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

Dong, Shihao; Han, Sen; Luo, Yingwei; Han, Xiao; Xu, Ouming

doi:10.1016/j.conbuildmat.2021.124935

Cited by 16 publications

(5 citation statements)

References 11 publications

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“…The results of ITAM correlate well with those of SLP [36], SPM, and HFT [37]. The processed point cloud data could precisely characterize the actual pavement macrotexture [38]. Weng et al (2022) applied image-based multiscale features for texture depth estimation, and the random forest model yielded the best results (cross-fold validation R 2 = 0.8192) [39].…”

Section: Introductionmentioning

confidence: 73%

A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

2022

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Tire–pavement interaction noise (TPIN) accounts mainly for traffic noise, a sensitive parameter affecting the eco-based maintenance decision outcome. Consistent methods or metrics for lab and field pavement texture evaluation are lacking. TPIN prediction based on pavement structural and material characteristics is not yet available. This paper used 3D point cloud data scanned from specimens and road pavement to conduct correlation and clustering analysis based on representative 3D texture metrics. We conducted an influence analysis to exclude macroscope pavement detection metrics and macro deformation metrics’ effects (international roughness index, IRI, and mean profile depth, MPD). The cluster analysis results verified the feasibility of texture metrics for evaluating lab and field pavement wear, differentiating the wear states. TPIN prediction accuracy based on texture indicators was high (R2 = 0.9958), implying that it is feasible to predict the TPIN level using 3D texture metrics. The effects of pavement texture changes on TPIN can be simulated by laboratory wear.

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Section: Introductionmentioning

confidence: 73%

A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

2022

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“…The characteristics of the reconstructed pavement macrotexture are expected to be consistent with the corresponding actual pavement macrotexture. If the reconstructed macrotexture characteristics have a significant correlation with benchmarks, the reconstructed macrotexture could be regarded as effective (J. Chen et al., 2019; S. Dong et al., 2021a).…”

Section: Methodsmentioning

confidence: 99%

“…If the reconstructed macrotexture characteristics have a significant correlation with benchmarks, the reconstructed macrotexture could be regarded as effective (J. Chen et al, 2019;S. Dong et al, 2021a).…”

Section: Effectiveness Evaluation Metricsmentioning

confidence: 99%

“…Previous studies © 2022 Computer-Aided Civil and Infrastructure Engineering. demonstrated that abundant macrotexture could increase noise levels at lower frequencies (S. Dong et al, 2021a;Sandberg, 1987). Besides, the hysteresis was mainly determined by the pavement macrotexture, which is the main source of the friction between the vehicle tire and the pavement surface (N. Dong et al, 2019;Huyan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Asphalt pavement macrotexture reconstruction from monocular image based on deep convolutional neural network

Dong

Han

et al. 2022

Computer aided Civil Eng

Self Cite

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Pavement macrotexture is one of the major factors affecting pavement functions, and it is meaningful to reconstruct the pavement macrotexture rapidly and accurately for pavement life cycle performance and quality evaluation. To reconstruct pavement macrotexture from monocular image, a novel method was developed based on a deep convolutional neural network (CNN). First, the red-greenblue (RGB) images and depth maps (RGB-D) of pavement texture were acquired by smartphone and laser texture scanner, respectively, from various asphalt mixture slab specimens fabricated in the laboratory, and the pavement texture RGB-D dataset was established from scratch. Then, an encoder-decoder CNN architecture was proposed based on residual network-101, and different training strategies were discussed for model optimization. Finally, the precision of the CNN and the three-dimensional characteristics of the reconstructed macrotexture were analyzed. The results show that the established RGB-D dataset can be used for training directly, and the established CNN architecture is plausible and effective. The mean texture depth and f 8mac of the reconstructed macrotexture both correlate with the benchmarks significantly, and the correlation coefficients are 0.88 and 0.96, respectively. It could be concluded that the proposed CNN can reconstruct the macrotexture from monocular RGB images precisely, and the reconstructed macrotexture could be further used for pavement macrotexture evaluation.

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“…New three-dimensional pavement texture metrics have proven superior in noise evaluation for unevenly textured surfaces. Moreover, texture-based noise models, such as the Statistical Energy Analysis (SEA) and Vibration Model (VM) algorithms, have enhanced our understanding of the relationship between texture level and noise frequency, with digital image processing revealing the critical role of micro-texture in noise reduction [31][32][33]. Material-wise, rubberized pavements show significant potential in reducing high-frequency noise.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Anti-Skid and Noise Reduction Performance of Cement Concrete Pavement with Different Grooved and Dragged Textures

Yang,

et al. 2024

Processes

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Cement concrete pavements are crucial to urban infrastructure, significantly influencing road safety and environmental sustainability with their anti-skid and noise reduction properties. However, while texturing techniques like transverse grooving have been widely adopted to enhance skid resistance, they may inadvertently increase road noise. This study addressed the critical need to optimize pavement textures to balance improved skid resistance with noise reduction. Tests were conducted to assess the influence of surface texture on skid resistance and noise, exploring the relationship between texture attributes and their performance in these areas. The investigation examined the effects of texture representation methods, mean profile depth, and the high-speed sideway force coefficient (SFC) on noise intensity and pavement skid resistance. The findings revealed that transverse grooves significantly improved the SFC, enhancing skid resistance. In contrast, longitudinal burlap drag, through its micro- and macro-texture adjustments, effectively reduced vibration frequencies between the tire and pavement, thus mitigating noise. Utilizing the TOPSIS multi-objective optimization framework, an optimization model for pavement textures was developed to augment skid resistance and noise reduction at varying speeds. The results indicated that at 60 km/h, an optimal balance of groove width, depth, and spacing yielded superior skid resistance with a minimal noise increase. At 80 km/h, increased groove spacing and depth were shown to effectively decrease noise while maintaining efficient water evacuation. The optimal pavement texture design must consider the specific context, including traffic volume, vehicle types, and operating speeds. This study provides essential guidance for optimizing urban cement concrete pavement textures, aiming to diminish traffic noise and bolster road safety.

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

Cited by 16 publications

References 11 publications

A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology

Asphalt pavement macrotexture reconstruction from monocular image based on deep convolutional neural network

Optimization of Anti-Skid and Noise Reduction Performance of Cement Concrete Pavement with Different Grooved and Dragged Textures

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