Tire-pavement Interaction Research Articles

Low noise pavement characterization through TCN analysis

The use of low-noise pavements is a valid solution to reduce road traffic noise at source. Further studies are needed to optimize their design and ensure their durability. In this work, low-noise pavements were characterised using innovative and traditional methods on four different pavements: two of them contained crumb rubber added by dry and wet processes, one had an optimized texture and the last one was a traditional dense asphalt concrete. Using a sensor inside the tyre, the tyre cavity noise (TCN) was recorded and analysed, putting it in relation with the Close Proximity Method (CPX) data and the pavement texture measured by a laser profilometer. This paper aims to improve the knowledge of the relationship between the vibrational components of the tyre-pavement interaction using new and old descriptors, extending the types of pavements studied in past researches.

Sound Source Localization at the Rolling Tire

A vehicle's tire rolling on pavement causes complex noise generating mechanisms which are a dominant sub-source of vehicle traffic noise. For the quantification of noise levels due to these tire-pavement interactions, standardized acoustic measurement methods exist, such as the Close-Proximity (CPX) method. These methods require microphones mounted near a rolling tire. However, they do not provide insight in the distribution and amplitudes of present sound sources. In this contribution, we aim at identifying the dominant sound sources of the rolling tire at discrete frequencies. This is accomplished by an inverse method combining microphone array measurements and Finite Element (FE) simulations. The microphone measurements were recorded on Austrian highways with a large measurement trailer built for research purposes. With the considered inverse method, not only the amplitudes of the dominant sound sources but also their phases can be identified. With these identified sources and a validated FE model of the measurement trailer, the sound pressure field within the trailer may be computed. The thereby calculated sound pressure is compared to measurements at the CPX microphone positions and other locations within the trailer.

Bitumen and mastic local rupture test modelling with DEM

We present some preliminary results of a national research project dealing with the wearing course damage and the tire–pavement interactions with traffic loadings, where asphalt concrete raveling test and its modelling are ongoing studies. For this, we used the Local Fracture Test (LFT) or the French acronym RULOB test, developed by Gustave Eiffel University, to analyse the fracture behaviour of bitumen and mastic. The test reproduced a film of binder placed between two aggregates under defined loading conditions. The results of the test highlighted the influence of the loading conditions on the failure scenarios encountered, and the different ageing conditions (RTFOT, RTFOT+PAV). The role of filler in the bitumen (mastic) is also investigated in the same way. The study also attempted to reproduce the experimental test using the DEM software LMGC90. The results agree with the next step of our ongoing study which is to model ravelling tests of asphalt concrete.

Thermo-micromechanical finite element modelling of tyre–pavement interaction under UAE environmental conditions

ABSTRACT This work presents a finite element model of the thermo-mechanical behaviour of tyre–pavement interaction, focusing on the effects of temperature variations in the UAE on skid resistance under various tyre operating conditions. The pavement is modelled as multiple layers to account for the stiffness contribution of each layer. The top asphalt layer is modelled at a microscale level to consider its various constituents such as air voids, aggregates and binder. Moreover, the model accounts for the viscoelastic properties of tyre and pavement, considering their dependence on time and temperature. The finite element simulations of a rolling tyre over the pavement have been carried out under different tyre operating conditions and various temperature cases reflecting the winter, spring, and summer seasons in the UAE. The simulation results show that the maximum level of skid resistance occurs during the winter season and thereafter drops by a significant amount during the summer. This research provides good insights about the seasonal variation of skid resistance in the UAE, which enhances road safety.

A Novel Tire and Road Testing Bench for Modern Automotive Needs

The automotive industry is currently transforming, primarily due to the rise of electric and hybrid vehicle technologies and the need to reduce vehicle mass and energy losses to decrease consumption, pollution, and raw material usage. Additionally, road surface manufacturers emphasize improving pavement durability and reducing rolling noise. This necessitates precise load condition definitions and drives the need for reliable wheel testing benches. Many current benches use abrasive-coated rollers or synthetic tapes, but devices capable of testing on actual road surfaces are rare. In this work, a novel device for testing tire-pavement interaction is proposed. The system features a cart moving along a closed-track platform, ensuring test repeatability and enabling structural durability tests on uneven surfaces with installed obstacles. The cart is equipped with a cantilever arm capable of supporting either a testing wheel with customizable dimensions and kinematic parameters or a tire integrated with a complete suspension system, moving along a customizable pavement surface. The system includes actuators and sensors for applying vertical loads and adjusting the alignment of the testing wheel (slip angle, camber angle, etc.), allowing the characterization of tire behavior such as wear, fatigue, rolling noise, and rolling resistance. Multibody simulations were performed to evaluate the bench’s feasibility in terms of kinematics, power requirements, and structural loads. Results confirmed how this novel test bench represents a promising advancement in tire testing capabilities, enabling comprehensive studies on tire performance, noise reduction, and the structural dynamics of vehicle subsystems.

Impact of Dynamic Wheel Loading on Flexible Pavement Responses for Non-Free Rolling Conditions

Since 2000, pavement design methodologies have transitioned from empirical to mechanistic-empirical procedures. However, the complexities of loading patterns, contact stress distribution, material characterization, vehicle maneuvering, and dynamic load amplification are still not fully considered, despite their significant effect on pavement performance. In this study, state-of-the-art numerical models were used to investigate the changes in critical pavement responses derived from the combined effect of roughness-induced dynamic loading and vehicle maneuvering. A decoupled vehicle–tire–pavement interaction approach composed of a random process to generate artificial road roughness profiles, a mechanical full truck model, and three-dimensional finite element tire and flexible pavement models allow the prediction of the impact of road roughness on vehicle dynamics. In addition, the study quantifies the effect of dynamic loading on contact stresses, and consequently, on pavement critical responses. Because of the expected increase in axle weight from truck electrification, overweight scenarios were also considered. Changes in load history and distribution of strain fields were assessed for two typical pavement structures (thin and thick). The combined effect of roughness-induced dynamic loading and vehicle maneuvering greatly altered the critical pavement responses associated with bottom-up fatigue cracking, near-surface cracking, and rutting. Under the most adverse conditions, the critical tensile strains at the bottom of the asphalt concrete increased up to 125%, the shear strain increased up to 100%, and the compressive strain escalated to 120% when compared with the reference cases. Higher temperatures exacerbated the impact of dynamic wheel loading and vehicle maneuvering.

Analysis of tire-pavement interaction modeling and rolling energy consumption based on finite element simulation

Energy consumption is caused by the rolling resistance of tires as a vehicle moves forward, which contributes to resource consumption and the greenhouse effect. The objective of this paper is to investigate the variation of tire-pavement rolling energy consumption under different operating condition factors. For this purpose, a rolling finite element dynamic model was developed based on real tire and road contact. The correlation between the effective radius and free angle is determined during the free rolling process. The variations of different working conditions on rolling energy consumption under tire-road coupling were analyzed according to both tire and pavement factors, and the influence factors were analyzed by grey correlation analysis. The results show that the factors affecting the significance of rolling energy consumption in descending order are tire load, tire pressure, coefficient of friction, pavement stiffness, and rolling speed. Specifically, energy consumption can be increased by increasing the tire load or friction coefficient, the cumulative increase of rolling energy consumption reaches 68.3% and 14.51%, respectively. Energy consumption can be decreased by increasing the tire pressure or pavement stiffness, the cumulative decrease in energy consumption is 20.45% and 5%, respectively. In addition, the rolling energy consumption increases and then decreases within an increasing speed range(10–80 km/h). The speed trend inflection point is 20 km/h. The research results can provide a technical reference for the development of a low-energy consumption design scheme for tire-pavement systems. To better serve the construction needs of green low-carbon and sustainable road transportation.

Road Pattern Classification Using Deep Learning for Noise Data for Autonomous Driving Vehicle

Tire-pavement Interaction Noise (TPIN) is noise caused by interactions between rolling tires and road surfaces. After measuring the TPIN using a microphone, transform TPIN to images using continuous wavelet transform (CWT). The transformed images are used to classify the driving road and tire using convolutional neural network (CNN). The road and tire classification network using CNN is required for braking systems in autonomous vehicle. The CNN in this paper can classify snow road, asphalt road, and two types of tires with over 97% accuracy.

Tire–Pavement Interaction Simulation Based on Finite Element Model and Response Surface Methodology

Acquiring accurate tire–pavement interaction information is crucial for pavement mechanical analysis and pavement maintenance. This paper combines the tire finite element model (FEM) and response surface methodology (RSM) to obtain tire–pavement interaction information and to analyze the pavement structure response under different loading conditions. A set of experiments was initially designed through the Box–Behnken design (BBD) method to obtain input and output variables for RSM calibration. The resultant RSM was evaluated accurately using the analysis of variance (ANOVA) approach. Then, tire loading simulations were conducted under different magnitudes of static loading using the optimal parameter combination obtained from the RSM. The results show that the deviations between the simulations and the real test results were mostly below 5%, validating the effectiveness of the tire FEM. Additionally, three different dynamic conditions—including free rolling, full brake, and full traction—were simulated by altering the tire rolling angle and translational velocities. Finally, the pavement mechanical response under the three rolling conditions was analyzed based on the tire–pavement contact feature.

A Fast Approach to Optimize Tread Pattern Shape for Tire Noise Reduction

Impact force induced by tread pattern is one of the major mechanisms of tire noise; thus, how to reduce tire impact noise has been an important issue in regard to tire technology. In this paper, the mechanism of tire–pavement interaction noise is briefly described, and a prediction model for pattern noise is proposed. The prediction model was validated with indoor semi-anechoic chamber and pass-by noise experiments. Then, an optimization method for the tread pattern was proposed by using the basis vector method, and the synthetic pattern shape was generated through a linear combination of basis shape vectors. Finally, a novel multi-objective function was proposed, aimed at minimizing the impact noise generated by the tire pattern, and the weight factors of the basis vectors were optimized with a genetic algorithm. The method proposed in this paper can be used to evaluate or improve pattern performance and reduce trial and error in the pattern design stage.

Road type classification using deep learning for Tire-Pavement interaction noise data in autonomous driving vehicle

This study presents a novel artificial intelligence model (AIM) for the real-time classification of 13 different road types in an autonomous vehicle. The model was developed based on a combination of a continuous wavelet transform (CWT) and convolutional neural network (CNN). Previously, three methods have been used for the classification of road types, depending on the type of sensor. First, a camera sensor has been widely used because it can capture the road type directly. Second, a vibration sensor has been used, since the vibration level measured on the suspension or inside the tire depends on the road type. Finally, an acoustic sensor has been used, especially in measuring tire–pavement interaction noise (TPIN). In a previous feasibility study, an AIM was developed to classify road types using TPIN signals, which vary depending on the road type. It can distinguish between two road types: asphalt and snow. Recently, CNN has been widely used as an AIM for classification, but it is limited as the input size of the CNN should be optimized for real-time processing due to its long calculation times, even for a 2D convolution process. Its input is image data, which can be produced through the CWT of the TPIN signal. This study proposes an AIM that can classify 13 different road surfaces in real-time while driving. In this study, a method to determine the optimal filter band and data length used for CWT is proposed. The method was developed based on the classification accuracy of an AIM. The developed AIM was successfully applied to the real-time classification of road types with an accuracy of 95% on a public road.

Pavement responses under wide base tyres subjected to moving loads

Dual tyre assemblies are in use for heavy duty trucks since long time due to various benefits. However, under increasing pressure to accomplish more with less, truck industries have been shifting to new kind of wide base tyres called “super single tyre”. A few studies carried out to compare pavement response under dual tyre, and super single tyre have concluded that super single induce more damage to the pavement compared to the traditional dual tyres. However, most of these studies have not considered the moving loads in their analysis. The present study analyses the tyre-pavement interaction at different speeds with both the type of tyres using 3D-Move program. Results indicate that wide base tyres cause more damage in a pavement when the trucks ply at lower speeds and approximately same damage at higher speeds compared with dual tyre assemblies.

A state-of-the-art review of measurement and modelling of skid resistance: The perspective of developing nation

A critical review of lab and field measurement methodologies, harmonization in measuring techniques, and modelling of skid resistance of asphalt concrete pavement have been provided. Although several past studies have provided literature review on general topics of skid resistance, to the best of the author’s knowledge, none of them have compressively covered the topic considering the status & requirements of developing nations. There has been significant development in speed with the improvement in computational facilities. In modern times, with the improvement in infrastructure quality in developing nations, permitted speeds have also drastically increased. To avoid skid-related accidents, it is important to develop good practices in maintaining sufficient skid resistance. The requirements and the availability of technology might be significantly different in developing nations. The suitability and limitations of various methods used for capturing the skid characteristics of the surface have been outlined. The harmonization in skid resistance measurement using laboratory and field-testing methods has been summarized. Correlation analysis of various in-situ and laboratory test data has been made to maintain a better harmony of measurement either in the field or in the laboratory. In the subsequent sections, progress in the modelling approach (analytical to numerical) has been discussed in brief. Computational capabilities of an analytical and numerical modelling approach for predicting pavement skid resistance characteristics have been reviewed. These models have been developed to consider complex attributes of tire pavement interactions like hydroplaning, temperature rise in the tire, mix morphology, tire inflation, and vehicle acceleration and deceleration for predicting skid resistance. These attributes of skid resistance have been discussed in detail and presented a basic overview of the model development process which is missing in past review studies. Few recent studies on skid resistance measurement and modelling to highlight the use of new technology and improvements over conventional techniques have been presented in the manuscript which has not been reviewed earlier. Critical factors affecting the skid resistance model like hydroplaning, tire-related parameters, temperature, and surface texture have been highlighted in this manuscript. Few key research directions have been suggested as the scope of future study to predict a more reliable skid resistance model.

Effect of Pavement Roughness on Arterial Noise Using Different Vehicle Types

For a healthy and sustainable environment, it is necessary to reduce the negative impact of noise on neighborhoods adjacent to arterials. The development of the automotive industry, the introduction of hybrid and electric vehicles, and improvements to vehicle bodies have made tire–pavement interaction the primary source of noise at regular traffic speeds. The objective of this study was to investigate the effect of pavement surface condition on noise levels for different vehicles, including gasoline, hybrid, electric vehicles, and a single-unit truck. Both interior and exterior noises were measured along rural and urban arterials. The exterior noise was measured using the controlled pass-by method. The results of the analysis indicated that pavement roughness and traffic speed had significantly influenced the interior noise level. It was found that the relationship between interior noise and vehicle speed, and pavement roughness is linear. Compared to gasoline car, hybrid and electric cars may reduce interior noise by 7 and 11 dB(A), respectively. For rural arterials and speeds of 80 km/h, exterior noise was strongly affected by pavement roughness. Analysis of exterior noise on urban arterials at 50 and 60 km/h speeds showed that pavement roughness has a significant impact on the noise level. In contrast, the noise analysis under two-speed levels, which are too close to each other, did not help in capturing the impact of speed on exterior noise. Therefore, further studies are recommended to highlight this issue. For a sustainable and healthy environment, it is vital to keep the pavement in good condition and increase the proportion of electric and hybrid cars in the vehicle fleet.

Study of dynamic characteristics of air cavity resonances from tyre-pavement interaction noise using single frequency filtering.

Air cavities in the tyre tread and the gap formed between tyre and pavement are excited during vehicle movement on a pavement. The former results in pipe resonance, and the latter results in horn resonance. These effects will be varying with speed of the vehicle and also because of conditions of tyre, pavement and, tyre-pavement interaction (TPI). The objective of this paper is to study the dynamic characteristics of air cavity resonances from the signals of the tyre-pavement interaction noise, which was collected by a pair of microphones, when a two wheeler is driven on a pavement at different speeds. The dynamic characteristics of the resonances are analysed using single frequency filtering (SFF) analysis of signals. The method gives spectral information at every sampling instant. The effects of impacts of the tyre treads, pavement characteristics, and TPI on the resulting cavity resonances are examined at four different vehicle speeds and for two different types of pavements. The analysis shows that the SFF spectra bring out the distinct characteristics of the pavements on the formation of air cavities and excitation of the resonances of these cavities. This analysis may help in determining the condition of the tyre and pavement.

합성곱 신경망과 소음 신호의 연속 웨이블릿 변환을 이용한 주행 노면 및 타이어의 분류

This paper presents the method of classifying the road surface type using tire-pavement interaction noise (TPIN) signal. TPIN refers to a noise caused by interactions between rolling tires and road surfaces and is measured using the rugged microphone installed in the wheel cover of tire. The road surface information is identified using continuous wavelets transform (CWT). CWT images for the measured TPIN are used as the input of Convolutional neural network (CNN). The CNN extracts the feature for road surface throughout convolution and pooling process and classifies the road surface type in fully connected neural network. Two road surfaces, snow road and asphalt road, are classified using a method that combines the CWT and CNN methods The results indicate an accuracy of over 97 %. Two different tires are used for the experiment. Results from the road classification can be used to control the braking systems of autonomous vehicle in future.

Study of dynamic characteristics of tread impact excitation from tyre-pavement interaction noise (TPIN) of a two-wheeler

A two or four wheeler moving on a pavement produces acoustic signal due to interaction of the tyre and pavement, called tyre-pavement interaction noise (TPIN). In general, the TPIN signal can be interpreted as the response of the air cavities system, tyre and vehicle body to the impulse-like excitation caused by the tyre tread impact on the pavement. The TPIN signal carries information about the tyre, pavement, tread impact and response of the air cavities excited due to tyre-pavement interaction (TPI). The measured acoustic signal is degraded due to other sounds and vibrations. The average spectral characteristics derived from the measured signals do not give any indication of the dynamic characteristics of the tread impact, which is the major excitation component in the TPIN signal. In this paper the signal analysis method based on single frequency filtering (SFF) is used, where the time varying characteristics of the tread impact are analysed in terms of impulse-like sequence for the TPIN data collected when a two-wheeler was driven on different types of pavements at various speeds. The characteristics of the impulse-like sequence are examined for studying the effects of tyre tread impact, pavement type and speed of the vehicle. The objective of this study is to analyse the excitation component of the TPIN signal for different speeds of the vehicle moving on two different types of pavements. Due to randomness in the TPI, the dynamic characteristics of the excitation component of the signal needs to be examined to relate the characteristics to the tyre, pavement and TPI.

Towards an improved prediction of asphalt pavement rutting through thermal-mechanical coupled constitutive modelling

ABSTRACT To predict accurately the evolution of asphalt pavement rutting, an advanced numerical modelling process was proposed and successfully implemented through the finite element software ABAQUS. In the process, a thermal-visco-elastic-plastic constitutive model for asphalt mixture was derived into numerical form and compiled using the user-defined material subroutine (UMAT). To apply realistic traffic loading, a mathematical form of loading model was proposed according to the tire-pavement interaction and realised through the user-defined load subroutine (DLOAD). Besides, a heat transfer model was calculated before the mechanical calculation, and the results of the pavement temperature field were used as the boundary condition for the mechanical calculation. A few case simulations were conducted using the numerical process and the results indicate that the severe rutting at pavement intersections or parking lots is due to not only the longer loading time but also the deeper plastic deformation region. In addition, the numerical model is capable of quantitatively identifying the critical temperature at which the pavement rutting starts to grow rapidly; accordingly, a new criterion for taking strategies to avoid or slow down the rutting distress of the pavement can be proposed, which is of great significance for the practice.

An artificial neural network (ANN) approach to model Tire-Pavement interaction noise (TPIN) based on tire noise separation

Tire-pavement interaction is a dominant noise source for passenger cars and trucks above 25 mph (40 km/h) and 43 mph (70 km/h), respectively. For the same pavement, tires with different tread patterns and constructions generate noise of different levels and frequencies. An analogous trend is observed when the same tire rolls on different pavement surfaces. Based on substantial experimental data collected for tire noise and pavement profile, two artificial neural networks (ANN) were developed to predict the tread pattern (ANNTPN) and the non-tread pattern related noise (ANNNTPN) components of tire noise, separately. The major inputs of ANNTPN are the coherent tread profile spectrum and the air volume velocity spectrum calculated from the digitized 3D tread pattern. The major input of ANNNTPN is the tread rubber hardness. The vehicle speed is also included as input for the two ANNs. Furthermore, parameters such as tire size and the two-dimensional pavement profile are used as inputs to predict non-tread pattern related noise for different tire sizes and pavement surfaces. The experimental data used to train and test these ANNs include tire noise collected for thirty-seven different tires tested over a range of speeds (45–65 mph, i.e., 72–105 km/h) on a non-porous asphalt pavement. Additionally, five tires out of the thirty-seven were tested on twenty-six different pavement surfaces (Virginia Tech Transportation Institute SMART Road). Moreover, pavement profile data was obtained at both testing sites. Finally, the once-per-revolution signal of the wheel was recorded and employed to monitor the vehicle speed and, more importantly, to perform the order tracking analysis. This is to break down the tire noise into the tread and non-tread pattern related noise components (denoted as TPN and NTPN, respectively). The optimized ANNs are able to predict the tire-pavement interaction noise well for different tires on different non-porous pavement surfaces tested.

Multiscale modelling and simulation for asphalt pavements under moving tire footprint loads

ABSTRACT The tire-pavement interaction has become an important research topic in recent years as the road transportation system and automobile industry develop. The distribution of contact stress between tires and asphalt pavement caused by traffic load is nonuniform. Furthermore, the asphalt mixture is a typical heterogeneous composite material, consisting at the microscale of aggregates, asphalt binder and air voids. However, much of the previous research has tended to focus on uniform-loaded pavement structure, which is comprised of homogeneous asphalt mixture. In this study, the inhomogeneous contact stress was applied on the macroscale finite element pavement model to study the influence of footprint on the mechanical properties of the asphalt pavement. The effect of tire contact stress on asphalt mixtures was investigated using a coupling method between the pavement model and a microscale model of asphalt mixture. This proposed algorithm is capable of simulating the impact of traffic load on the pavement structure and the asphalt mixture in real situations. It is useful for gaining a better understanding of the deformation processes that occur within the asphalt mixture under the real traffic load, which cannot be derived from the conventional laboratory tests and thus is beneficial to the pavement design.