Search Results (149)

In the context of pavement management systems (PMSs), overloaded trucks impose severe economic and environmental burdens by accelerating pavement deterioration and increasing greenhouse gas (GHG) emissions. Existing research on Weigh-in-Motion (WIM) placement has rarely incorporated environmental impacts, particularly greenhouse gas (GHG) emissions, into the decision-making process. Instead, most studies have focused on infrastructure damage and have paid limited attention to how enforcement interacts with driver evasion behavior and schedule-related constraints. To address this gap, this study develops a bi-level optimization framework that simultaneously minimizes PMS costs, travel costs, and environmental (GHG) costs. The upper-level problem represents the total social cost minimization, while the lower-level problem models drivers’ routes and demand shift. The framework endogenously captures utility-based demand shifts, allowing overloaded drivers to switch to legal operations when enforcement and schedule-related constraints outweigh overloading benefits. A numerical study using the Sioux Falls network demonstrates that dual WIM installations significantly outperform single configurations, achieving network-wide cost reductions of up to 1.5% compared to 0.4%. Notably, PMS costs for overloaded trucks decreased by nearly 60%, confirming the effectiveness of strategic enforcement. Ultimately, this study contributes a unified decision-support tool that reframes WIM enforcement from a passive control measure into a proactive strategy for sustainable freight management. Full article

Despite continued efforts by the Korean government to improve road safety, truck-related accidents remain disproportionately fatal, with a rate approximately 2.6 times higher than that of passenger vehicles. Although legal regulations prohibit overloading and improper loading, existing enforcement practices—primarily dependent on low-speed weigh-in-motion (WIM) systems—are limited in coverage and responsiveness. This study develops and validates standardized test procedures for detecting overloading and improper loading in commercial freight vehicles using a high-speed weigh-in-motion (HS-WIM) system. The HS-WIM system offers advanced sensing capabilities, including vehicle speed, length, axle configuration, and weight measurement at highway speeds. However, Korean HS-WIM performance standards currently lack detailed guidance, especially concerning group axle load testing and asymmetric cargo detection. To address these regulatory and technical gaps, a comprehensive set of test scenarios was designed based on domestic and international standards. A dedicated testbed was constructed, and 12 commercial vehicle types were tested under varied speeds and loading conditions. The proposed procedures reliably detect violations, and the study introduces evaluation criteria that improve HS-WIM system accuracy and support future enforcement and policy development in Korea. Full article

This paper develops a theoretical framework and a numerical implementation for real-time estimation of the gross mass of heavy vehicles using only on-board signals: tire inflation pressure from the TPMS and radial deformation inferred from a monocular chassis camera. Each wheel is modeled as a single-degree-of-freedom radial oscillator with pressure-dependent stiffness $k_r\left(P\right)$ and damping $c_r\left(P\right)$. The contact patch geometry follows a compressed-arc approximation that maps radial deformation $\delta$ to contact length $L\left(\delta \right)$ and area $S\left(\delta \right)$. Two independent force surrogates are constructed—$F_k=k_r\left(P\right)\delta$ and $F_q=q\left(P\right)S\left(\delta \right)$, where $q\left(P\right)$ denotes the mean contact pressure—and fused by an adaptive Kalman filter operating at 30 Hz to recover per-wheel loads and total mass. Tuning the fusion weight $\lambda$ yields a relative mass estimation error below 5% across $0.001\le \delta \le 0.20$ m, and the maximum observed error is 4.99%. Numerical experiments using fixed-step RK4 and embedded RK45 methods confirm the accuracy and real-time feasibility on commodity hardware (runtime <33 ms per step). Uncertainty analysis based on Latin hypercube sampling, the PRCC, and Sobol indices shows robustness to parameter perturbations ($\pm 5\%$ inflation, $\pm 10\%$ stiffness, $\pm 15\%$ damping, $\pm 1^{°}$ camera pitch, $\pm 2$ kPa TPMS bias). Observability analysis supports identifiability under the tested regimes. The estimator delivers wheel and axle loads for on-board alerts, telematics, V2X pre-screening for road user charging and weigh-in-motion technology, and friction-aware control. Full article

A six-year-old, neutered male Pomeranian weighing 4.25 kg was presented with a two-year history of non-weight-bearing lameness of the left thoracic limb following an untreated traumatic olecranon fracture. Orthopedic examination revealed markedly reduced elbow joint range of motion and muscle atrophy. Radiographs demonstrated a distinct fracture line with proximolateral displacement of the olecranon fragment. Preoperative computed tomography (CT) and three-dimensional (3D) reconstruction were used to establish the surgical plan and to pre-contour a locking plate. Surgical treatment was performed in sequential steps, including removal of scar tissue, reopening of the bone marrow channel, and internal fixation. Considering the compromised biological environment of a chronic non-union, a bioactive graft composed of porous leaf-stacked structure (LSS) polycaprolactone particles incorporating recombinant human bone morphogenetic protein-2 (rhBMP-2) and mesenchymal stem cells (MSCs) was applied in combination with plate-screw fixation. The patient showed progressive improvement after surgery, achieving full weight-bearing and restoration of elbow joint motion comparable to the contralateral side. Follow-up radiographs and CT confirmed fracture union, and the radiolucency of the LSS scaffold enabled precise monitoring of bone healing. This case highlights the potential utility of combining patient-specific surgical planning with sustained delivery of rhBMP-2 and MSCs using LSS particles for the management of chronic non-union fractures in small animals. Full article

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

Considering that soft airbag sensors made from soft materials are limited to detecting only normal forces, a novel PEBA–silicone composite magneto-sensitive airbag sensor is proposed for simultaneously detecting normal contact force and horizontal motion during human–robot interaction. In terms of structural design, the PEBA–silicone composite airbag is manufactured using fused deposition modeling, 3D printing, and silicone casting, achieving a balance between high airtightness and adjustable stiffness. Beneath the airbag, a magneto-sensitive substrate with several NdFeB magnets is embedded, while a fixed Hall sensor detects spatially varying magnetic fields to determine horizontal displacements without contact. The results of contact-force and motion experiments show that the proposed sensor achieves a force resolution of 20 g, a force range of 0 to 1100 g, a fitting sensitivity of 7.54 N/Pa, an average static stiffness of 4.82 N/mm, and a horizontal motion detection range of 0.125 to 1 cm/s. In addition, the prototype of the sensor is lightweight (with the complete assembly weighing 81.25 g and the sensing part weighing 56.13 g) and low-cost, giving it potential application value in exoskeletons and industrial grippers. Full article

With the widespread implementation of bridge structural health monitoring (SHM) systems, monitored bridge networks have gradually formed. Understanding vehicle loads and considering spatio-temporal correlations within bridge networks is critical for structural condition assessment and maintenance decision making. This study aims to predict traffic flows by investigating traffic flow correlations within a bridge network using multi-bridge data, thereby supporting bridge network-level SHM. A transformer-based traffic flow prediction model considering spatio-temporal correlations of bridge networks (ST-TransNet) is proposed. It integrates external factors (processed via fully connected networks) and multi-period traffic flows of input bridges (captured by self-attention encoders) to generate traffic flow predictions through a self-attention decoder. Validated using weigh-in-motion data from an 8-bridge network, the proposed ST-TransNet reduces prediction root mean square error (RMSE) to 12.76 vehicles/10 min, outperforming a series of baselines—SVR, CNN, BiLSTM, CNN&BiLSTM, ST-ResNet, transformer, and STGCN—with significant relative reductions of 40.5%, 36.9%, 36.6%, 37.3%, 35.6%, 31.1%, and 22.8%, respectively. Ablation studies confirm the contribution of each component of the external factors and multi-period traffic flows, particularly the recent traffic flow data. The proposed ST-TransNet effectively captures underlying the spatio-temporal correlations of traffic flow within bridge networks, offering valuable insights for enhancing bridge assessment and maintenance. Full article

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

In structural and bridge engineering, the axle weights and interaxle spacings of heavy trucks are useful for assessing the capacity of existing bridges, developing live load models, and other issues. Weigh-in-motion data have become the most common source for recording axle weights and interaxle spacings; however, information is not as direct and may not be as precise as that from static surveys. Surveying vehicles by stopping them beside the highway is not common nowadays; nevertheless, surveys provide very reliable information on truck axle weights and interaxle spacing. In this study, data from three surveys on two Mexican highways recorded in 2016 and 2018 are provided. The data contain the gross vehicular weights, axle weights, and interaxle spacings of heavy trucks. The discussion is given as to how the provided information can be useful for the bridge and transportation engineering community and for reliability and code calibration tasks for Mexican bridges and a future design code for bridges in Mexico City. It is concluded that statistical values are consistent with WIM data, with differences due to different methods used, recording time, samples size and others, and that trucks heavier than the legal weight circulate in Mexican highways; static surveys are useful to strongly support this important issue. Further research to compare samples from different surveying techniques, as well as the use of the information to investigate load effects on bridges, is recommended. Full article

This study presents a novel, bioinspired perching mechanism designed to enhance the landing and takeoff capabilities of flapping wing micro aerial vehicles (FWMAVs). Drawing inspiration from the human hand, the lightweight gripper integrates a compliant claw structure actuated by shape memory alloys (SMAs) that mimic muscle movement. These SMA springs act as compact, lightweight substitutes for traditional actuators like motors or solenoids. The mechanism operates via short electrical impulses that trigger both opening and closing motions. A detailed design process was undertaken to optimize phalange lengths for cylindrical grasping and to select appropriate SMAs for reliable performance. Weighing only 50 g, the gripper leverages the high power-to-weight ratio and flexibility of SMAs, with the springs directly embedded within the phalanges to reduce size and mass while preserving high-force output. Experimental results demonstrate fast actuation and a grasping force of approximately 16 N, enabling the gripper to hold objects of varying shapes and sizes and perform perching, grasping, and carrying tasks. Compared to existing solutions, this mechanism offers a simpler, highly integrated structure with enhanced miniaturization and adaptability, making it especially suitable for low-payload MAV platforms like FWMAVs. 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

Fatigue is one of the main sources of mechanical failure in steel bridges. However, a few studies have investigated the relationship between the longitudinal shape of bridge girders and long-term fatigue effects. This paper shows how different girder profiles affect the probability of fatigue damage occurring in continuous I-multigirder steel bridges. The analysis was conducted using realistic traffic scenarios defined through truck data collected in USA and Canada. Monte Carlo simulations with 5000 realizations were performed on several continuous bridge configurations with different span lengths and different girder profiles. The results of the analysis showed that the probability of fatigue damage is affected by profile shape and the smoothness of the transition between the maximum and minimum height of the cross section. In particular, the probability of fatigue damage on continuous I-multigirder steel bridges can be reduced by up to 26% for typical fatigue construction details over a bridge service life of 75 years by modifying the geometry of the girders during the design phase of the bridge. Full article