Search Results (88)

Ultra-high-performance concrete (UHPC) reinforced with recycled tire steel fibers (RTSFs) was studied to evaluate its mechanical properties and cracking behavior. Using acoustic emission (AE) monitoring, researchers tested various RTSF replacement rates in compression and flexural tests. Results revealed a clear trend: mechanical properties initially improved then declined with increasing RTSF content, peaking at 25% replacement. AE analysis showed distinct patterns in energy release and crack propagation. Signal timing for energy and ringing count followed a delayed-to-advanced sequence, while b-value and information entropy changes indicated optimal flexural performance at specific replacement rates. RA-AF classification demonstrated that shear failure reached its minimum (25% replacement), with shear cracks increasing at higher ratios. These findings demonstrate RTSFs’ dual benefits: enhancing UHPC performance while promoting sustainability. The 25% replacement ratio emerged as the optimal balance, improving strength while delaying crack formation. This study provides insights into the mechanism by which waste tire steel fibers enhance the performance of UHPC. This research provides valuable insights for developing eco-friendly UHPC formulations using recycled materials, offering both environmental and economic advantages for construction applications. Full article

Particulate matter (PM) originating from road dust is an increasing concern in urban air quality, particularly as non-exhaust emissions from tire–pavement interactions gain prominence. Existing models often focus on meteorological and traffic-related variables while oversimplifying pavement surface characteristics, limiting their applicability across diverse spatial and traffic conditions. This study investigates the influence of concrete pavement macrotexture—specifically the Mean Texture Depth (MTD) and surface wavelength—on PM₁₀ resuspension. Field data were collected using a vehicle-mounted DustTrak 8530 sensor following the TRAKER protocol, enabling real-time monitoring near the tire–pavement interface. A multivariable linear regression model was used to evaluate the effects of MTD, wavelength, and the interaction between silt loading (sL) and PM₁₀ content, achieving a high adjusted R² of 0.765. The surface wavelength and sL–PM₁₀ interaction were statistically significant (p < 0.01). The PM₁₀ concentrations increased with the MTD up to a threshold of approximately 1.4 mm, after which the trend plateaued. A short wavelength (<4 mm) resulted in 30–50% higher PM₁₀ emissions compared to a longer wavelength (>30 mm), likely due to enhanced air-pumping effects caused by more frequent aggregate contact. Among pavement types, Transverse Tining (T.Tining) exhibited the highest emissions due to its high MTD and short wavelength, whereas Exposed Aggregate Concrete Pavement (EACP) and the Next-Generation Concrete Surface (NGCS) showed lower emissions with a moderate MTD (1.0–1.4 mm) and longer wavelength. Mechanistically, a low MTD means there is a lack of sufficient voids for dust retention but generates less turbulence, producing moderate emissions. In contrast, a high MTD combined with a very short wavelength intensifies tire contact and localized air pumping, increasing emissions. Therefore, an intermediate MTD and moderate wavelength configuration appears optimal, balancing dust retention with minimized turbulence. These findings offer a texture-informed framework for integrating pavement surface characteristics into PM emission models, supporting sustainable and emission-conscious pavement design. Full article

This study aims to investigate the application of nuclear magnetic resonance (NMR) to characterize mortars containing recycled rubber waste as an eco-innovative material for sustainable construction. The primary objective was to analyze the way rubber granules influence hydration kinetics, microstructural development and pore structure. The innovative mortar formulations incorporated rubber granules, casein, natural hydraulic lime (NHL), and latex. NMR analysis revealed distinct T₂ relaxation time distributions correlated with different pore sizes and water states: shorter T₂ values demonstrate strongly bound water in small pores, while longer T₂ values are associated with loosely bound or free water in larger pores. The formulation with 3.5% NHL and 5% rubber granules exhibited optimal microstructural characteristics. These results reveal that NMR is a valuable, non-destructive tool for monitoring cementitious material evolution and supporting the use of tire-derived waste in eco-innovative mortar designs. Full article

To address the issue of uneven temperature distribution during the tire vulcanization process based on electromagnetic heating, this study focuses on the hot plate of a tire vulcanizing machine. An octagonal hot plate with dimensions of 1380 mm × 1380 mm × 60 mm was adopted, and temperature sensors were installed to monitor temperature changes in real time. Through electromagnetic simulation, the effects of current intensity, frequency, and coil-to-hot-plate distance on temperature uniformity were studied. The simulation results show that the temperature difference increases with current intensity and current frequency, while the temperature difference decreases with the increase in coil-to-hot-plate distance. To minimize the temperature gradient, the coil layout was structurally optimized based on the geometric features of the hot plate to improve magnetic field distribution. Several coil arrangements were designed and compared, including uniform, dual-ring, multi-ring, and the newly proposed flower-shaped configuration. It shows that the multi-ring circular coil has the best uniformity when heating a circular hot plate, and the flower-shaped coil has best temperature uniformity when heating an octagonal hot plate. Experimental validation using an industrial-scale prototype confirmed that the optimized design reduced temperature variation to within ±2 degrees Celsius. This work contributes a practical and geometrically informed coil design strategy for improving the temperature uniformity and energy efficiency of electromagnetic heating systems in industrial tire vulcanization. Full article

Intelligent tire technology significantly enhances vehicle performance and driving safety by integrating sensors and electronics within the tire to facilitate the real-time monitoring of tire–road interactions. However, its testing and validation face challenges due to the absence of integrated bench and road testing frameworks. This paper introduces a novel, comprehensive testing system designed to support the full lifecycle development of intelligent tire technologies across both laboratory and real-world driving scenarios, focusing on accelerometer and strain-based sensing. Featuring a modular, distributed architecture, the system integrates an instrumented wheel equipped with multiple embedded tire sensors and a wheel force transducer (WFT), as well as vehicle motion and driving behavior sensors. A robust data acquisition platform based on NI CompactRIO supports multiple-channel high-precision sensing, with sampling rates of up to 50 kHz. The system ensures that data performance aligns with diverse intelligent tire sensing principles, supports a wide range of test parameters, and meets the distinct needs of each development stage. The testing system was applied and validated in a tire vertical load estimation study, which systematically explored and validated estimation methods using multiple accelerometers and PVDF sensors, compared sensor characteristics and estimation performance under different installation positions and sensor types, and culminated in a product-level assessment in road conditions. The experimental results confirmed the higher accuracy of accelerometers in vertical load estimation, validated the developed estimation algorithms and the intelligent tire product, and demonstrated the functionality and performance of the testing system. This work provides a versatile and reliable platform for advancing intelligent tire technologies, supporting both future research and industrial applications. Full article

Tire additives, extensively utilized as industrial raw materials, may enter aquatic environments through various pathways during production, usage, or disposal processes. Research has shown that these additives pose potential threats to human health. However, the information regarding human exposure to 1,3-diphenylguanidine (DPG), 1,3-di-o-tolylguanidine (DTG), and 1,2,3-triphenylguanidine (TPG) (collectively referred to as DPGs) remains limited. The objective of this research was to evaluate human exposure to DPG and its derivatives by analyzing urine and wastewater samples. DPG, DTG, and TPG were frequently detected in urine samples, with median concentrations of 0.19, 0.06, and 0.03 ng/L, respectively. The median urinary concentration of DPG was significantly higher in children than in the general population (p < 0.05). Nevertheless, higher concentrations of DPGs were detected in wastewater, with median values of 20.7 ng/L (DPG), 0.13 ng/L (DTG), and 0.85 ng/L (TPG). The per capita mass loads of ∑DPGs in wastewater treatment plants (WWTPs) were significantly higher on weekdays than weekends, whereas domestic WWTPs exhibited slightly lower average loads on weekdays compared to weekends. Additionally, urine–wastewater collaborative monitoring revealed that urinary excretion contributed only 28% to the total mass load of ∑DPGs in municipal wastewater, indicating it is not the main source in southern China. Consequently, the wastewater-based epidemiology (WBE) approach based on the analysis of parent compounds is unsuitable for assessing human exposure to DPGs. These results aid in developing an efficient surveillance system for understanding human exposure trends to DPGs. Full article

In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management. Full article

This study aims to accurately estimate vertical tire forces on racing tires of specific stiffness using acceleration, pressure, and speed data measurements from a test rig. A hybrid model, termed Random Forest Assisted Deep Neural Network (RFADNN), is introduced, combining a novel deep learning framework with the Random Forest Algorithm to enhance estimation accuracy. By leveraging the Temporal Convolutional Network (TCN), Minimal Gated Unit (MGU), Long Short-Term Memory (LSTM), and Attention mechanisms, the deep learning framework excels in extracting complex features, which the Random Forest Model subsequently analyzes to improve the accuracy of estimating vertical tire forces. Validated with test data, this approach outperforms standard models, achieving an MAE of 0.773 kgf, demonstrating the advantage of the RFADNN method in required vertical force estimation tasks for race tires. This comparison emphasizes the significant benefits of incorporating advanced deep learning with traditional machine learning to provide a comprehensive and interpretable solution for complex estimation challenges in automotive engineering. Full article

Tire pressure monitoring systems (TPMSs) are essential for maintaining driving safety by continuously monitoring critical tire parameters, such as pressure and temperature, in real time during vehicle operation. Among these parameters, tire pressure is the most significant, necessitating the use of highly precise, cost-effective, and energy-efficient sensing technologies. With the rapid advancements in micro-electro-mechanical system (MEMS) technology, modern automotive sensing and monitoring systems increasingly rely on MEMS sensors due to their compact size, low cost, and low power consumption. This study presents a novel high-precision capacitive pressure sensor based on a capacitive micromachined ultrasonic transducer (CMUT) structure and a silicon–silicon direct bonding process. The proposed design offers exceptional performance with high accuracy, ultra-low power consumption, and reduced production costs, making it an optimal solution for enhancing the precision and efficiency of TPMS. Leveraging its low power requirements, capacitive sensing technology emerges as a superior choice for energy-efficient systems in the automotive industry. Full article

Electric scooters (e-scooters) are becoming popular for short-distance urban transportation since they prioritize environmental sustainability. To enhance the rider’s safety in Alberta’s winter weather, this study entails incorporating real-time posture identification, tire pressure, and slip traction monitoring in e-scooters. This is achieved by deploying sensors from traction control and tire pressure monitoring systems into the rims of e-scooters. The suggested rim’s 3D CAD models, its whole manufacturing cycle, and a simulation-based production layout study are discussed in detail. The manufacturing process simulations reveal bottlenecks, followed by proposed optimizations exhibiting enhanced efficiency, and minimized waiting times. Full article

Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D−CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D−CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study. Full article

Many literature sources state that radial ply tires achieve lower rolling resistance values than cross ply tires. From a certain point of view, radial ply tires are gentler on the ground than cross ply tires. The effort was therefore to experimentally verify this statement for two radial ply and cross ply tires similar in shape and size. The work deals with the diagnostics of rolling resistance levels achieved by radial ply and cross ply tires on selected forest soil under the laboratory conditions of a soil test channel. BKT 210/95 R16 Agrimax RT 855 and Özka 7.50-16 8PR KNK 50 were chosen as radial ply and cross ply tires, respectively, and had approximately the same dimensions. The soil in the soil test channel can be characterized as a loamy sand with an average moisture content of 30% and an initial bulk density of 1445.07 kg·m⁻³. Another monitored parameter was the diagnostics of changes in soil density caused by tire movement in order to assess the degree of soil compaction. From the results of the work, it follows that there is no statistically significant difference between radial ply and cross ply tires in terms of the achieved levels of rolling resistance on the soil. The observed tires also caused intense compaction of the soil in the soil test channel, especially at higher tire pressures and higher vertical loads. The analysis of the results also shows that changes in tire pressure in both tires cause more energy loss and soil compaction than changes in the vertical load. Full article

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers. Full article

A ${\mathcal{H}}_{\infty }$ robust adaptive nonlinear observer for state and parameter estimation of a class of Lipschitz nonlinear systems with disturbances is presented in this work. The objective is to estimate parameters and monitor the performance of nonlinear processes with model uncertainties. The behavior of the observer in the presence of disturbances is analyzed using Lyapunov stability theory and by considering an ${\mathcal{H}}_{\infty }$ performance criterion. Numerical simulations were carried out to demonstrate the applicability of this observer for a semi-active car suspension. The adaptive observer performed well in estimating the tire rigidity (as an unknown parameter) and induced disturbances representing damage to the damper. The main contribution is the proposal of an alternative methodology for simultaneous parameter and actuator disturbance estimation for a more general class of nonlinear systems. Full article

Diverse larval habitats significantly influence female mosquito oviposition. Utilizing traps that simulate these habitats is helpful in the study of the bioecology and characteristics of pathogen-transmitting species during oviposition. This study evaluated the feasibility of different traps in natural environments by comparing sampling methods and detecting the oviposition of epidemiologically important mosquitoes, with emphasis on Haemagogus species, in a fragment of the Atlantic Forest in Silva Jardim, Rio de Janeiro State, Brazil. Monthly collections were conducted from March 2021 to October 2023 using four types of traps: plastic containers, tires, bamboo, and sapucaia. Immatures were collected from these traps using a pipette, placed in plastic bags, and transported to the laboratory. Tire was the most efficient trap, showing the highest mosquito abundance (n = 1239) and number of species (S = 11). Conversely, the plastic container trap exhibited the lowest diversity (H = 0.43), with only two species and a low mosquito abundance (n = 26). The bamboo trap captured six species and recorded the second-highest diversity index (H = 1.04), while the sapucaia trap captured five species and had the third-highest diversity index (H = 0.91). Of the total immatures collected, 1817 reached adulthood, comprising 13 species, two of which are vectors of the sylvatic yellow fever virus: Haemagogus leucocelaenus and Haemagogus janthinomys. In conclusion, detecting key vectors of the sylvatic yellow fever virus in Brazil highlights the need for ongoing entomological and epidemiological surveillance in the study area and its vicinity. These efforts are crucial for monitoring vector presence and activity, identifying potential transmission hotspots, and devising effective control and prevention strategies. Full article