Abstract
This paper more specifically focuses on the estimation of a road profile (i.e., along the “wheel track”). Road profile measurements have been performed to evaluate the ride quality of a newly constructed pavement, to monitor the condition of road networks in road management systems, as an input to vehicle dynamic systems, etc. The measurement may be conducted by a slow-moving apparatus directly measuring the elevation of the road or using a means that measures surface roughness at highway speeds by means of accelerometers coupled with high speed distance sensors, such as laser sensors or using a vehicle equipped with a response-type road roughness measuring system that indirectly indicate the user’s feelings of the ride quality. This paper proposes a solution to the road profile estimation using an artificial neural network (ANN) approach. The method incorporates an ANN which is trained using the data obtained from a validated vehicle model in the ADAMS software to approximate road profiles via the accelerations picked up from the vehicle. The study investigates the estimation capability of neural networks through comparison between some estimated and real road profiles in the form of actual road roughness and power spectral density.
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References
American Society of Testing and Materials, Standard Terminology Relating to Vehicle Pavement Sections, ASTM E867, Annual Book of ASTM Standards, 4.03 (2000).
R. W. Perera and S.D. Kohn, Issues in Pavement Smoothness: A Summary Report, Final Report, NCHRP Project 20-51 [01], Transportation Research Board, Washington D.C., (2002).
American Society of Testing and Materials, Standard Test Method for Measuring the Longitudinal Profile of Traveled Surfaces with an Accelerometer Established Inertial Profiling Reference, ASTM E950, Annual Book of ASTM Standards, 4.03 (2004).
American Society of Testing and Materials, Standard Test Method for Measuring Pavement Roughness Using a Profilograph, ASTM E1274, Annual Book of ASTM Standards, 4.03, (2008).
American Society of Testing and Materials, Standard Test Method for Measuring Road Roughness by Static Level Method, ASTM E1364, Annual Book of ASTM Standards, 4.03 (2000).
American Society of Testing and Materials, Standard Practice for Computing International Roughness Index of Roads from Longitudinal Profile Measurements, ASTM E1926, Annual Book of ASTM Standards, 4.03 (2003).
American Society of Testing and Materials, Standard Practice for Computing Ride Number of Roads from Longitudinal Profile Measurements Made by an Inertial Profile Measuring Device, ASTM E1489, Annual Book of ASTM Standards, 4.03 (2007).
M. W. Sayers and S. M. Karamiahas, The Little Book of Profiling, University of Michigan Transportation Research Institute, (1998).
A. González, E. J. O’brien, Y. Li and K. Cashell, The use of vehicle acceleration measurements to estimate road roughness, Vehicle System Dynamics, 46 (2008) 483–499.
Daniel Hugo, Stephan P. Heyns, Roger J. Thompson and Alex T. Visser, Condition-Triggered Maintenance for Mine Haul Roads with Reconstructed-Vehicle-Response to Haul Road Defects, Journal of the Transportation Research Board, (2007) 254–260.
Harry M. Ngwangwa, P.Stephan Heyns, Kobus F. J. Labuschange and Grant K. Kululanga, An Overview of the Neural Network Based Technique for Monitoring of Road Condition via Reconstructed Road Profiles, Proc. of the 27th Southern African Transport Conference, ISBN No.: 978-1920017-34-7, (2008).
I. Kageyama, On a Control of Tire Force Coefficient for Vehicle Handling with Neural Network System - Estimation for Load Rate of Tire Forces, 13th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks, Chengdu, Sichuan, China, (1992) 31–40.
H. B. Pacejka and T. Takahashi, Pure Slip Characteristics of Tires on Flat and Undulated Road Surfaces, Proc. Int. Symp. Advanced Vehicle Control, SAE, Japan, No. 923064, (1992) 360–365.
L. Palkoviks, M. El-Gindy and H. B. Pacejka, Modeling of the Cornering Characteristics of Tires on an Uneven Road Surface, Dynamic Version of the “Neuro-Tire”, Int. Journal of Vehicle Design, 15(1/2) (1994) 189–215.
M. Wurtenberger and R. Iserman, Model Based Supervision of Lateral Vehicle Dynamics, Proc. American Control Conference, 1 (1994) 408–412.
A. Ghazizadeh and A. Fahim, Neural Network Representation of a Vehicle Model: “Neuro-Vehicle”, Int. J. of Vehicle Design, 17(1) (1996) 55–75
W. R. Pasterkamp and H. B. Pacejka, Application of Neural Networks in the Estimation of Tire/Road Friction Using the Tire as Sensor, SAE, No. 971122, (1997).
ADAMS/Tire Help, ADAMS/CAR Software.
Mechanical Vibration-Road Surface Profiles-Reporting of Measured Data, International Organization for Standardization, ISO 8608, (1995).
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This paper was recommended for publication in revised form by Associate Editor Kyongsu Yi
Mahdi Yousefzadeh received his B.S. degree in Mechanical Engineering from Mazandaran University (currently called Babol Noshirvani University of Technology), Iran, in 2000. He then received his M.S. degree from K.N. Toosi University of Technolgy, Iran, in 2003.
Shahram Azadi received his B.S. and M.S. degrees in Mechanical Engineering from Sharif University of Technology, Iran, in 1988 and 1992, respectively. He then received his Ph.D. from Amirkabir University of Technology, Iran, in 1999. Dr. Azadi is currently a Professor in the faculty of Mechanical Engineering at K.N.Toosi University of Technology in Tehran, Iran.
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Yousefzadeh, M., Azadi, S. & Soltani, A. Road profile estimation using neural network algorithm. J Mech Sci Technol 24, 743–754 (2010). https://doi.org/10.1007/s12206-010-0113-1
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DOI: https://doi.org/10.1007/s12206-010-0113-1