Search Results (264)

The diagnosis of missing seedlings in high-density drill-seeded crops is often hindered by the strong coupling between visual perception and diagnostic rules, which leads to an irreversible cascade amplification of underlying missed detection errors. To address this dilemma, this paper proposes a “Perception–Topology” collaborative decoupling framework oriented toward row structure perception. In the perception phase, a row-structure-enhanced detection model (RS-YOLO) is constructed. It integrates Space-to-Depth (SPD) conversion, a Selective Frequency-domain Aggregation Module (SFAM), and a Row-Structure Attention Mechanism (RSM) to effectively suppress tire rut interference and explicitly reinforce the spatial topological priors of crops. In the diagnostic phase, an Adaptive Intra-row Gap Analysis (AIGA) algorithm is proposed. By utilizing a dynamic median intra-plant spacing scale and core canopy geometric pruning, this algorithm fundamentally reformulates missing seedling diagnosis into a physical interruption metric of one-dimensional graph connectivity. Evaluated on a finely reconstructed UAV-based sorghum imagery dataset, RS-YOLO achieved a significant improvement of 2.7% in precision and 3.2% in recall over the baseline model, providing a structure-aligned, high-confidence input for the diagnostic process. Based on this perceptual foundation, the AIGA algorithm ultimately achieved a diagnostic precision of 96.11% and a recall of 91.48% without the need for negative sample annotations. This framework effectively severs the propagation chain of perceptual errors, providing a noise-robust and highly physically interpretable new paradigm for the automated inspection of field population structures. Full article

In response to the bottleneck issue of natural rubber selection in aircraft tire formulation design, this study proposes a data-driven screening methodology that integrates a simulated performance database with grey system theory. A multidimensional performance simulation database was constructed, encompassing representative NR brands from six major global producing regions: Malaysia, Indonesia, Thailand, Vietnam, Hainan (China), and Yunnan (China). This repository encompasses critical metrics, including raw rubber constitution, molecular characteristics, and the static/dynamic mechanical behaviors of vulcanizates. Utilizing this foundation, a novel material selection protocol was formulated, grounded in a multi-objective weighted intelligent grey target decision framework. The Analytic Hierarchy Process (AHP) was applied to ascertain differentiated performance criteria and assign corresponding weights, specifically tailored to the functional necessities of distinct aircraft tire sections. To substantiate the model’s efficacy, the primary tire of the ubiquitous Boeing 737-800 served as a validation case. The optimal Natural Rubber (NR) grade identified by the algorithm was cross-referenced with the empirical expertise and engineering practices of premier global tire manufacturers, thereby confirming the framework’s robustness and predictive accuracy. Consequently, this investigation establishes a comprehensive intelligent decision-making architecture, spanning data construction to engineering deployment, offering a quantitative and referential pathway for NR material screening in aviation applications. Full article

To address the issue of vehicle instability and veering during braking when a single wheel fails in an electric vehicle’s electromechanical braking (EMB) system, an integrated application-oriented control framework based on adaptive sliding mode control (ASMC) is proposed. To address the shortcomings of SMC—such as difficulty in suppressing oscillations and the high workload associated with parameter tuning—a novel composite reaching law function was designed, and the TD3 algorithm was employed to optimize the sliding mode control parameters. When a failure in the EMB system is detected, the upper-layer control uses an improved ASMC algorithm to calculate the vehicle’s additional yaw moment. The lower-layer control employs an optimal control algorithm to distribute braking force, taking into account braking intensity, yaw moment, and tire utilization. This approach is integrated with sliding mode steering control to enhance vehicle stability during braking. To meet the driver’s braking requirements, a backpropagation (BP) neural network is first employed to identify braking intent. Based on this, the additional yaw moment is calculated by the upper-layer controller, and the brake force distribution is optimized through the lower-layer controller, thereby improving the vehicle’s stability. Through co-simulation analysis using Simulink-2024a and CarSim-2019.1, the results show that, compared to traditional algorithms, the proposed hierarchical control strategy reduced the maximum sideslip angle by 51.4%, decreased the maximum yaw rate by 47.2%, and reduced the maximum lateral offset by 45.6%. This control strategy enables enhanced stability across various braking intensity conditions. Full article

To achieve the resource utilization of waste tires and improve the mechanical performance of cemented tailings backfill, rubber–cemented tailings backfill (R-CTB) specimens were prepared with four rubber particle sizes (20-, 40-, 60-, and 80-mesh) and four contents (2%, 4%, 6%, and 8%). A 0% rubber control group was introduced to address the lack of quantitative comparison. Uniaxial compression, digital image correlation (DIC), and scanning electron microscopy (SEM) were used to study mechanical behavior, energy evolution, and microstructural characteristics at 7 and 28 days. Results indicate that strength and elastic modulus first increase then decrease with particle size and decrease with content rise. Compared with the control group, R-CTB shows lower strength but significantly higher ductility and energy dissipation. Finer particles cause strain localization; higher content and finer size increase pores and weaken interfaces. Rubber incorporation transforms failure from brittle to ductile, providing a basis for engineering application. Full article

The synergistic utilization of waste plastics and tires in asphalt modification is a highly promising sustainable strategy. However, the differential impacts of distinct plastic molecular architectures on the performance and network evolution of rubber-modified asphalt remain fundamentally unclear. This study systematically investigated the physical, rheological, and microstructural properties of composite asphalts modified with desulfurized rubber powder (DRP) and four representative plastics: polyethylene (PE), styrene–isoprene–styrene (SIS), styrene–ethylene–butylene–styrene (SEBS), and styrene–butadiene–styrene (SBS). Furthermore, the pavement performance of the asphalt mixtures prepared via dry and wet methods was comparatively evaluated. Microstructural and spectroscopic analyses revealed that the composite modification was primarily governed by physical blending and swelling. The non-polar, semi-crystalline PE resulted in severe phase separation and extreme low-temperature brittleness. Conversely, the saturated hydrogenated mid-blocks of SEBS endowed the asphalt with the highest high-temperature rutting resistance but severely compromised its low-temperature stress relaxation. Remarkably, SBS interacted synergistically with DRP to form a highly homogeneous and densely interwoven three-dimensional network, thereby achieving an optimal viscoelastic balance, outstanding storage stability, and superior low-temperature ductility. Pavement performance tests further demonstrated that the wet method significantly outperformed the dry method for block copolymers by facilitating sufficient pre-swelling. Overall, the SBS-DRP composite-modified asphalt prepared via the wet method exhibited the most exceptional and balanced comprehensive pavement performance, providing a robust theoretical foundation for the sustainable and high-value recycling of multi-source solid wastes in paving engineering. Full article

This work deals with a practical method of using crumb rubber resulting from waste tires to produce modified bitumen via a wet mixing method for road construction in Iraq. Due to wide variation in temperatures and over-loading traffic in Iraq, rutting deformation is the most observed structural pavement problem. Also, tire wear and tear are higher in Iraq than in other countries due to high temperature and dry weather most of the year, which makes considerable amounts of waste tire piles easily accessible. Utilizing this waste material could be crucial to the environment and economy of the country, as well as to the sustainability of resources. Using waste tire materials as bitumen modifiers in the production of hot mix asphalt is a widely practiced experiment, although it is applied differently depending on the weather, type of bitumen used, and its availability. In the methodology of this research, it is suggested to modify asphalt grades 60/70 by a certain amount of crumb rubber (5–20%). The modified asphalt and asphalt grade 40/50 were used in preparing two types of asphalt concretes to examine their volumetric properties and evaluate their rutting behavior. The results for both mixtures were compared to the Iraqi General Specifications for Roads and Bridges (SORB/R9). The findings showed significant improvements in Marshall stability and flow, as well as in the percentages of voids satisfied in the modified mixture. After using rubberized asphalt in the mixture, the rutting depth was recorded below 20 mm and decreased by 30% and 26% at temperatures of 40 °C and 60 °C, respectively, compared to the controlled mixture. Full article

Crumb rubber-modified asphalt concrete (CMAC) has gained increasing attention as a sustainable pavement material capable of improving mechanical performance while utilizing waste tire resources. This study investigates the rutting resistance and fatigue behavior of CMAC using a combined experimental and mechanistic–empirical modeling approach. Asphalt mixtures containing 0–25% crumb rubber by binder weight were prepared and evaluated through Marshall stability and indirect tensile fatigue tests, whereas Fourier-transform infrared spectroscopy (FTIR) was used to examine binder–rubber interactions. The results indicate that crumb rubber significantly influences both the volumetric and mechanical properties of asphalt mixtures. Mixtures containing 10–15% crumb rubber exhibited optimal performances, achieving up to 36% higher Marshall stability and improved fatigue life compared with conventional asphalt mixtures. FTIR analysis revealed that rubber particle swelling and limited chemical interactions enhanced binder elasticity and improved binder–aggregate compatibility. However, excessive rubber content (≥20%) resulted in reduced stability owing to increased binder absorption and decreased effective binder film thickness. A mechanistic–empirical model incorporating viscoelastic, viscoplastic, and fatigue damage parameters successfully reproduced the experimental trends and identified the same optimal rubber content range. The findings demonstrate that CMAC with a moderate rubber content can enhance pavement durability and structural performance while promoting environmentally sustainable road construction through the reuse of waste tires. Full article

Against the backdrop of the global search for alternatives to fossil fuels, waste tires have attracted attention as a significant resource due to their enormous production volume and considerable energy potential. However, the application of tar derived from waste tires alone is limited by its poor stability and other deficiencies. This study systematically investigates the co-pyrolysis behavior and synergistic mechanisms of waste tires and beech sawdust at various blending ratios. Thermogravimetric analysis indicates that the addition of beech sawdust reduces the decomposition temperature of the blend and induces a synergistic effect that promotes waste tire pyrolysis within the temperature range of 384–440 °C. Pyrolysis experiments results show that tar yield of the blends reached 64.45 wt.%, while the char yield decreased from 40.67 wt.% to 24.83 wt.%. Also, the presence of beech sawdust synergistically enhanced the formation of aromatic hydrocarbons in the tar of waste tires, with the total yield of aromatics increasing synergistically by up to 54.8%. Specifically, the yields of stable alkylbenzenes such as toluene and xylene were consistently promoted, whereas the yields of unsaturated aromatics such as allylbenzene and 2,4-dimethylstyrene were enhanced at low beech sawdust ratios but suppressed at higher ratios. Based on these findings, the interaction mechanisms underlying the co-pyrolysis process were elucidated, providing theoretical guidance for the high-value utilization of waste tires. Full article

Contemporary vehicle development, particularly for overactuated platforms, demands design methodologies that bridge the gap between high-level performance targets and hardware selection. Existing physics-based models, while essential, offer limited utility for this systems-level design task. This paper introduces a novel analytical framework for vehicle lateral dynamics, predicated on a reformulated single-track model that integrates the concept of tire-specific slip. The derived specific slip-based bicycle model enables a comprehensive frequency-domain analysis of handling characteristics, articulated through three fundamental metrics: the front and rear axle specific slips and the vehicle wheelbase. Our results quantify the influence of these parameters on key handling attributes, including stability, responsiveness, and roll susceptibility. This work provides a constitutive tool for the model-based design of next-generation vehicles, enabling the a priori selection and optimization of chassis hardware to meet predefined performance objectives and informing the synthesis of advanced motion control systems. Full article

Electric vehicles (EVs) have emerged as a vital component of sustainable urban mobility. In this life cycle assessment, the GREET model (Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies) is used to compare three EV scenarios for Washington, DC, the capital of the United States. We compare these three scenarios to a 2022 baseline scenario that describes the current state of EV utilization in Washington, DC. The three future scenarios we examine are based on policy assumptions that differ in the extent to which they integrate renewable energy into the EV future of Washington, DC. Our findings suggest a significant decrease in greenhouse gases between 52 and 66 percent by 2050 and a similar decline in other air-pollutants associated with all three future scenarios. This confirms the advantages of EVs for urban air quality. However, two important aspects of the analysis suggest that there is (1) the threat of emissions leakage associated with electricity imports into DC, which complicates the overall assessment of local environmental benefits; and (2) an increase in non-exhaust emissions of particulate matter attributable to tire and brake wear. These emissions cannot be removed through electrification and tend to increase due to the increased weight of EVs. Our analysis shows that the full capabilities of electric vehicles can best be realized through grid decarbonatization. Achieving genuine sustainable mobility therefore requires complementary strategies that address transboundary emissions and vehicle-specific non-exhaust particulates. Full article

Single-wheel brake failure in electromechanical brake (EMB) systems breaks the left-right symmetry of wheel forces and yaw moments, creating a critical conflict between emergency braking effectiveness and lateral stability. To address this symmetry-breaking condition, this paper proposes a bimodal, adaptive, coordinated fault-tolerant control strategy that integrates dynamic brake torque redistribution with active front steering (AFS). A novel dynamic interaction model linking deceleration demand with tire adhesion utilization enables real-time assessment and optimization of the balance between longitudinal braking performance and yaw stability. Braking forces are allocated based on adhesion utilization through a layered two-mode strategy—balanced distribution prioritizing lateral stability and compensatory distribution engaging the healthy front wheel when rear axle capacity is exceeded. An integral sliding-mode controller computes the additional yaw moment needed to suppress yaw-rate deviation, with rigorous Lyapunov stability analysis confirming closed-loop stability. AFS is triggered only when yaw-rate deviation exceeds 0.05 rad/s or adhesion utilization reaches 90%, incorporating hysteresis to ensure smooth transitions and minimize unnecessary steering intervention. Comprehensive co-simulations using Carsim and MATLAB/Simulink under diverse failure locations (left-front and right-rear wheels), road adhesion levels ($\mu$ = 0.85 and 0.5), and braking intensities (0.2 g–0.6 g) demonstrate that the proposed strategy reduces lateral displacement by up to 85.3% compared to full-time AFS control while maintaining over 99% deceleration satisfaction. The results establish an effective dual-objective fault-tolerant framework that enhances both robustness and functional safety of EMB systems under symmetry-breaking faults, offering a physically interpretable, computationally efficient solution well-suited for real-time automotive applications. Full article

Ensuring the driving safety of hazardous chemical vehicles is a critical priority. High temperatures in tires and tanks can lead to catastrophic accidents, including fires and road damage, particularly in bridge and tunnel sections. Therefore, the purpose of this study is to utilize deep learning to obtain the temperature of vehicle tires and tanks in real time. We constructed a comprehensive dataset by combining the FLIR infrared vehicle dataset, the SPT visible tire dataset, and self-collected thermal video frames captured in various environments. State-of-the-art object detection models, including different scales of YOLOv8, YOLOv9, and YOLOv10, were evaluated for the multi-target detection of vehicles, tires, and tanks. Comparative analysis reveals that the YOLOv8-L model optimized with the GIoU loss function delivers the best performance. Specifically, it achieves a mean Average Precision (mAP) of 97.9% with an average inference time of 6.9 ms per frame, effectively balancing accuracy and real-time efficiency. Finally, by mapping the detection bounding boxes to the radiometric temperature matrix, the system achieves precise, real-time temperature monitoring of the vehicle components. Full article

Utilizing waste tire crumb rubber to modify asphalt enhances the damping and noise reduction performance of pavements. This study systematically evaluated the damping performance of crumb rubber–modified asphalt over a wide temperature range. A high-temperature damping index based on the loss factor and a low-temperature energy dissipation ratio derived from the Burgers model were proposed for quantitative characterization. The results show that damping performance is primarily controlled by temperature and crumb rubber content, while particle size plays a secondary role. Increasing crumb rubber content markedly improves damping performance. When the crumb rubber content exceeds 20%, the damping temperature stability, peak loss factor, and its retention tend to level off, whereas the low-temperature enhancement diminishes when the content exceeds 25%. Accordingly, the robust combinations are 80-mesh (≈180 μm) with 20% content for high-temperature conditions and 80-mesh with 25% content for low-temperature conditions. Multivariate nonlinear regression models achieved high predictive accuracy (R² = 0.927 and 0.985). Microscopic analyses indicate that crumb rubber increases constrained interfacial phases and system viscosity, and partial particle exposure at 20–25% further enhances interfacial friction and energy dissipation, consistent with the observed macroscopic damping behavior. These findings provide a theoretical basis for robust, noise-reducing pavements. Full article

Autonomous trucks can be used in different loading combinations, including different axle configurations, tire types, and lateral wander mode scenarios. In this research, four different truck types have been selected with varying gross weights and axle configurations. The four different truck types include a 5-axle long-haul semi-truck, a 6-axle electric autonomous truck, a 6-axle autonomous truck platoon leader, and a 5-axle autonomous truck platoon follower. Furthermore, three different tire footprint scenarios, consisting of a conventional dual wheel assembly, a wide base tire, and a new generation wide base tire, have been used. In order to utilize the possibility of lateral wander programmed into the autonomous trucks, three different lateral wander models, including zero lateral wander, a human-driven probabilistic lateral wander, and an optimum uniform wander mode, have been used. Finite element analysis has been employed to incorporate the effects of various scenarios on a conventional pavement section. Results showed improved pavement life with the use of uniform wander mode, where trucks T1 and T2 improved the pavement life by 47% and 56%, respectively, when compared to truck T3. Furthermore, the use of uniform wander mode decreases rutting and fatigue damage by 36% and 28%, respectively, on average for all scenarios. The use of new generation wide-base tires is recommended, since it reduces damaging strains by 38% when compared to the dual tire configuration. Full article

Accurate estimation of vehicle sideslip angle is vital for the stability and safety of four-wheel independent drive electric vehicles (4WIDEVs), but it faces challenges, including model uncertainties caused by tire yaw stiffness variations and system delays. This paper proposes a novel adaptive fusion strategy that combines the dynamic robust observer (DRO) and the improved adaptive square-root unscented Kalman filter (ASUKF). The DRO is designed based on a two-degrees-of-freedom vehicle model and ensures stability through linear matrix inequalities (LMIs), effectively handling parameter uncertainties and time delays; the ASUKF utilizes a three-degrees-of-freedom model and the magic formula tire model, combined with Sage–Husa adaptive filtering, to address the nonlinear tire dynamics. The key innovation of this paper is the introduction of a fuzzy-rule-based adaptive weighting mechanism that dynamically adjusts the fusion weights of the DRO and ASUKF in real time, thereby exploiting their complementary advantages under uncertainty and nonlinear conditions. The simulation and experimental validations demonstrate that this method significantly improves estimation accuracy, reducing the estimation error of vehicle sideslip angle by an average of 9.36%, and maintains robust performance and dynamic adaptability in various conditions, providing a reliable solution for the real-time state estimation of intelligent electric vehicles. Full article