| The roughness of road surface involves the three factors of people, cars, and roads. In the field of vehicle engineering road roughness is the main factor in the environment of vehicle operating and the main sources of incentive and vibration. Road roughness generates resistance and vibration while vehicles are running. The resistance depletes vehicles' power and influences the life-span of vehicle dynamical system and transmission system. And the vibration impacts vehicles'smooth-going and taking comfortableness, handling stability, traveling speed, bearing system's reliability and life-span. In the field of road engineering, road roughness is one of the evaluation indexes of the road project quality at both domestic and international countries, and roughness-test runs though the various links such as quality testing, assessment, confirmation in the period of road's construction and quality inspection, maintenance in the period of road's operation. It is very significant for the fields of vehicle engineering and road engineering to analyze on the road surface roughness, reasonably carry on appraising and grading the roughness, and establish the input model of road surface.In this paper, the key technological contents, such as characteristic determination of road surface roughness, its fractal grade-way roughness and fractal model, were carried on deep and systematic research, based on the current situation of analysis, simulation and grading on the road roughness, combined with various of theories and technologies which have been widely used in various fields, such as analytic theory, computing technology, mathematical model.The concrete work is as follows:Firstly road roughness data collecting system was designed based on virtual instrument technology, to improve the function of the contact-type detectors of the roughness, and a great deal of road roughness data was collected combined with non-contact type of laser roughness car. Then we carried on the traditional analysis of characteristic parameter on the measured data of road roughness. Result showed that more parameters were required when the whole characteristics of the road were described, and these parameters had instability with the changes in metrical scale, even with the same parameters surface characteristics are different greatly, so metrical scale-related and non-uniqueness existed. That is say, the traditional parameters are unable to represent characteristic of the road roughness uniquely, so it is essential to find a new parameters.Fractal theory has rapidly developed into a new branch of mathematics theory since the year of 1975.It has extensive application in describing the signal, the signal dealing and other fields. The research, which confined with surface roughness and fractal theory, has already become an important research branch in crossing science. Numerous researches indicate that fractal dimension can describe essential characteristic of the complicated phenomenon, so in this paper, fractal theory was used to systematically research into characteristics of measured road roughness. First of all, the ideal fractal curves was generated though Weierstrass-Mandelbrot function to analyze comparatively on different methods by which fractal dimensions was confirmed, then the different methods' applicable range, advantages and disadvantages were confirmed to analyze fractal curves. Through different computational methods to confirm fractal dimension, we analyzed the scale law linear relation of longitudinal sections of measured road roughness, which is regarded as two-dimensional curve. Then we found there is relevant scale law linear relation in log-log coordinate of the three methods, besides root-mean-square method, remainder-variation method and structure function method. After calculating and analyzing on both non-scale sector and fractal dimension of the three methods, we found that root-mean-square method is quite stable in the non-scale sector instead of remainder-variation method and structure function method. On the other hand, root-mean-square method has both clear physical significance and signal function to the surface profile curve, so root-mean-square method is definitely a efficacious method to calculate the fractal dimension of road roughness. After the fractal dimension of selected road roughness was calculated by the method of root-mean-square, we expressed the degree of the road roughness based on the results, then we found that only the parameters of fractal dimension can not determine the condition of a road surface roughness, in other words, the fractal dimension has relativity when it is used to signify measured road roughness.In order to further confirm the only parameter which signify the road roughness uniquely, started off with existing international grade standard of road roughness, based on the power spectrum-datum of road surface in every grades, high-procedures tabulates for road surface in each grades were obtained through Fourier inverse transformation law. Then we analyzed the relations between the daily signal parameters of road roughness and its fractal parameters. The result showed that there was very small difference and unclear divide between fractal dimension calculated through root-mean-square method and the prominent fractal characteristics of road surface in different grade simulated by Fourier inverse transformation, but the vertical axis'intercept was greater in Logarithmic distribution of measure and scale, which were from worse grade of road and calculated through root-mean-square method. After we calculated the signal parameters, we found that this intercept and root-mean-square deviation of the outline showed a good linear relationship. Because the fractal parameters of road characteristics were very small and their changes were adverse to use of road grade, we proposed to regard the combination between fractal dimension and root-mean-square deviation of profile as a integrated parameter to signify the road roughness, that is, road roughness index was regarded as a signal parameter of road roughness, and the indexes of every grade road were calculated.Based on the analysis of international standards on classification principles of road surface roughness, its limitations were analyzed by actually road surface roughness. The characteristic parameters of measured road were calculated. Root-mean-square roughness, fractal dimension and the relationship between the roughness index and the road grade based on international standards were analyzed statistically. A road surface roughness classification was established, that regards roughness index as the characteristic parameter.Fractal road model was finished by fractal interpolation function based on the measured discrete data of road surface roughness. Then the results of fractal model through time-frequency domain and fractal parameters were tested, and the factors which affect the accuracy of fractal interpolation were analyzed. The results indicate that it is feasible to use fractal interpolation function which is based on iterated function system to simulate road surface roughness. It has an important reference on objective characterization of road surface roughness, data compression and the manufacture of road surface roughness measuring instruments. The fractal interpolation model of road surface roughness can be used as road input in the simulation of automobile vibration response and this model is closer to the actual road.Finally, the applicability of the fractal road mode was verified through simulation and experiment and the approximation degree, between the fractal model of road surface roughness and the actual random input road, was evaluated. On the platform of ADAMS,the vehicle model of multiple input roads has been established and it is consistent with the actual vehicle. The selected input data are actual measured random data, inverse Fourier transform data, data of fractal interpolation model and the built-in road surface model in the software. Then vehicle dynamic response simulation test has been done. After comparing the vehicle dynamic response parameters under the four above input roads, the result indicates that fractal model can better approach the actual measured road. Its dynamic response parameter is closest to measured road. Further collecting the vehicle vibration response parameters through real vehicle experiment and comparing with the simulation results, we found that the simulation result is in good agreement with the real vehicle test result.
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