Tire-pavement Noise Research Articles

Impacts of the replacement of distressed pavements by low noise pavements - a case study in Portugal

BRISA is employing efforts to evaluate the pavement influence as a factor of source noise reduction, while studying forms of adjusting the applicability of the CNOSSOS noise prediction method to the reality of the concession road network, in order to comply with European directives about Environmental Noise evaluation and management. This Project evaluates the environmental noise effects of replacing a wearing course of Porous and Betuminous Asphalt at end-of-life for a course of SMA12 and new Betuminous Asphalt. Two methodologies were applied to evaluate Tire-Pavement noise, (i) through Statistical Pass-By and (ii) Close Proximity methods. Using these methodologies, it was possible to evaluate the effect of the intervention and the evolution of the noise in the first few months of the exploration of the layers and to monitor noise performance of the pavements during its entire lifetime. Brisa is challenged to have better pavements knowledge to extend CNOSSOS pavement library and update noise models.

Introducing the rubber plate impact method: a versatile alternative to close proximity test for tire-pavement noise characterization on asphalt highways

Despite its widespread use, the close proximity (CPX) test for tire-pavement noise is limited to existing pavements, posing challenges for timely and effective corrections of noisy pavements. Additionally, the CPX test entails significant procurement and operation costs. Addressing these issues, this study introduces the rubber plate impact (RPI) method as a simple experimental technique for assessing tire-pavement noise in both laboratory and field settings. The RPI test utilizes the folding motion of the treaded rubber plate on the road surface to simulate air pumping and tread impact noise as two main contributors to tire-pavement noise. Indoor RPI tests on asphalt concrete samples of dense graded asphalt and porous asphalt mixes revealed dominant effects of air void content and associated surface texture on the level of noise. The RPI testing procedure was verified at 17 highway test sections, where both CPX and RPI tests were conducted for comparison. Multiple linear regressions between RPI and CPX data were analyzed to propose CPX prediction models at different speeds (80, 100, 120 km/h), and the performance of these prediction models was validated with independent noise datasets collected from four different highway routes. The findings suggest promising prospects for the RPI test as a cost-effective and versatile alternative to CPX for tire-pavement noise characterization on asphalt-surfaced roads, with implications for road materials design and noise mitigation strategies.

Laboratory and Field Performance Evaluation of NMAS 9.5, 8.0, and 5.6 mm SMA Mixtures for Sustainable Pavement

This study evaluates the laboratory and field performance of stone mastic asphalt (SMA) mixtures with nominal maximum aggregate sizes (NMAS) of 9.5, 8.0, and 5.6 mm. Aggregates and fine aggregates of these sizes were produced using an impact crusher and a polyurethane screen. Mix designs for SMA overlays on aged concrete pavement were developed. Laboratory tests assessed rutting performance using full-scale accelerated pavement testing (APT) equipment and reflective cracking resistance using an asphalt mixture performance tester (AMPT). Field evaluations included noise reduction using CPX equipment, skid resistance using SN equipment, and bond strength using field cores. Results showed that for 8.0 mm SMA mixtures to achieve the same rutting performance as 9.5 mm SMA, PG76-22 grade binder was required, whereas 5.6 mm SMA required PG82-22. The 8.0 and 5.6 mm SMA mixtures showed 22.2% and 25% reduced crack progression, respectively, compared with the 9.5 mm SMA mixtures. Field evaluations indicated that 8.0 mm and 5.6 mm SMA pavements reduced tire–pavement noise by 1.7 and 0.8 dB, increased skid resistance by 8.5% and 2.0%, and enhanced shear bond strength by 150%, compared with 9.5 mm SMA. Overall, the 8.0 mm SMA mixture on aged concrete pavement demonstrated superior durability and functionality toward sustainable pavement systems.

Road-pavement classification by artificial neural network model based on tire-pavement noise and road-surface image

This study focuses on an artificial neural network (ANN) model for classifying pavement types using acoustic and image data. While conventional studies often use road-surface images for pavement classification, they face challenges with image quality degradation owing to external factors, such as sunlight angle, shadows, and lighting. Therefore, in this study, tire-pavement noise, which has different noise characteristics depending on the material and surface treatment, is used independently and in conjunction with image data for ANN training. To construct the training dataset, tire-pavement noise, and road-surface images are collected from 11 highway sampling sites in South Korea. Two simultaneous measurements are used: the tire-pavement noise is collected using the On-board sound intensity (OBSI) method, and the camera captures the road-surface images. 1/3 octave SIL, spectrum, MFCC, GLCM, and HOG are extracted from the raw data, and the ANN models are trained by these features. Using the spectrum as an input feature for the ANN yields a classification accuracy of 95.18%. However, the total number of parameters in the ANN is double that of the other models. To reduce the ANN size, 1/3 octave band SIL is used for training, and the model size is halved. However, the accuracy decreases by 13.47 percentage points. To overcome this significant decrease, the 1/3 octave bands SIL and image features were used to train ANN, simultaneously. This approach increases the accuracy by 93.85%. By training the ANN using MFCC, which is commonly used as an acoustic feature in other machine learning studies, the highest classification accuracy of 96.84% is achieved. Additionally, MFCC models are affected by the number of coefficients and the signal length. To include the dominant frequency of tire-pavement noise, more than 13 coefficients are used, a number generally known to be suitable for speech recognition. Increasing the number of coefficients from 13 to 40 improves accuracy by 1.17 percentage points. The interval for slicing raw WAV files is reduced to increase the training data and classify the pavement using shorter signals without statistically significant accuracy loss. Although accuracy does not decrease until the signal lengths reach 0.5 seconds, it rapidly decreases when the signal lengths become shorter than 0.4 seconds.

Impedance measurements on reflective surfaces: effect of nodes

The ISO 13472-2 standardizes the in-situ measurement of normal-incidence acoustic absorption of road pavements. The standard describes the use of the two-microphone method with an impedance tube specially configured for sealing against pavement surfaces. The method is of interest to the tire-pavement noise and vehicle pass-by noise communities. For roads constructed of dense graded asphalt, for example, pass-by noise test tracks conforming to ISO 10844, the pavement is a highly reflective surface. Under this measurement condition, the acoustic field within the tube develops nodes at one or the other microphone positions at characteristic frequencies. These nodes present as sharp peaks and valleys in the two-microphone transfer function. Such narrow bandwidth features are susceptible to measurement error, in particular, signal processing leakage as evidenced by significant drops in the coherence function at the frequencies of the nodes. In this paper we study the nature of the nodes and the effect they have on the two-microphone transfer function and coherence function. We further investigate how different formulations of the transfer function affect the overall 1/3rd octave band accuracy. The effect of the number of averages on both coherence and leakage error is assessed. The potential to solve this leakage effect by switching microphones to other positions is evaluated in relation to a procedure with two fixed microphones. It was concluded that although shifting microphone positions does have advantages in terms of preventing leakage, the mandatory application of a reference measurement reduces the overall error. Finally, recommendations for improving the procedure in ISO 13472-2 are formulated.

Multi-scale analysis of damping characteristics of dry mixed rubberized porous asphalt mixtures for tire-pavement noise reduction

Waste rubber particles have the potential to enhance the damping performance of asphalt pavement. However, there is a scarcity of research on the mechanism of the influence of rubber particles on the damping characteristics of asphalt mixtures. The purpose of the paper is to explore the influence of rubber particles on the damping characteristics, and reveal the noise-damping mechanism of dry mixed rubberized porous asphalt mixture (RPAM). The dynamic modulus test based on UTM was used to characterize the dynamic stiffness characteristics and energy dissipation capacity of RPAM. The self-made free vibration-damping test was applied to evaluate the damping properties of RPAM. X-ray Computed Tomography (CT) was used to scan the specimens of RPAM, and image analysis techniques were established to study the distribution characteristics of rubber particles. The correlation between the distribution characteristics of rubber particles and damping properties was constructed to explore the damping mechanism of the RPAM. The results showed that with the increase of rubber particle content, the stiffness of RPAM decreases. From an energy perspective, RPAM with more rubber particles has a stronger energy dissipation ability, resulting in better damping properties. From a microscopic perspective, RPAM with more rubber particles has a larger proportion of effective rubber particles, which are key to improving damping properties. It is worth noting that the rubber particle content should not exceed 3% to meet the requirements of pavement performance. The findings offer practical guidelines for the design of low noise pavement.

Modeling the Tire-Pavement Noise as a Regression Function of Speed, Mixture Properties, and Age of the Layer

Modeling the Tire-Pavement Noise as a Regression Function of Speed, Mixture Properties, and Age of the Layer

Noise Performance of Chip Seal Pavements

Increasing numbers of local and state highway agencies are using chip seals for highway pavement maintenance owing to their relatively low cost and simplicity of application. These surfaces can significantly increase both exterior traffic noise levels and interior noise for vehicle occupants. Recent studies included as part of the NCHRP 15-68 Effect Low Noise Rumble Strips Project have quantified these effects using pass-by and interior noise and on-board sound intensity (OBSI) measurements in several states. To minimize the tire–pavement noise generated by chip seals, the California Department of Transportation (Caltrans) has conducted research on different chip seal pavement designs near the City of Bishop, California. Previous measurements of chip seal pavement have indicated an average OBSI level of 106.2 dBA with a range of ±2 dB. The five quietest test sections at Bishop produced an average level of 101.8 dB with a range of ±0.6 dB. In this paper, previous pass-by and interior noise and OBSI measurements are provided along with the comparison of OBSI data to that measured at the Bishop sites. The performance and design information on the Bishop sites are also provided.

Perception of tire-pattern noise

Due to the increase of electric motor cars for preventing spread of carbon dioxide, noise atmosphere inside the car compartment is more important for the perception of tire-pavement noise. This paper describes the impression of pattern noise created by the tire thread pattern under real running condition. Our impression on tire-pattern noise is more prominent if the peak level of the pattern noise together with its frequency location and width of the peak. This paper deals with our impression on tire pattern noise how our impression vary with its peak level and frequency location.

Study of Tire–Pavement Noise Acoustic Performance in Resilient Road Pavement Made of Strain-Hardening Cementitious Composites

A modified strain-hardening cementitious composite (SHCC) material, fabricated using corundum aggregates (SHCC-Cor), has been proposed for roadway applications as it offers high structural performance and high skid resistance. However, the acoustic performance of SHCC is unclear and has not been well studied in the past. Theoretically, SHCC may not provide the optimum solution in acoustic performance as it provides a low texture profile, high density, and low porosity. In this study, the acoustic performance of pavement slabs made of SHCC and modified SHCC-Cor are investigated using a nondestructive method to determine the surface roughness (macro texture) of slab surfaces. The pavement–tire noise level was then simulated using SPERoN software. As result, the noise level coming from the pavement made of SHCC could be up to 65 dB(A), while the noise level for SHCC-Cor increased up to 69.2 dB(A) because of the lower shape factor (G) due to a rougher surface as a result of the existence of corundum aggregate on the SHCC surface. The aeroacoustics were also increased compared to the SHCC slab. The modification of SHCC-Cor by introducing grooves (SHCC-Cor-Gro) successfully reduced the sound level coming from the vibration.

Effects of Pavement Texture Characteristics on Tire–Pavement Noise from Dense-Graded Asphalt Concrete Pavement

Effects of Pavement Texture Characteristics on Tire–Pavement Noise from Dense-Graded Asphalt Concrete Pavement

Comparative Evaluation of Pavement Macrotexture Measurements from Different Devices

Ride quality, safety, and tire pavement noise are functional performance measures of concern to highway motorists. Owing to the significant influence of surface macrotexture on wet-weather skid resistance, splash and spray, and highway traffic noise, a need exists for reliable and repeatable methods to measure pavement macrotexture over the road network. To address this need, the National Cooperative Highway Research Program sponsored a project that developed draft protocols for test methods, equipment specifications, and data quality assurance practices for network-level macrotexture measurement. The project conducted three field experiments to evaluate available technologies in regard to repeatability, agreement, and accuracy. This paper focuses on the field experiment conducted at the Texas A&M University RELLIS Campus, which aimed to validate macrotexture measurements from high-speed laser equipped devices suitable for network-level macrotexture data collection and processing. To accomplish this objective, the RELLIS experiment included test speed and laser exposure time in the test matrix, fabricated a measurement beam to collect static reference texture data using a high-resolution laser, and compared texture measurements from different devices with corresponding reference values. This paper presents the findings and conclusions drawn from the RELLIS experiment.

Laboratory evaluation method of tire-pavement noise deterioration combining Rolling Tire down Tester with accelerated abrasion machine

Tire-pavement noise and its durability have long been investigated by onsite observations. This research aims to explore a laboratory evaluation method of tire-pavement noise deterioration that combines the Rolling Tire down Tester (RTT) with the accelerated abrasion machine. Firstly, microphone placement was determined and repeatability of the laboratory RTT noise test method was evaluated, considering tire landing positions and RTT noise variability. Secondly, impacts of abrasion cycles on abrasion effects were analyzed based on the selected multi-variable accelerated abrasion machine (MAAM). Finally, noise deterioration characteristics were investigated combining RTT with MAAM. Placing microphones on two sides is recommended and the abrasion is terminated after 24,000 abrasion cycles. Overall noise level arises with increasing abrasion cycles, and linear correlation exists. The laboratory changes of noise level, 1/3 octave band spectrum, and noise growth rates are generally consistent with previous research, demonstrating feasibility of evaluating noise deterioration combining RTT with MAAM.

Measured Effect of Low-Height Solid Safety Barriers on Heavy Truck Noise

Low-height solid safety barriers (SSBs) are constructed along highways where an adequate clear zone cannot be provided between the highway and an important roadside feature. Where a roadside SSB is a permanent installation, it is possible that it also provides noise reduction benefits. Highway traffic noise sources that could be shielded by a low-height SSB include tire–pavement noise and any other noise originating close to the pavement surface, such as a low exhaust on a heavy truck. This paper reports the findings of a study examining how heavy truck noise is shielded by low-height roadside SSBs. Measurements of the maximum sound level (Lmax) of individual heavy truck pass-by events at two representative locations in Ohio were obtained at a position where a low-height SSB was present as well as at a nearby unshielded position. The results indicated that a perceptible reduction in the pass-by event Lmax (between 3 and 5 dBA) was realized for locations behind the SSBs. Variations in the measured noise reduction were associated with the line-of-sight shielding between various truck noise sources and the receiver positions. For events in which the exhaust source was shielded by the SSB, the measured noise reductions were higher at the site with the 42-in. tall SSB, although there was no difference for the site with the shorter (32-in.) SSB. It is recommended that analysts consider the potential for noise reduction associated with low-height SSBs in locations where such barriers are expected to be permanent.

Effects of asphalt pavement characteristics on traffic noise reduction in different frequencies

To evaluate the effects of asphalt pavement characteristics on traffic noise reduction in different frequencies, stone mastic asphalt with crumb rubber and porous asphalt concrete were prepared. Dynamic modulus test, air voids test, standing wave tube test, improved tire acceleration method, water submerged test and voids-clogging test were utilized. The results showed that the deformation characteristics of stone mastic asphalt have significant effects on the tire-pavement noise reduction in intermediate-frequency and high-frequency bands. Larger connected air voids of porous asphalt concrete could obtain better ability of noise reduction, especially in the low-frequency band. Both the water submerged and voids-clogging have slight effects on the noise reduction of stone mastic asphalt, while having greater influences on porous asphalt concrete. Water submerged mainly increases the high-frequency band noise, and voids-clogging affects the noise at low-frequency and high-frequency bands. These findings provide significant guidance in the application and promotion of ecological low noise pavements.

Counting of exposed aggregate number on pavement surface based on computer vision technique

Functional performance of exposed aggregate concrete pavement (EACP), such as tyre-pavement noise, is influenced by the surface texture depth and wavelength. To minimise tyre-pavement noise, the texture wavelength, which is represented by the exposed aggregate number (EAN), should be controlled. Normally, the EAN is conducted by manual counting. It requires much human efforts. Therefore, this study suggests an efficient method to count the EAN on a digital image of EACP surface through the deep learning model faster region-based convolutional neural network (Faster R-CNN). The TensorFlow Object Detection API was used to adjust the parameters in the training model. Results of the suggested model were compared with the manual counting in the mock-up and field test dataset. The result showed that the mean absolute error was 5.34 and 8.19 for the mock-up and field tests, respectively. Therefore, the proposed method can be used to preliminarily estimate the EAN under specified condition.

Stress distributions in the textures of prefabricated pavement surface created with the assistance of 3D printing technology

ABSTRACT Textures of concrete pavements not only provide essential functions for vehicles but also affect the durability of pavements. The development of 3D printing and prefabrication technologies enables the manufacturing of intricate surface textures at the industrial scale. Such textures can provide excellent functions such as high skid resistance and low tire-pavement noise; however, the durability of the pavement textures and surfaces remains unknown. In this study, the effects of texture on tire-induced stress distribution were evaluated in detail. Finite element models were created to simulate tire loading, pavement, and their interactions. The models were partially validated through literature data and an onsite experiment. The models were subsequently used to analyze stress distributions on a flat pavement surface and on three types of pavement textures: rectangular, arc-shaped, and rounded trapezoidal. The analysis results show that pavement texture plays an important role in the distributions of various surface stresses. Compared with flat surface, all the types of textures cause stress concentrations. The arc-shaped texture is subjected to both the maximum tensile and compressive stresses, while the rectangular texture and the rounded trapezoidal texture behave similarly in stress magnitude and distribution.

Evaluation of tire-pavement noise based on three-dimensional pavement texture characteristics

To investigate the effect of pavement texture on tire-pavement noise and study the correlation of noise level with new proposed three-dimensional (3D) pavement texture evaluation indices, indoor tire-pavement noise measurement was carried out using a self-developed test device. 12 different asphalt mixture slab specimens were prepared in laboratory, and the texture information was scanned by laser texture scanner. 3D pavement texture indices f8mac, f8mic and σ were calculated based on pretreated pavement texture data, and their correlations with noise level were analyzed. To verify the effectiveness of proposed 3D pavement texture evaluation indices, common 2D pavement texture evaluation indices LTX,0.03→0.5, LTX,0.5→32 and LTX,ch were also calculated, and their correlations with noise level were compared with 3D pavement texture evaluation indices. The results show that the correlation coefficients between overall sound pressure level (SPL) and f8mac, f8mic, LTX,0.5→32 and LTX,0.03→0.5 were 0.9211, 0.7708, 0.9137, and 0.8796 respectively, and higher pavement texture level could enhance and reduce noise level simultaneously at different frequency bands. Moreover, The correlation coefficients of overall SPL with LTX,ch and σ are 0.9141 and 0.7254 respectively, and less uniform distributed pavement texture can promote noise level significantly. Therefore, it could be concluded that the new proposed 3D pavement texture indices could be effectively used for the evaluation of tire-pavement noise, and uniform pavement texture with low texture level is recommended for the quiet pavement design.

Influence of temperature on tire-pavement noise in hot climates: Qatar case

The ambient air temperature greatly influences tire-pavement noise measurements. Therefore, various international standards recommended a temperature correction factor to compensate for noise measurements at varying temperatures. This temperature correction factor is developed based on the local environment (temperature varying from 5 °C to 35 °C) and generally varies with different pavements. Pavements in the Gulf region experienced harsh weather conditions, with temperatures often crossing 40 °C during summer. This study investigates the effect of air temperature on noise measurements in hot climatic conditions of Gulf regions. The On-Board Sound Intensity (OBSI) noise measurements technique was used to collect the noise data at different temperatures ranging from 25 °C to 43 °C. A linear regression analysis was performed to verify the relationship between noise and temperature. Three statistical criteria were selected to avoid parasitic phenomena and contamination of noise data during measurement. Statistical analysis demonstrated a strong relationship between noise intensity and temperature, especially at a frequency above 1250 Hz. Spectra analysis of the noise measurements also showed that the temperature coefficient is frequency-dependent, and generally, a frequency above 1250 Hz has a higher temperature coefficient. OBSI test results at various pavements with different mean texture depths of dense-graded pavement showed that the temperature coefficient varied from 0.052 dB/°C to 0.142 dB/°C. There is an apparent trend of decreasing temperature coefficient with increasing mean texture depth of dense graded asphalt pavements. However, no clear dependence on the temperature coefficient on the vehicle’s speed is observed in this study.

Artificial Neural Network Model for Road Pavement Classification using Features of Tire-Pavement Noise and Road Surface Images

Artificial neural network models were developed to classify road pavement types into the transverse-tined, the longitudinal-tined, NGCS(Next Generation Concrete Surface), Diamond Grinding, and Stone Mastic Asphalt by utilizing tire-pavement noise and road surface images. Tire-pavement noise data were collected by OBSI(On-Board Sound Intensity) method, and analyzed to obtain sound intensity level, sound pressure level, and sound quality indices. Road surface image data was analyzed through image feature extraction algorithms of Hough transformation and HOG(Histogram of gradient). The important features among the acoustic and image characteristics were selected by a random forest model. The acoustic features selected by the random forest algorithm are the overall sound intensity level of 400~5kHz 1/3-octave bands, the sound intensities (W/m2) of 800~2kHz 1/3-octave bands, loudness, fluctuation strength and tonality. The image features selected are the number of longitudinal lines extracted from Hough transform algorithm and HOG of the central cell. The two groups of the selected features were applied separately or together to an artificial neural network model to find classification performance. The classification accuracy rates of the models using acoustic features only, image features only and both acoustic and image features combined were 90.8%, 88.8%, and 97.3%, respectively.