Mechanistic Roughness Model Based on Vehicle-Pavement Interaction

2000 ◽  
Vol 1699 (1) ◽  
pp. 114-120 ◽  
Author(s):  
Mofreh F. Saleh ◽  
Michael S. Mamlouk ◽  
Emmanuel B. Owusu-Antwi
Keyword(s):  
Vehicle Dynamics ◽  
Performance Model ◽  
Dynamic Force ◽  
The Road ◽  
Pavement Roughness ◽  
Roughness Model ◽  
Road Roughness ◽  
Dynamic Forces ◽  
Force Profile ◽  
Level Performance

A mechanistic roughness performance model that takes into account vehicle dynamics was developed for use in flexible pavement design and evaluation. The model was developed in the form of a relation between roughness and number of load repetitions, axle load, and asphalt layer thickness. The model is completely mechanistic and uses vehicle dynamics analysis to estimate the dynamic force profile and finite element structural analysis to estimate the change of pavement surface roughness for each load repetition. The model makes use of the fact that pavement roughness changes the magnitude of the vehicle dynamic forces applied on the pavement and that the dynamic forces change the road roughness. The developed mechanistic roughness performance model can be used to estimate the 80-kN (18-kip) equivalent single-axle load for mixed traffic. The model can also be used to design pavement so that it will last for a certain number of load repetitions before reaching a predetermined roughness level. Performance-based specifications can be developed using the methodology presented in this study. The model has been calibrated and verified with field data elsewhere.

scholarly journals METROLOGICAL SUPPORT OF GEODETIC WORKS IN THE ROAD ROUGHNESS MEASURING

2021 ◽  
Vol 6 (166) ◽  
pp. 130-133
Author(s):  
H. Sarkisian ◽  
V. Tymoshevskyi ◽  
S. Urdzik
Keyword(s):  
Metrological Support ◽  
Dynamic Load ◽  
Road Maintenance ◽  
Rigid Pavement ◽  
The Road ◽  
Pavement Roughness ◽  
Road Roughness ◽  
Accuracy Of Measurements ◽  
On The Road

Most of the transport and operational indicators that directly affect the road roughness depend on the roughness of coverage. Therefore, the control and timely monitoring of the road roughness is an extremely important issue that needs the attention of road maintenance services. At monitoring of the road roughness it is most expedient to use a technique of leveling of a covering. The method of leveling the coating provides more detailed information about the coating and allows you to determine the smallest deformations on the road coating, which may be at the first stage of their development, especially at that stage of their development, and show roughness and various parameters. One of the main tasks of measurements in the process of performing geodetic works is not only to obtain the measurement result, but also to assess its reliability. The required quality of instrumental measurement can not be achieved without adhering to the principles of unity and the required accuracy of measurements, so much attention should be paid to the metrological support of geodetic works. The purpose of this article is to analyze the metrological support of geodetic works in determining the pavement roughness and substantiation of the required accuracy of measuring the non-rigid pavement roughness. On the basis of dependences for determining the coefficient of dynamic load on pavement and the correlation between the pavement roughness and the coefficient of dynamic load and on the basis of experimental data, the necessary accuracy of measuring the non-rigid pavement roughness is substantiated. Based on the analysis, it was found that the accuracy of determining the height of the irregularities should not exceed 0.5 mm, for which it is necessary to use optical or electron-optical levels.

Road Flatness Detection Using Permutation Entropy (PE)

2014 ◽  
Vol 721 ◽  
pp. 420-423 ◽  
Author(s):  
Qing Chao Wang ◽  
Wan Qing Song ◽  
Jian Kai Liang
Keyword(s):  
Detection Method ◽  
Permutation Entropy ◽  
Data Array ◽  
The Road ◽  
Pavement Roughness ◽  
Road Roughness

Aimed at checking the signal’s nonlinearity and instability of road flatness, in order to get a more convenient and significant approach to verify the pavement smoothness, a road roughness detection method on the base of Permutation Entropy (PE) algorithm is put forward. This method firstly relevantly deals with the collecting road signals, then getting Permutation Entropy (PE), and reaches relevant drawing data array. By the use of Matlab drawing, the road roughness can be proved according to the PE. The experiment turns out that Permutation Entropy (PE) algorithm has an effective and convenient effect on checking road flatness, and it is more obvious to show pavement roughness compared the method of the Holder index.

Research on the Simulation of Random Road Excitation in Time-Domain

2015 ◽  
Vol 738-739 ◽  
pp. 508-511 ◽  
Author(s):  
Yue Feng Zhu ◽  
Li Wang
Keyword(s):  
Dynamic Loads ◽  
Ar Model ◽  
Road Transportation ◽  
The Road ◽  
Pavement Roughness ◽  
Road Roughness ◽  
Moving Vehicles ◽  
Road Excitation ◽  
Increasing Demand ◽  
The Given

With the development of national economy and the increasing demand for the road transportation, the vehicles are widely used. The dynamic loads imposed by moving vehicles have variations due to the surface roughness and the larger dynamic loads can affect the pavement performance and life. In order to improve the accuracy of the reconstruction of the road roughness, the time series model—AR model was established based on the given standard pavement roughness PSD which can provide the theory conditions of vibration control and the interaction between vehicles and road.

scholarly journals Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1655 ◽  
Author(s):  
Ahmadreza Mahmoudzadeh ◽  
Amir Golroo ◽  
Mohammad Jahanshahi ◽  
Sayna Firoozi Yeganeh
Keyword(s):  
Surface Defects ◽  
Cost Effective ◽  
Pavement Management ◽  
Outdoor Environment ◽  
Primary Data ◽  
International Roughness Index ◽  
The Road ◽  
Pavement Roughness ◽  
Power Spectral ◽  
Road Roughness

Measuring pavement roughness and detecting pavement surface defects are two of the most important tasks in pavement management. While existing pavement roughness measurement approaches are expensive, the primary aim of this paper is to use a cost-effective and sufficiently accurate RGB-D sensor to estimate the pavement roughness in the outdoor environment. An algorithm is proposed to process the RGB-D data and autonomously quantify the road roughness. To this end, the RGB-D sensor is calibrated and primary data for estimating the pavement roughness are collected. The collected depth frames and RGB images are registered to create the 3D road surfaces. We found that there is a significant correlation between the estimated International Roughness Index (IRI) using the RGB-D sensor and the manual measured IRI using rod and level. By considering the Power Spectral Density (PSD) analysis and the repeatability of measurement, the results show that the proposed solution can accurately estimate the different pavement roughness.

A fuzzy evaluation method of road vehicle automatic weighing instrument in dynamic force metrological performance

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1365-1372
Author(s):  
Xiaohui Mao ◽  
Liping Fei ◽  
Xianping Shang ◽  
Jie Chen ◽  
Zhihao Zhao
Keyword(s):  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Dynamic Performance ◽  
Dynamic Force ◽  
Fuzzy Evaluation ◽  
Road Vehicle ◽  
Evaluation Standard ◽  
The Road ◽  
Metrological Performance

The measurement performance of road vehicle automatic weighing instrument installed on highways is directly related to the safety of roads and bridges. The fuzzy number indicates that the uncertain quantization problem has obvious advantages. By analyzing the factors affecting the metrological performance of the road vehicle automatic weighing instrument, combined with the fuzzy mathematics theory, the weight evaluation model of the dynamic performance evaluation of the road vehicle automatic weighing instrument is proposed. The factors of measurement performance are summarized and calculated, and the comprehensive evaluation standard of the metering performance of the weighing equipment is obtained, so as to realize the quantifiable analysis and evaluation of the metering performance of the dynamic road vehicle automatic weighing instrument in use, and provide data reference for adopting a more scientific measurement supervision method.

scholarly journals Road Roughness Estimation Based on the Vehicle Frequency Response Function

Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 89
Author(s):  
Qingxia Zhang ◽  
Jilin Hou ◽  
Zhongdong Duan ◽  
Łukasz Jankowski ◽  
Xiaoyang Hu
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Gaussian White Noise ◽  
Estimation Method ◽  
Time Shift ◽  
Ride Quality ◽  
The Road ◽  
Road Roughness ◽  
Measured Response

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.

scholarly journals Striated Tire Yaw Marks—Modeling and Validation

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4309
Author(s):  
Wojciech Wach ◽  
Jakub Zębala
Keyword(s):  
Vehicle Dynamics ◽  
Linear Equations ◽  
Tire Model ◽  
Vehicle Accidents ◽  
The Road ◽  
Variable Curvature ◽  
Macro Scale ◽  
On The Road ◽  
Multi Body ◽  
Non Linear Equations

Tire yaw marks deposited on the road surface carry a lot of information of paramount importance for the analysis of vehicle accidents. They can be used: (a) in a macro-scale for establishing the vehicle’s positions and orientation as well as an estimation of the vehicle’s speed at the start of yawing; (b) in a micro-scale for inferring among others things the braking or acceleration status of the wheels from the topology of the striations forming the mark. A mathematical model of how the striations will appear has been developed. The model is universal, i.e., it applies to a tire moving along any trajectory with variable curvature, and it takes into account the forces and torques which are calculated by solving a system of non-linear equations of vehicle dynamics. It was validated in the program developed by the author, in which the vehicle is represented by a 36 degree of freedom multi-body system with the TMeasy tire model. The mark-creating model shows good compliance with experimental data. It gives a deep view of the nature of striated yaw marks’ formation and can be applied in any program for the simulation of vehicle dynamics with any level of simplification.

On the Road Profile Estimation from Vehicle Dynamics Measurements

2021 ◽  
Author(s):  
Angelo Domenico Vella ◽  
Antonio Tota ◽  
Alessandro Vigliani
Keyword(s):  
Vehicle Dynamics ◽  
The Road ◽  
Road Profile ◽  
On The Road ◽  
Profile Estimation

The Time Domain Simulation of Non-Stationary Road Roughness

2013 ◽  
Vol 423-426 ◽  
pp. 1238-1242
Author(s):  
Hao Wang ◽  
Xiao Mei Shi
Keyword(s):  
Power Spectrum ◽  
Time Domain ◽  
Ride Comfort ◽  
Time History ◽  
Random Excitation ◽  
Power Spectrum Density ◽  
The Road ◽  
Road Roughness ◽  
Spectrum Density ◽  
History Of

The input of road roughness, which affects the ride comfort and the handling stability of vehicle, is the main excitation for the running vehicle. The time history of the road roughness was researched with the random phases, based on the stationary power spectrum density of the road roughness determined by the standards. Through the inverse Fourier transform, the random phases can be used to get the road roughness in time domain, together with the amplitude. Then, the time domain simulation of the non-stationary random excitation when the vehicle ran at the changing speed, would also be studied based on the random phases. It is proved that the random road excitation for the vehicle with the changing speed is stationary modulated evolution random excitation, and its power spectrum density is the stationary modulated evolutionary power spectrum density. And the numerical results for the time history of the non-stationary random inputs were also provided. The time history of the non-stationary random road can be used to evaluate the ride comfort of the vehicle which is running at the changing speed.

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