are validated numerically by means of examples and case studies. These studies include the performance evaluation of a multiple strain sensor BWIM system, as well as, a novel approach to acceleration-based BWIM.

Comparative Accuracy Analysis of Truck Weight Measurement Techniques

Roads and bridges are designed to meet the transportation demands for traffic volume and loading. Knowledge of the actual traffic is needed for a rational management of highway infrastructure. There are various procedures and equipment for measuring truck weight, including static and in weigh-in-motion techniques. This paper aims to compare four systems: portable scale, stationary truck weigh station, pavement weigh-in-motion system (WIM), and bridge weigh-in-motion system (B-WIM). The first two are reliable, but they have limitations as they can measure only a small fraction of the highway traffic. Weigh-in-motion (WIM) measurements allow for a continuous recording of vehicles. The presented study database was obtained at a location that allowed for recording the same traffic using all four measurement systems. For individual vehicles captured on a portable scale, the results were directly compared with the three other systems’ measurements. The conclusion is that all four systems produce the results that are within the required and expected accuracy. The recommendation for an application depends on other constraints such as continuous measurement, installation and operation costs, and traffic obstruction.

Identifying damage on a bridge using rotation-based Bridge Weigh-In-Motion

Bridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.

A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes

A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges, and in some extreme cases, it will directly lead to a bridge failure. Bridge weigh-in-motion (BWIM) system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways. However, the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge. The fast Fourier transform (FFT) method, which can significantly purify the collected structural responses (dynamic strains) received from sensors or transducers, was used in axle counting, detection, and axle weighing technology in this study. To further improve the accuracy of the BWIM system, the field-calibrated influence lines (ILs) of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method. In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones, the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system. Moreover, the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.

Iterative linear optimization method for bridge weigh-in-motion systems using accelerometers

The static bridge weigh-in-motion (BWIM) systems are mostly based on strain measurements and are particularly suited for stiff short-span bridges. Recently, the BWIM systems based on acceleration measurements are developed for long-span bridges because of the portability and low-cost of accelerometers as compared to strain gauges. Although, these BWIM systems can estimate the gross vehicle weights (GVWs) with high accuracy, but they fail to identify the weights of individual axles accurately especially for vehicles with closely spaced axles. In this paper, an iterative linear optimization problem (ILOP) was proposed to accurately identify the individual axle weights and GVWs of vehicles traversing a bridge. A BWIM system consisting of only microelectromechanical system (MEMS) accelerometers was employed in an in-service steel girder bridge having multiple lanes. The proposed method used the bridge displacement responses as the measured responses which were determined from the recorded acceleration data. The information about the vehicle speed, number of axles and axle spacings were obtained by identifying the peaks in the recorded acceleration data. The effectiveness and accuracy of the proposed method were demonstrated through field tests using the four-axle test vehicles with closely spaced axles. The results showed that the axle weights of vehicles with closely spaced axles could be identified with much better accuracy by the proposed method as compared to classic BWIM systems which are based on Moses’ original algorithm.

Using Statistical Analysis of an Acceleration-Based Bridge Weigh-In-Motion System for Damage Detection

This paper develops a novel method of bridge damage detection using statistical analysis of data from an acceleration-based bridge weigh-in-motion (BWIM) system. Bridge dynamic analysis using a vehicle-bridge interaction model is carried out to obtain bridge accelerations, and the BWIM concept is applied to infer the vehicle axle weights. A large volume of traffic data tends to remain consistent (e.g., most frequent gross vehicle weight (GVW) of 3-axle trucks); therefore, the statistical properties of inferred vehicle weights are used to develop a bridge damage detection technique. Global change of bridge stiffness due to a change in the elastic modulus of concrete is used as a proxy of bridge damage. This approach has the advantage of overcoming the variability in acceleration signals due to the wide variety of source excitations/vehicles—data from a large number of different vehicles can be easily combined in the form of inferred vehicle weight. One year of experimental data from a short-span reinforced concrete bridge in Slovenia is used to assess the effectiveness of the new approach. Although the acceleration-based BWIM system is inaccurate for finding vehicle axle-weights, it is found to be effective in detecting damage using statistical analysis. It is shown through simulation as well as by experimental analysis that a significant change in the statistical properties of the inferred BWIM data results from changes in the bridge condition.

Method and sensor for monitoring weight of trucks in motion based on bridge girder end rotations

Traffic monitoring, including the estimation of axle and gross vehicle weights of passing trucks over bridges plays an important role in the determination of actual service loads. Knowledge of these loads aides in the development of appropriate maintenance plans and enforcement of weight limits on bridges. Bridge weigh in motion (BWIM) technology uses bridges as a scale to measure truck weights and speeds. The research reported in this article pertains to the development of a new BWIM system based on the monitoring of girder rotations at the bridge abutments. The measured end rotations are directly related to the axle loads by means of influence lines. The changes in girder rotations at the abutments are measured by a fibre optic rotation sensor, specifically designed for the proposed BWIM system. The rotation sensors are then incorporated into the BWIM system for correlation of the axle weights to the end span rotations. The applicability and accuracy of the proposed system was examined by tests on a four-span steel girder bridge. The field implementation included calibration and validation of the system with trucks pre-weighed at a nearby weigh station.

Deep Sensing Approach to Single-Sensor Vehicle Weighing System on Bridges

Bridge weigh-in-motion (BWIM) is a technique for detecting overloaded vehicles crossing a bridge without requiring them to stop. It may also be useful for monitoring the structural health of the bridge itself. To achieve accurate weighing of each vehicle, its properties, such as speed, locus, and wheel positions, should be estimated in advance. Conventionally, such information has been obtained via additional sensors such as cameras or via peak-signal detection, using multiple sensors installed across the bridge. This may require substantial computational resources or expensive synchronization between sensors, and the complexity of the overall BWIM system may lead to frequent breakdowns. In this paper, we propose a single-sensor-based BWIM system that utilizes a deep neural network. First, a vehicle’s properties are obtained via feature extraction from the bridge strain response, as sampled by a single strain sensor. BWIM is then performed, using the same response data. The model parameters for vehicle detection are optimized automatically by consulting a surveillance camera while obtaining ground-truth data for a large number of vehicles crossing the bridge. After the model is optimized for the target bridge, the camera may be removed. Our proposal paves the way toward low-cost, compact, and single-sensor BWIM systems.

New Bridge Weigh-in-Motion System Using Piezo-Bearing

The traditional BWIM (bridge weigh-in-motion) system measures the deformation of the bridge by means of sensors and uses these measurements to estimate the characteristics of passing traffic by means of dedicated algorithms. However, the application of the BWIM system is subordinated to the type of superstructure, composition, geometry, materials, and shape of the bridge, the location of the strain sensors used in the system, and the need to calibrate the measured strain curve and of a precise model of the structure at hand. In order to be free from these constraints, this paper proposes a simpler BWIM system using the bridge bearings as a weighing scale to measure the reaction forces at the supports resulting from the passing traffic. To that goal, the piezocomposite element known for its durability and responsiveness to external loading is embedded appropriately in the bridge bearing to achieve the piezo-bearing. This paper presents the BWIM system constituted by the so-called piezo-bearing, the results of a series of tests conducted to verify the responsiveness of the system to external dynamic excitation, and a numerical example to validate the feasibility of the proposed BWIM system. The numerical example shows that the identification of the characteristics of the vehicle crossing the bridge can be realized based simply upon the theory of mechanics using the time histories of the measured reaction forces instead of the deformation of the bridge.

Development of a Bridge Weigh-in-Motion System Based on Long-Gauge Fiber Bragg Grating Sensors

Monitoring the weight of vehicles is essential for policymakers to maintain the condition of bridges. In the group of methods for weighing vehicles, bridge weigh-in-motion (B-WIM) systems are among the most prominent and have several advantages, such as convenience and no interruption to traffic. However, they still have one drawback in that, for most systems, a vehicle’s velocity and wheelbase need to be given in advance. Currently, this problem is usually solved by adding an axle detector, which leads to higher costs. Under such circumstances, this paper presents an alternative B-WIM system to measure a vehicle’s velocity, wheelbase, and axial and gross weight merely based on a single set of long-gauge fiber Bragg grating (FBG) sensors without additional devices. To test the effectiveness of this method under various conditions, a vehicle-bridge interaction (VBI) system simulation was performed based on the result of an indoor experiment. Also, an in situ test on an expressway bridge was performed to initially verify its feasibility. The results showed that this method can achieve its function with good accuracy under various conditions.