Search Results (67)

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

In this paper, the evaluation of the mechanical performance of novel, designed topologically optimized shin pads with advanced materials will be conducted with the aid of Finite Element Analysis (FEA) to assess the endurance of the final structure on impact phenomena extracted from actual real-life data acquired from contact sports. The main focus of the developed prototype is to have high-enough energy absorption capabilities and vibration isolation properties, crucial for the development of trustworthy protective equipment. The insertion of advanced materials with controlled weight fractions and lattice geometries aims to strategically improve those properties and provide tailored characteristics similar to the actual human skeleton. The final design is expected to be used as standalone protective equipment for athletes or as a protective shield for the development of human lower limb prosthetics. In this context, computational investigation of the dynamic mechanical response was conducted by replicating a real-life phenomenon of the impact during a contact sport in a median condition of a stud kick impact and an extreme case scenario to assess the dynamic response under shock-absorption conditions and the final design’s structural integrity by taking into consideration the injury prevention capabilities. The results demonstrate that the proposed lattice geometries positively influence the injury prevention capabilities by converting a severe injury to light one, especially in the gyroid structure where the prototype presented a unified pattern of stress distribution and a higher reduction in the transmitted force. The incorporation of the PA-12 matrix reinforced with the reused ground tire rubber results in a structure with high enough overall strength and crucial modifications on the absorption and damping capabilities vital for the integrity under dynamic conditions. Full article

Vehicle dynamics play a crucial role in assessing vehicle performance, comfort, and safety. To precisely depict the dynamic behaviors of a vehicle, fractional damping is employed to substitute the conventional damping in suspensions and tires. Taking the fractional damping into account, a four-degrees-of-freedom vehicle model is developed, which encompasses the vertical vibration and pitch motion of the vehicle body, as well as the vertical motions of the front and rear axles. The vibration equations are solved in the Laplace domain using the transfer function method. The validity of the transfer function method is verified through comparison with a benchmark case. The vibrations of the vehicle are analyzed under the effects of suspension and tire properties, pavement excitation, and vehicle speed. The assessment methods employed include the time-domain vibration response, amplitude–frequency curves, phase diagrams, the frequency response function matrix, and weighted root mean square acceleration. The results show that the larger fractional order results in higher energy dissipation. Elevated values of the fractional order α, suspension stiffness, and the damping coefficient contribute to greater stable vibration amplitudes in vehicles and a consequent degradation in ride comfort. Higher tire stiffness reduces vehicle vibration amplitude, while the fractional order β and tire damping have a negligible effect. Moreover, increased vehicle speed and a greater pavement input amplitude adversely affect ride comfort. Full article

To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article

This study investigates the rheological and chemical effects of waste tire pyrolytic oil (TPO) and its encapsulation (POC) as rejuvenators for asphalt binders. Driven by the need for sustainable and effective strategies to Recycle Reclaimed Asphalt Pavement (RAP), we investigated the use of TPO in two forms: as a liquid additive and as polymer capsules. The capsules, made in a 1:5 mass ratio (one part polymer, five parts TPO), were assessed through two methods: rheological tests (dynamic modulus and phase angles) and chemical composition analysis (carbonyl and sulfoxide indices). The binders underwent three aging levels: unaged, primary aging (RTFO), and secondary aging (PAV). Five liquid TPO dosages (1%, 2%, 4%, 6%, 9% by weight) and three encapsulated TPO dosages (6%, 9%, 12% by weight) were tested. Results show that TPO reduces stiffness, increases viscous response, and lowers aging indices, with higher dosages enhancing the effect. Quantitatively, 9% liquid TPO restores PAV-aged binder to near-unaged conditions, suitable for RAP recycling, while 4% release from POCs achieves rejuvenation comparable to RTFO-aged binders, enabling self-healing applications. The estimated release of TPO from POCs during mixing was 20–40%, ensuring a gradual softening effect. These findings highlight the potential of TPO and POC in enhancing asphalt durability and recycling. Full article

Tire deformation monitoring is a critical requirement for improving vehicle safety, performance, and intelligent transportation systems. However, most existing flexible strain sensors either lack directional sensitivity or have not been validated in real-world driving environments, limiting their practical application in smart tires. In this work, we report the fabrication of a flexible piezoresistive strain sensor based on a porous laser-induced graphene (LIG) network embedded in an Ecoflex elastomer matrix, with integrated directional force recognition. The LIG–Ecoflex sensor exhibits a high gauge factor of 9.7, fast response and recovery times, and stable performance over 10,000 cycles. More importantly, the anisotropic structure of the LIG enables accurate multi-directional stress recognition when combined with a convolutional neural network (CNN), achieving an overall classification accuracy exceeding 98%. To further validate real-world applicability, the sensor was mounted inside passenger car tires and tested under different loads and speeds. The results demonstrate reliable monitoring of tire deformation with clear correlations to load and velocity, confirming robustness under dynamic driving conditions. This study provides a new pathway for the integration of direction-aware, high-performance strain sensors into intelligent tire systems, with broader potential for wearable electronics, vehicle health monitoring, and next-generation Internet of Vehicles applications. Full article

In recent years, novel Personal Mobility Vehicles (PMVs) with a narrow width and an inward tilting mechanism, similar to motorcycles (MCs), have been proposed to prevent overturning during turns. Due to their compact size, these vehicles have inherent limitations in collision safety, making their dynamic safety and accident avoidance capabilities particularly crucial. In this study, a comparative analysis was conducted using a simulated single lane change course to evaluate obstacle avoidance performance. The results reveal that PMVs equipped with an active inward tilting mechanism exhibit superior obstacle avoidance capabilities. Based on the roll moment equilibrium conditions of these vehicles, an investigation of vehicle states during avoidance maneuvers revealed that both actual and virtual tilt angles coexist in PMVs, and their combined equivalent tilt angle effectively balances the roll moment during turning. This unique mechanism, which integrates the responsiveness of passenger cars with motorcycle-like tire lateral force characteristics, underpins the exceptional obstacle avoidance capabilities of actively inward tilting PMVs. Full article

Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices $K$ is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix $K$ is selected from the control matrix set, and $K$ is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced. Full article

This study resolves a critical challenge in electromechanical brake system validation: conventional ABS/RBS integrated platforms’ inability to dynamically simulate tire-road adhesion characteristics during braking. We propose a fuzzy adaptive PID-controlled magnetic powder clutch (MPC) system that achieves ground braking force simulation synchronized with slip ratio variations. The innovation encompasses: (1) Dynamic torque calculation model incorporating the curve characteristics of longitudinal friction coefficient ($\phi$) versus slip ratio ($s$), (2) Nonlinear compensation through fuzzy self-tuning PID control, and (3) Multi-scenario validation platform. Experimental validation confirms superior tracking performance across multiple scenarios: (1) Determination coefficients R² of 0.942 (asphalt), 0.926 (sand), and 0.918 (snow) for uniform surfaces, (2) R² = 0.912/0.908 for asphalt-snow/snow-asphalt transitions, demonstrating effective adhesion characteristic simulation. The proposed control strategy achieves remarkable precision improvements, reducing integral time absolute error (ITAE) by 8.3–52.8% compared to conventional methods. Particularly noteworthy is the substantial ITAE reduction in snow conditions (236.47 vs. 500.969), validating enhanced simulation fidelity under extreme road surfaces. The system demonstrates consistently rapid response times. These improvements allow for highly accurate replication of dynamic slip ratio variations, establishing a refined laboratory-grade solution for EV regenerative braking coordination validation that greatly enhances strategy optimization efficiency. Full article

The production of ordinary Portland cement has had a significant environmental impact, leading to increased interest in sustainable alternatives. This comprehensive review thus explores the performance and applications of rubberized alkali-activated concrete (RuAAC), an innovative material combining alkali-activated concrete with crumb rubber (CR) from waste tires as a coarse/fine aggregate replacement. The study examined current research on the components, physical and mechanical properties, and seismic performance of RuAAC structures. Key findings revealed that CR addition enhances dynamic characteristics while reducing compressive strength by up to 63% at 50% CR replacement, though ductility improvements partially offset this reduction. Novel CR pretreatment methods, such as eggshell catalyzation, can enhance seismic resilience potential. While studies on the structural seismic performance of RuAAC are limited, relevant research on rubberized conventional concrete indicated several potential benefits, highlighting a critical gap in the current body of knowledge. Research on the behavior of RuAAC in full-scale structural elements and under seismic loading conditions remains notably lacking. By examining existing research and identifying crucial research gaps, this review provides a foundation for future investigations into the structural behavior and seismic response of RuAAC, potentially paving the way for its practical implementation in earthquake-resistant and sustainable construction. Full article

Physics-assisted machine learning is a powerful framework that enhances data efficiency by integrating the strengths of conventional machine learning with physical knowledge. This paper applies this concept and focuses on the design of a driver evaluator using physics-assisted unsupervised learning, which serves as a virtual reference generator that provides different driving modes for vehicles equipped with active actuators. A strategy that applies sensitivity analysis regarding the vehicle handling performance, aiming to reduce the computational workload of the clustering algorithms, is proposed. First, a bicycle model with nonlinear Pacejka’s tire models is established for the analysis of lateral dynamics. Next, mathematical interpretations of sensitivity analysis are derived to evaluate the contribution of physical parameters to the system response and build the reduced parameters set. Then, Gaussian mixture models are fitted to a database generated with the full parameters set and another with the reduced set, respectively. Finally, step-steer and constant radius tests are performed to assess the handling performance with respect to the two validated centroids. Comparisons of lateral dynamics and understeer characteristics indicate that the proposed method can accurately distinguish driving modes in a much faster manner compared to traditional machine learning. This methodology has significant potential for practical applications with large databases and more complex systems. Full article

Improper car tire pressure affects dynamics, fuel economy, and driver safety. Current central tire inflation systems (CTISs) regulate tire pressure relative to its reference value. However, the current CTIS is limited in its automation, as the system requires the loading of present conditions and the manual input of terrain conditions. Therefore, the system lacks intelligent components which would increase its efficiency. Adding a terrain recognition feature to the current CTIS technology, the tire pressure management system (TPMS) described in this paper enhances the capability to adjust to the ideal tire pressure according to the terrain condition. In this study, we integrate a terrain recognition component which uses a convolutional neural network (CNN), specifically, ResNet-18, into the TPMS to classify and detect terrain conditions and apply the correct pressure level. A one-tire terrain-based TPMS model was developed through system integration. The system was tested under flat, uneven, and soft terrain conditions. The CNN model demonstrated 95% accuracy in classifying the chosen terrains, with demonstrated adaptability to nighttime environments. Inflation and deflation tests were conducted at varying speeds and terrains, and the results showed longer inflation times at higher pressure ranges, while deflation times remained consistent regardless of pressure range. A negligible impact on inflation and deflation speed was observed at speeds below 15 km/h. Instantaneous response time between the microcontrollers increases efficiency in the overall CTIS process. Full article

To address the performance degradation in model predictive control (MPC) under vehicle state uncertainties caused by external disturbances (e.g., crosswinds and tire cornering stiffness variations) and rigid constraint conflicts, we propose a robust MPC framework with adaptive weight adjustment and dynamic constraint relaxation. Traditional MPC methods often suffer from infeasibility or deteriorated tracking accuracies when handling model mismatches and disturbances. To overcome these limitations, three key innovations are introduced: a three-degree-of-freedom vehicle dynamic model integrated with recursive least squares-based online estimation of tire slip stiffness for real-time lateral force compensation; an adaptive weight adjustment mechanism that dynamically balances control energy consumption and tracking accuracy by tuning cost function weights based on real-time state errors; and a dynamic constraint relaxation strategy using slack variables with variable penalty terms to resolve infeasibility while suppressing excessive constraint violations. The proposed method is validated via ROS (noetic)–MATLAB2023 co-simulations under crosswind disturbances (0–3 m/s) and varying road conditions. The results show that the improved algorithm achieves a 13% faster response time (5.2 s vs. 6 s control cycles), a 15% higher minimum speed during cornering (2.98 m/s vs. 2.51 m/s), a 32% narrower lateral velocity fluctuation range ([−0.11, 0.22] m/s vs. [−0.19, 0.22] m/s), and reduced yaw rate oscillations ([−1.8, 2.8] rad/s vs. [−2.8, 2.5] rad/s) compared with a traditional fixed-weight MPC algorithm. These improvements lead to significant enhancements in trajectory tracking accuracy, dynamic response, and disturbance rejection, ensuring both safety and efficiency in autonomous vehicle control under complex uncertainties. The framework provides a practical solution for real-time applications in intelligent transportation systems. Full article

The development of prosthetic limbs has benefited individuals who suffered amputations due to accidents or medical conditions. During the development of conventional prosthetics, several challenges have been observed regarding the functional limitations, the restricted degrees of freedom compared to an actual human limb, and the biocompatibility issues between the surface of the prosthetic limb and the human tissue or skin. These issues could result in mobility impairments due to failed mimicry of the actual stress distribution, causing discomfort, chronic pain, and tissue damage or possible infections. Especially in cases where underlying conditions exist, such as diabetes, possible trauma, or vascular disease, a failed adaptation of the prosthetic limb could lead to complete abandonment of the prosthetic part. To address these challenges, the insertion of topologically optimized parts with a biomimetic approach has allowed the optimization of the mimicry of the complex functionality behavior of the natural body parts, allowing the development of lightweight efficient anthropomorphic structures. This approach results in unified stress distribution, minimizing the practical limitations while also adding an aesthetic that aids in reducing any possible symptoms related to social anxiety and impaired social functioning. In this paper, the development of a novel anthropomorphic designed prosthetic foot with a novel Thermoplastic Polyurethane-based composite (TPU-Ground Tire Rubber 10 wt.%) was studied. The final designs contain advanced sustainable polymeric materials, gyroid lattice geometries, and Finite Element Analysis (FEA) for performance optimization. Initially, a static evaluation was conducted to replicate the phenomena at the standing process of a conventional replicated above-knee prosthetic. Furthermore, dynamic testing was conducted to assess the mechanical responses to high-intensity exercises (e.g., sprinting, jumping). The evaluation of the dynamic mechanical response of the prosthetic limb was compared to actual plantogram-derived foot pressure data during static phases (standing, light walking) and dynamic phenomena (sprinting, jumping) to address the optimal geometry and density, ensuring maximum compatibility. This innovative approach allows the development of tailored prosthetic limbs with optimal replication of the human motion patterns, resulting in improved patient outcomes and higher success rates. The proposed design presented hysteretic damping factor and energy absorption efficiency adequate for load handling of intense exercises (0.18 loss factor, 57% energy absorption efficiency) meaning that it is suitable for further research and possible upcycling. Full article

Assessing infrastructure sustainability requires an evaluation of technical, economic, environmental, and social dimensions, with the latter often being overlooked. Asphalt mixtures incorporating end-of-life tire textile fiber additives in Chile have emerged as a sustainable alternative to conventional fibers. However, the social sustainability of these additives remains underexplored. This study develops a model to assess the social sustainability of asphalt additives in Chile using fuzzy cognitive mapping. The methodology includes three stages: (1) qualitative exploration of the conceptual model by information triangulation, (2) construction of a fuzzy cognitive model to estimate social contributions, and (3) dynamic analysis of four additives, including those derived from end-of-life tire textile fiber. The results show that these recycled additives generate distinct social impacts, particularly in terms of consumer interest, innovation, knowledge transfer, and regulatory alignment. Additionally, technical contributions and certifications significantly influence sustainability assessments, exhibiting greater independence from other factors. The findings highlight the potential of repurposed textile fiber as a socially sustainable alternative in asphalt production. This approach supports circular economy initiatives, fosters innovation, and enhances the acceptance of sustainable infrastructure materials in Chile, contributing to a more resilient and responsible construction sector. Full article