Bridge Weigh-in-motion Systems 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.

Machine Learning and Signal Processing for Bridge Traffic Classification with Radar Displacement Time-Series Data

This paper introduces a novel nothing-on-road (NOR) bridge weigh-in-motion (BWIM) approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series data. BWIMs allow site-specific structural health monitoring (SHM) but are usually difficult to attach and maintain. GBR measures the bridge deflection contactless. In this study, GBR and an unmanned aerial vehicle (UAV) monitor a two-span bridge in Germany to gather ground-truth data. Based on the UAV data, we determine vehicle type, lane, locus, speed, axle count, and axle spacing for single-presence vehicle crossings. Since displacement is a global response, using peak detection like conventional strain-based BWIMs is challenging. Therefore, we investigate data-driven machine learning approaches to extract the vehicle configurations directly from the displacement data. Despite a small and imbalanced real-world dataset, the proposed approaches classify, e.g., the axle count for trucks with a balanced accuracy of 76.7% satisfyingly. Additionally, we demonstrate that, for the selected bridge, high-frequency vibrations can coincide with axles crossing the junction between the street and the bridge. We evaluate whether filtering approaches via bandpass filtering or wavelet transform can be exploited for axle count and axle spacing identification. Overall, we can show that GBR is a serious contender for BWIM systems.

A Data-Driven Approach for Bridge Weigh-in-Motion from Impact Acceleration Responses at Bridge Joints

Bridge weigh-in-motion (BWIM) serves as a method to obtain the weight of passing vehicles from bridge responses. Most BWIM systems proposed so far rely on the measurement of bridge global vibration data, usually strain, to determine the vehicle load. However, because the bridge’s global response is sensitive to all vehicles on the bridge, the global vibration-based BWIM techniques usually suffer from inaccuracy in the case where multiple vehicles are present on the bridge. In this paper, a data-driven approach is proposed to extract the passing vehicle’s weight and driving speed from vertical acceleration at the bridge joint. As a type of local vibration, the impulse acceleration responses at a bridge joint can be recorded only during a short period when a vehicle is passing over the joint and are thus not sensitive to vehicles at other locations of the bridge. A field test is conducted at a bridge to prepare labeled training data for the use of a convolutional neural network. One accelerometer is installed on the bridge joint to record impulse acceleration, while the vehicle’s weight and driving speed are obtained from a WIM station and a camera near the bridge, respectively. A network that detects the vehicle’s passage as well as its passing lane is first proposed, followed by a 1-D convolutional neural network that uses the raw data of acceleration as the input to predict the vehicle’s gross weight and driving speed. A comparison is made between the 1-D network and an updated 2-D network that uses the wavelet coefficients as the input matrix. The latter one shows better performance, indicating that it is important to choose the proper input data for the network to be trained. A transfer learning technique is used to test the feasibility of the proposed method. Results show that the proposed method can be extended with limited data to bridges other than the bridge where the network is trained.

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.

A strain gauge-based Bridge Weigh-In-Motion system using deep learning

Several breakthroughs have appeared in different applications due to deep learning which can be applied in Bridge Weigh-In-Motion (BWIM) systems as well. Therefore, deep learning applications should be examined in BWIM systems. However, some deep learning-based solutions have already been proposed in the international literature, they cannot be compared to static methods because available datasets are not appropriate to train and test artificial neural networks. In addition, numerous other aspects have been already considered during tests. In the current paper, a numerically simulated and validated comprehensive dataset is provided. It is designed to meet requirements of incremental development. This dataset makes it possible to benchmark static algorithms and deep learning-based methods on the same dataset. In the second part of the paper, a deep learning-based solution is proposed. The developed solution shows promising results on the provided dataset and also demonstrates the applicability of the dataset.

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.

Trajectory Tracking and Load Monitoring for Moving Vehicles on Bridge Based on Axle Position and Dual Camera Vision

Monitoring traffic loads is vital for ensuring bridge safety and overload controlling. Bridge weigh-in-motion (BWIM) technology, which uses an instrumented bridge as a scale platform, has been proven as an efficient and durable vehicle weight identification method. However, there are still challenges with traditional BWIM methods in solving the inverse problem under certain circumstances, such as vehicles running at a non-constant speed, or multiple vehicle presence. For conventional BWIM systems, the velocity of a moving vehicle is usually assumed to be constant. Thus, the positions of loads, which are vital in the identification process, is predicted from the acquired speed and axle spacing by utilizing dedicated axle detectors (installed on the bridge surface or under the bridge soffit). In reality, vehicles may change speed. It is therefore difficult or even impossible for axle detectors to accurately monitor the true position of a moving vehicle. If this happens, the axle loads and bridge response cannot be properly matched, and remarkable errors can be induced to the influence line calibration process and the axle weight identification results. To overcome this problem, a new BWIM method was proposed in this study. This approach estimated the bridge influence line and axle weight by associating the bridge response and axle loads with their accurate positions. Binocular vision technology was used to continuously track the spatial position of the vehicle while it traveled over the bridge. Based on the obtained time–spatial information of the vehicle axles, the ordinate of influence line, axle load, and bridge response were correctly matched in the objective function of the BWIM algorithm. The influence line of the bridge, axle, and gross weight of the vehicle could then be reliably determined. Laboratory experiments were conducted to evaluate the performance of the proposed method. The negative effect of non-constant velocity on the identification result of traditional BWIM methods and the reason were also studied. Results showed that the proposed method predicted bridge influence line and vehicle weight with a much better accuracy than conventional methods under the considered adverse situations, and the stability of BWIM technique also was effectively improved. The proposed method provides a competitive alternative for future traffic load monitoring.

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.

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.

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.

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.

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.

A Simplified Shear-Strain Based Bridge Weigh-In-Motion Method For In-Service Highway Bridges

Bridge Weigh-In-Motion (BWIM) has been demonstrated to be reliable for obtaining critical information about the characteristics of trucks that travel over the highways. Continued improvements provides greater opportunity for increased use of BWIM. Traditional BWIM systems based on measuring the bending strain of the bridge have various challenges which has led to a class of BWIM methodologies that employ the use of shear strain in determining the gross vehicle weight (GVW) of crossing trucks. However, the known techniques of these shear-strain BWIM methods assume or measure the shear influence line for the calculation of the GVW. In this paper, an alternative shear-strain based BWIM technique is proposed. The method presented here is independent of the influence line, does not require a measurement of the speed of the truck, and is based on the difference in magnitude observed at the discontinuity of the shear strain record as a truck crosses over the sensor location on the bridge. A series of field tests is presented that demonstrate this shear-strain based BWIM method has error levels consistent with other more complex BWIM methods and as such has great potential to be used for determining the GVWs of trucks that travel on simple or multispan bridges in a consistent and reliable manner.

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.

Nothing-on-road bridge weigh-in-motion considering the transverse position of the vehicle

Bridge weigh-in-motion (BWIM) is a technology that uses the bridge as a weighing platform to estimate vehicle weights. Previous research has shown that ignoring the transverse position (TP) of vehicles may lead to significant identification errors of vehicle weight for BWIM systems. However, the traditional method to identify the vehicle’s exact TP requires using axle detectors on the road surface. Aiming at achieving the nothing-on-road (NOR) BWIM, this paper proposes a novel NOR BWIM algorithm that is able to identify the vehicle’s TP and axle weights using only the weighing sensors. Numerical simulations are conducted using three-dimensional vehicle and bridge models and the proposed algorithm was used to identify the vehicle’s TP and axle weights. The results show that the proposed algorithm can successfully identify the vehicle’s TP and that the identification accuracy of axle weights and gross vehicle weight is significantly improved after considering the vehicle’s TP. The effects of the road surface condition, the vehicle speed, the vehicle width, and different measurement stations on the identification accuracy are investigated. The proposed algorithm is then verified by a field study and the results indicate that the proposed algorithm can achieve acceptable identification accuracy in practice.

B-WIM systems application on reinforced concrete bridge structural assessment and highway traffic characterization

Abstract The vehicles that travel on Brazilian highways have changed a lot in the last decades, with an increase in the traffic load and in the amount of trucks. This fact is not exclusive to our country, so much that in order to assess the structural safety of bridges, there was a great development in bridge weigh-in-motion systems (B-WIM) the last decade, especially in developed countries. Moses, in 1979, was the first one to introduce the B-WIM concept. This work presents the results of a B-WIM system applied on a bridge over the Lambari river, located at BR 153 in Uruaçu (Goiás). The weigh-in-motion technique used is based on Moses' Algorithm and uses influence lines obtained direct from traffic. Traffic characterization of that particular highway, as well as the effects introduced in the bridge structure and the experimental dynamic amplification factor are also discussed. At the end it is concluded that the system used is capable of detecting, with good precision, the axle spacing and the gross vehicle weight shows errors inferior to 3% when compared with the gross weight acquired with static scale.

Vehicle axle identification using wavelet analysis of bridge global responses

Bridge weigh-in-motion (BWIM) technique uses an instrumented bridge as a weighing scale to estimate vehicle weights. Traditional BWIM systems use axle detectors placed on the road surface to identify vehicle axles. However, the axle detectors have poor durability due to the direct exposure to the traffic. To resolve this issue, a free-of-axle-detector (FAD) algorithm, which eliminates the use of axle detectors, was proposed. As a further improvement to simplify the BWIM systems, the concept of nothing-on-road (NOR) BWIM was recently introduced. The axle identification method proposed in this paper is an attempt to achieve the NOR BWIM, i.e., using bridge global responses to identify vehicle axles. Wavelet analysis is applied to extract the axle information from the global responses. This allows the BWIM technique to be achieved with only weighing sensors. Numerical simulations are conducted using three-dimensional vehicle and bridge models and the effect of several parameters, including sampling frequency, road surface condition and measurement noise on the identification accuracy is investigated. The results demonstrate that the proposed identification method using wavelet analysis can accurately identify vehicle axles, except for cases where the road surface condition is rough or measurement noises exceed certain levels.

Recent developments in bridge weigh in motion (B-WIM)

Bridge weigh in motion (B-WIM) uses accurate sensing systems to transform an existing bridge into a mechanism to determine actual traffic loading. This information on traffic loading can enable efficient and economical management of transport networks and is becoming a valuable tool for bridge safety assessment. B-WIM can provide site-specific traffic loading on deteriorating bridges, which can be used to determine if the reduced capacity is still sufficient to allow the structure to remain operational and minimise unnecessary replacement or rehabilitation costs and prevent disruption to traffic. There have been numerous reports on the accuracy classifications of existing B-WIM installations and some common issues have emerged. This paper details some of the recent developments in B-WIM which were aimed at overcoming these issues. A new system has been developed at Queens University Belfast using fibre optic sensors to provide accurate axle detection and improved accuracy overall. The results presented in this paper show that the fibre optic system provided much more accurate results than conventional WIM systems, as the FOS provide clearer signals at high scanning rates which require less filtering and less post-processing. A major disadvantage of existing B-WIM systems is the inability to deal with more than one vehicle on the bridge at the same time; sensor strips have been proposed to overcome this issue. A bridge can be considered safe if the probability that load exceeds resistance is acceptably low, hence B-WIM information from advanced sensors can provide confidence in our ageing structures.