Using of 3D Road Surface Model for Monitoring of Transverse Unevenness and Skid Resistance

Slabej, Martin; Kotek, Peter

doi:10.1016/j.proeng.2014.12.029

Cited by 5 publications

(6 citation statements)

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“…There are currently over 20 kinds of known equipment for the measurement of shear friction, although some of the devices of the same type may still differ in the marginal measuring conditions (Skiddometer, Scrim, etc.). Currently Profilograph GE is used for measuring the texture which gives us the value of an average depth of profile (MPD) [Slabej 2016].…”

Section: Current Status Of Macrotexture Measurementsmentioning

confidence: 99%

Macrostructure Digitalization of the Roadway Surface Profiles

Kaščak¹,

Baron²,

Török³

et al. 2019

MM SJ

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This paper considers the characteristics of the device ZScanner 700, it usage and ability to process data from measurements of the roadway's geometric profile. It refers to the ability of ZScanner 700 to record surface deviations with sufficient accuracy and the possibility of processing obtained digitalized data in GOM inspect software for surface analysis and Global Mapper for watershed simulation. This analysis and simulation is directly related to the MPD measurement performed on roadways. The purpose of the MPD measurement is to detect macroscopic road surface defects to avoid water retention on the road and the origin of hydroplaning effect.

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Section: Current Status Of Macrotexture Measurementsmentioning

confidence: 99%

Macrostructure Digitalization of the Roadway Surface Profiles

Kaščak¹,

Baron²,

Török³

et al. 2019

MM SJ

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show abstract

“…As shown in Figure 13(b), the IRI model is typical in simulating the vehicle vibration responses. Therefore, the influences of road geometry parameters on vehicle vibration can be evaluated by the vehicle vibration model and the evaluation indices shown in (16) and (17).…”

Section: Analysis Of the Geometric Parameters On Vehiclementioning

confidence: 99%

“…Considering that the vehicle/road vibration model represented by (15) can only evaluate the longitudinal and vertical parameters of the road, the cobblestone road is generated here according to the variation range of the parameters in Table 3, and the IRI and̈are calculated by (16) and (17). The corresponding relationship between the evaluation indices and the road parameters is shown in Figure 14.…”

Section: Analysis Of the Geometric Parameters On Vehiclementioning

confidence: 99%

“…In terms of modeling of road geometry, 3D models can more accurately reflect the effect of roads on vehicles, especially when vehicles are subjected to lateral loading; thus more accurate test results are obtained in the virtual test based on 3D model [17,18]. The application of laser scanning technology makes it possible to measure the 3D road profile directly and accurately.…”

Section: Introductionmentioning

confidence: 99%

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Research on 3D Modeling Method of Cobblestone Road Based on Geometric Characteristics

Feng

Cui

et al. 2018

Mathematical Problems in Engineering

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Based on the analysis of the geometric characteristics, distribution characteristics, and constraints of the cobblestone road, a three-dimensional (3D) parametric road modeling method based on geometric characteristics is proposed. The cobblestone model is established by the exponential oval equation, and the cobblestones distribution is determined by Monte Carlo stochastic search method. Then the 3D theoretical model of cobblestone road is obtained. To veritably simulate the cobblestone road, a generated 3D random road is fusing with the 3D cobblestone road model by alternating values, and the comparison between the real and modeling cobblestone roads is done in space domain and PSD curves. Then based on the establishment of the standard vehicle vibration model, the influences of the key geometric parameters of the cobblestone road on the vehicle vibration are analyzed by using the evaluation indices of the IRI and the RMS of the vehicle body acceleration. The proposed method can be extended to the 3D modeling of almost all strengthened test roads such as Belgian road, fish-scale pits road, ripple tracks, and washboard road and provides a 3D road modeling method with adjustable parameters, authenticity, and accuracy for the comprehensive construction of vehicle virtual test field.

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“…This is because different pavement materials have different mean depth planes (MDPs) and maximum height of the disturbing sand, which are mainly affected by the pavement materials, geometry, and surface characteristics. The MDP and maximum height of the disturbing sand are key features for calculating the MTD (Hu et al., ; Slabej and Kotek, ). A straightforward formula or unsupervised algorithm cannot extract the different MDPs and maximum heights from the 3D scan data of various pavement materials (Wen, ).…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis

Zheng

Gao

Sha

et al. 2018

Computer aided Civil Eng

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Several data processing techniques (DPTs) have been implemented for evaluating pavement surface texture to partially replace onsite inspections by humans. However, the extensively varying real‐world situations have resulted in challenges in the widespread adoption of DPTs. To overcome these challenges, we propose the use of a convolutional neural network (CNN) to calculate the mean texture depth (MTD) without computing the surface texture feature statistics. Because a CNN is capable of automatically learning data features, the proposed method does not require the conjugation of DPTs for extracting features. The proposed CNN was trained and tested using 8,000 and 1,000 3D scan data samples, respectively, and achieved an average error of 0.0024 cm. The stability of the CNN was analyzed based on various test results. Comparative studies were conducted to verify the superiority of the CNN over conventional MTD algorithms. The results demonstrated that the CNN‐based method is significantly more stable in various real‐world situations. Additionally, the CNN‐based method achieved higher accuracy of automatic feature extraction than traditional MTD methods. Finally, the CNN‐based method was applied to evaluate the surface texture statistics of four highways in Shanxi, China, which were different from the training and testing samples; the results establish the transferability of this method to different highways.

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Using of 3D Road Surface Model for Monitoring of Transverse Unevenness and Skid Resistance

Cited by 5 publications

References 0 publications

Macrostructure Digitalization of the Roadway Surface Profiles

Macrostructure Digitalization of the Roadway Surface Profiles

Research on 3D Modeling Method of Cobblestone Road Based on Geometric Characteristics

Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis

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