Search Results (82)

Machine learning (ML)-based techniques have received significant attention in various fields of industry and science. In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. To evaluate the prediction capabilities of ML, this study examines the performance of several ML algorithms in estimating the total weight and location of vehicles on a bridge using strain sensing. A novel framework based on a combined model and data-driven approach is described, consisting of the establishment of the finite element (FE) model, its updating according to load testing results, and data augmentation to facilitate the training of selected physics-informed regression models. The article discusses the design of the Fiber Bragg Grating (FBG) sensor-based Bridge Weigh-in-Motion (BWIM) system, specifically focusing on several supervised regression models of different architectures. The current work proposes the use of the updated FE model to generate training data and evaluate the accuracy of regression models with the possible exclusion of selected input features enabled by the structural specificity of a bridge. The data were sourced from the SHM system installed on a network arch bridge in Wolin, Poland. It confirmed the possibility of establishing the BWIM system based on strain measurements, characterized by a reduced number of sensors and a satisfactory level of accuracy in the estimation of loads, achieved by exploiting the network arch bridge structural specificity. Full article

Dynamic weighing systems, an advanced technology for traffic management, are designed to measure the weight of moving vehicles without obstructing traffic flow. These systems play a critical role in monitoring freight vehicle overloading, collecting weight-based tolls, and assessing the structural health of roads and bridges. However, due to the complex road traffic environment in real-world applications of dynamic weighing systems, some vehicles cannot be accurately weighed, even though precise parameter calibration was conducted prior to the system’s official use. The variation in driving behaviors among different drivers contributes to this issue. When different types and sizes of vehicles pass through the dynamic weighing area simultaneously, changes in the vehicles’ motion states are the main factors affecting weighing accuracy. This study proposes an improved SSD vehicle detection model to address the high sensitivity to vehicle occlusion and frequent vehicle ID changes in current multi-target tracking methods. The goal is to reduce detection omissions caused by vehicle occlusion. Additionally, to obtain more stable trajectory and speed data, a Gaussian Smoothing Interpolation (GSI) method is introduced into the DeepSORT algorithm. The fusion of dynamic weighing data is used to analyze the impact of changes in vehicle size and motion states on weighing accuracy, followed by compensation and experimental validation. A compensation strategy is implemented to address the impact of speed fluctuations on the weighing accuracy of vehicles approximately 12.5 m in length. This is completed to verify the feasibility of the compensation method proposed in this paper, which is based on vehicle information. A dataset containing vehicle length, width, height, and speed fluctuation information in the dynamic weighing area is constructed, followed by an analysis of the key factors influencing dynamic weighing accuracy. Finally, the improved dynamic weighing model for extracting vehicle motion state information is validated using a real dataset. The results demonstrate that the model can accurately detect vehicle targets in video footage and shows strong robustness under varying road illumination conditions. Full article

To address the technical limitations of traditional pavement performance prediction models in capturing temporal features and analyzing multi-factor coupling, this study proposes a Bayesian Optimization Dual-Layer Feature Fusion Model (BO-DLFF). The framework integrates heterogeneous data streams from embedded strain sensors, temperature/humidity monitoring nodes, and weigh-in-motion (WIM) systems, combined with pavement distress detection and historical maintenance records. A dual-stage feature selection mechanism (BP-MIV/RF-RFECV) is developed to identify 12 critical predictors from multi-modal sensor measurements, effectively resolving dimensional conflicts between static structural parameters and dynamic operational data. The model architecture adopts a dual-layer fusion design: the lower layer captures statistical patterns and temporal–spatial dependencies from asynchronous sensor time-series through Local Cascade Ensemble (LCE) ensemble learning and improved TCN-Transformer networks; the upper layer implements feature fusion using a Stacking framework with logistic regression as the meta-learner. BO is introduced to simultaneously optimize network hyperparameters and feature fusion coefficients. The experimental results demonstrate that the model achieves a prediction accuracy of R² = 0.9292 on an 8-year observation dataset, effectively revealing the non-linear mapping relationship between the Pavement Condition Index (PCI) and multi-source heterogeneous features. The framework demonstrates particular efficacy in correlating high-frequency strain gauge responses with long-term performance degradation, providing mechanistic insights into pavement deterioration processes. This methodology advances infrastructure monitoring through the intelligent synthesis of IoT-enabled sensing systems and empirical inspection data. Full article

In developing a digital twin of a real structure, finite element model updating (FEMU) is essential for refining the model’s response based on measured data, enabling the detection of structural damage or hidden reserves over time. This case study focused on a 40-year-old multi-span concrete roadway bridge, equipped with permanent bridge weigh-in-motion (B-WIM) and structural health monitoring (SHM) systems. Bridge responses from two calibration vehicles were used to derive strain influence lines (ILs) from mid-span B-WIM strain transducers mounted on the main girders. The error-domain model falsification (EDMF) methodology was applied to perform strain IL-based FEMU and the more conventional frequency-based, MAC-based, and combined frequency and MAC-based FEMU. Boundary conditions and three Young’s modulus adjustment factors, representing different groups of structural elements, were updated. The strain IL-based updated FE model, with averages of 35% and 50% stiffness increases for the two main girders, showed strong agreement with independently measured mid-span vertical displacements. Maximum values deviated not more than 5%. In contrast, the frequency and MAC-based updated FE model underestimated displacements by 25–30%. These findings highlight the potential of using B-WIM for FEMU and SHM on such types of bridges, particularly when the response under traffic load is of interest. Full article

The noticeable growth of road transport means that the protection of road infrastructure is becoming a critical issue. The main factor leading to the excessive degradation of roads are overloaded vehicles. The effective elimination of such vehicles from road traffic is possible through widespread usage of Weigh-In-Motion (WIM) systems for direct mass enforcement, thus eliminating the need for “manual” vehicle checks which are currently carried out by the appropriate services. WIM mass enforcement systems require strict metrological control, meaning that an initial verification, conducted at the moment when the system is installed, and subsequent periodic verifications are required. These operations aim to ensure that vehicle weighing error is consistently maintained within a permissible range of values. Fulfilment of this condition allows for the minimisation of the probability that a vehicle loaded within normative limits will be classified as overloaded. The long-term study of two WIM systems located on provincial road 975 in Wielka Wies, in southern Poland, equipped with load sensors made using different technologies (strain gauge sensors and quartz sensors) and in different weather conditions, has allowed us to formulate recommendations regarding the frequency with which subsequent verifications should be performed in order to ensure the reliability of the weighing results. This paper presents the results of these studies and conclusions formulated based on them; in this case, they showed a verification of the system can be performed every 8 months. The conclusions and recommendations that we have presented concern primarily those WIM stations which were the object of our study and caution should be exercised when generalising these to other cases. Its novelty results from several premises. For the first time, long-term studies of two WIM systems equipped with load sensors made with different technologies were carried out. Both systems were installed on the same surface, in the immediate vicinity of each other. They were installed on a standard road and were subjected to the constant impact of road traffic with identical parameters. Tests of both WIM systems were performed periodically, using the pre-weighed vehicles method, in different seasons, for a period of 15 months. During the tests, the same test vehicles drove through both WIM systems at the same speed. All of this resulted in the obtainment of a unique set of measurement data, the analysis of which allowed for the assessment and comparison of the proprieties of the load sensors made with both technologies. Full article

In modern agriculture, reducing the internal moisture content of crop seeds is essential to enhance the activity and mobility of seed oil molecules, thereby increasing oil yield while minimizing the risk of mold and deterioration. However, traditional drying methods often result in uneven heating, leading to seed scorching and diminished drying efficiency and economic returns. To address these limitations, this study proposes a novel thin-layer seed drying system incorporating a redesigned drying tray structure. Specifically, the system places the seed-bearing tray beneath a vibration module operating at a predetermined frequency. The vibration mechanism induces the uniform motion of the seeds, thereby preventing localized overheating (scalding) and enabling automatic weighing for the real-time monitoring of moisture reduction during the drying process. The advancement of wireless sensor technologies in agriculture has enabled the deployment of more refined, large-scale monitoring networks. In this work, a commercial chip-based piezoelectric vibration detection device was integrated into the experimental setup to collect time-domain response signals resulting from interactions among seeds, impurities, and the drying tray. These signals were used to construct a comprehensive database of seed collision signatures. To mitigate discontinuities in signal transmission caused by vibration and potential equipment failure, the shortest routing protocol (SRP) was implemented. Additionally, the system outage probability (OP) and a refined closed-form solution for signal transmission reliability were derived under a Rayleigh fading channel model. To validate the proposed method, a series of experiments were conducted to determine the optimal vibration frequencies for various seed types. The results demonstrated a reduction in seed scalding rate to 1.5%, a decrease in seed loss rate to 0.4%, and an increase in moisture monitoring accuracy to 97.0%. Compared to traditional drying approaches, the vibrating drying tray substantially reduced seed loss and effectively distinguished between seeds and impurities. Furthermore, the approach shows strong potential for broader applications in seed classification and moisture detection across different crop types. Full article

Establishing an accurate vehicle fatigue load spectrum is a critical prerequisite for fatigue life analysis and design of highway bridges. However, the time-varying and regional characteristics of vehicle loads pose significant challenges to achieving this goal. This study focuses on vehicle data collected by a weigh-in-motion system installed on a highway bridge in Chongqing, China. The statistical characteristics of vehicle-load-related parameters are analyzed, and the actual vehicle fatigue load spectrum for this section of the road is established. Specifically, vehicles are first categorized based on axle count characteristics. Then, statistical analyses are conducted on key parameters such as vehicle weight, headway time, and axle load for each vehicle type. Finally, the actual vehicle fatigue load spectrum is developed based on Miner’s linear damage rule and the equivalent fatigue damage principle, and the contributions of different vehicle types to fatigue damage are investigated. The results show that the weight distributions of different vehicle types follow a Gaussian mixture distribution, while the headway time distribution for each lane follows a log-normal distribution. A linear approximate relationship was observed between the axle loads of different vehicle types and their respective total weights. Although two-axle trucks exhibited higher frequencies, six-axle trucks contributed the most to structural fatigue damage, accounting for 53.81%. Therefore, six-axle trucks can be regarded as the standard fatigue vehicle model for this section of the road. These findings provide valuable insights for fatigue design and fatigue life assessment of highway bridges under similar vehicle loading conditions. Full article

The paper presents the general characteristics of weighing systems for vehicles in motion. A number of problems and constraints that accompany these systems to ensure adequate accuracy in the operation of these systems are pointed out. The efficient operation of WIM systems is also related to the proper preselection of vehicles for weighing in motion. The next part of the paper presents the basic classification and characteristics of machine learning algorithms, as well as examples of applications and implementations of these algorithms in various industries. The paper presents a model based on the XGBoost algorithm for estimating the weight of vehicles in motion, taking into account key characteristics of vehicles. The model was tested on large datasets from two locations in Poland, achieving high accuracy rates. The results indicate the model’s potential in optimizing preselection systems, allowing for the effective identification of overloaded vehicles. Future work will focus on testing the model at other locations to verify its scalability and operational efficiency. Full article

Overloaded trucks directly contribute to road infrastructure deterioration and undermine safety, posing significant challenges to sustainability. This makes enforcement to reduce their numbers and impacts essential. Weigh-in-motion (WIM) systems use road-embedded sensors to measure truck weights and enforce regulations. However, WIM cannot be installed on all routes, and some overloaded truck drivers can detour to avoid them instead of giving up overloading if the detour penalty is still lower than the extra profit from overloading. This paper focuses on optimal WIM location planning for overloaded truck management, incorporating a demand shift and user equilibrium model based on the utility functions of overloaded and non-overloaded trucks. The presented framework includes an upper-level problem for WIM placement and a lower-level problem for demand shifts and traffic assignments among overloaded trucks, non-overloaded trucks, and light-duty vehicles for a given WIM placement. Particularly, at the upper level, the primary objective is to minimize the traffic loadings, i.e., the expected equivalent single-axle load–kilometers per unit time, with the secondary objective of minimizing the total traffic disruptions over the target network. Simulations and sensitivity analyses are conducted through a numerical example. Consequently, this study proposes an optimal WIM placement framework that considers drivers’ utility-based route choice and social costs such as ESAL and traffic congestion. Full article

Weigh-in-motion systems can measure the number of axles to obtain a vehicle’s type and upper limit of weight, which, combined with the weight measured by the system, can be used for highway toll collection and overload management. This paper proposes a new modular system based on multi-sensor fusion and neural network axle recognition to address issues concerning the high failure rate of axle recognition devices and low weighing accuracy. We use a modular system consisting of multiple weighing platforms, enabling whole-vehicle-load weighing with multiple vehicles traveling through platforms. In addition, we propose a sequential generation model based on a Transformer and Gated Recurrent Unit to calculate the weighing signal generated by the weighing sensors, and then obtain the number of axles and the gross vehicle weight. Finally, the axle recognition algorithm and modular systems are tested in multiple scenarios. The accuracy of the axle recognition is 99.51% and 99.84% in the test set and the toll station, respectively. The weighing error of the modular system in the test field is less than 0.5%, and 99.18% of vehicles had an error of less than 5% at the toll station. The modular system has the advantages of high accuracy, consistent performance, and high traffic efficiency. Full article

This study presents a detailed analysis of the stability of weigh-in-motion sensors used at vehicle weighing stations. The objective of this research was a long-term assessment of reading variability, with a particular focus on the sensors’ application in automated measurement stations. These investigations constitute a critical component of modern traffic management systems and vehicle overload control. The analysis covered the period from 2022 to 2024, incorporating data from vehicles participating in regular traffic as well as dedicated control runs using vehicles with known wheel and axle load distributions. The study also considered changes in road surface conditions, particularly rut depth, and their variations over the examined period. The findings revealed that, despite the lack of station calibration over the three-year period, the observed parameters exhibited only minor changes. These results confirm the high stability of the applied measurement system and its ability to maintain measurement accuracy over extended operational periods, which is essential for its practical application in real-world traffic conditions. Full article

Weigh-in-motion (WIM) systems aim to estimate a vehicle’s weight by measuring static wheel loads as it passes at highway speed over roadway-embedded sensors. Vehicle oscillations and the resulting dynamic load components are critical factors affecting measurements and limiting accuracy. As of now, a satisfactory solution has yet to be found. This paper discusses a novel correction approach that fuses WIM sensor data with wheel oscillation captured by cameras. In an experiment, a hard plastic speed bump was placed ahead of a piezoelectric WIM sensor to induce oscillation in trucks crossing the WIM sensor. Three high-speed cameras captured the motion of the wheels. The results show that the proposed method improved the accuracy of the measured gross weight for significant wheel oscillations, while no improvement is observed for smaller oscillation amplitudes. Full article

Uncertainties in traffic flows pose significant challenges for the accurate fatigue safety assessment of bridge structures. Fatigue analysis requires detailed information on heavy vehicle-induced loads, which can be obtained from weigh-in-motion (WIM) systems. This paper develops a stochastic traffic load model based on site-specific WIM measurements to evaluate the fatigue reliability of steel bridges by enhancing simulation efficiency and incorporating correlations in traffic load parameters. Traffic loading is measured on site by WIM systems and used to develop a probabilistic model. A heavy traffic scenario load model is developed based on the Gaussian mixture model (GMM) and Poisson distribution. The correlation between traffic load parameters is addressed using the Nataf transformation. The fatigue reliability of critical components is evaluated using this procedure as an illustrative example. The results show that annual increases in traffic load significantly impact fatigue damage. This research provides a theoretical basis for improved traffic management and structural maintenance strategies. Full article

Overloaded trucks contribute heavily to road damage and increased maintenance costs, and Weigh-In-Motion (WIM) systems are an effective tool for detecting them without disrupting traffic flow. However, overloaded truck drivers often adjust their routes to avoid WIM stations, complicating enforcement efforts for road management stakeholders. To address these challenges, this study integrates the strategic behaviors of multiple stakeholders with diverse objectives into optimal WIM planning by modeling interactions among the government, pavement management agencies, and drivers. The authorities are responsible for WIM installation, while drivers minimize their respective travel costs. The proposed approach considers both road maintenance costs incurred by authorities and travel costs for drivers, based on a traffic assignment model for each WIM installation strategy. Basic concepts from game theory are adopted to formalize the dynamic interactions among these stakeholders. Full article

A Flapping-Wing Rotor (FWR) is a novel bio-inspired micro aerial vehicle configuration, featuring unique wing motions which combine active flapping and passive rotation for high lift production. Power efficiency in flight has recently emerged as a critical factor in FWR development. The current study investigates an elastic flapping mechanism to improve FWRs’ power efficiency by incorporating springs into the system. The elastic force counteracts the system inertia to accelerate or decelerate the wing motion, reducing the power demand and increasing efficiency. A dynamic model was developed to simulate the unique kinematics of the FWR’s wing motions and its elastic mechanism, considering the coupling of aerodynamic and inertial forces generated by the wings, along with the elastic and driven forces from the mechanism. The effects of the spring stiffness on the aerodynamic performance and power efficiency were investigated. The model was then verified through experimental testing. When a spring stiffness close to the mechanical system resonance was applied, the power efficiency of the test model increased by 16% compared to the baseline model without springs, generating an equivalent average lift. With an optimal elastic flapping mechanism for greater lift and lower power consumption, the FWR was fully constructed with onboard power and a control receiver weighing 27.79 g, successfully achieving vertical take-off flight. The current model produces ten times greater lift and has nearly double the wing area of the first 2.6 g flyable FWR prototype. Full article