Prediction Of Road Roughness For Asphalt Pavement Considering Maintenance Intervetion

As an integrated index in pavement management system, road roughness can be used either to estimate the riding quality of pavement or to provide a basis for maintenance decision making. This paper, which relies on the project "Long-term Asphalt Pavement Performance research program" of the Departure of Transportation (ID:2014318223010), has developed a model for predicting roughness progression under different cross-section information and after various maintenance intervention. The model can be used to provide realistic basis for maintenance planning in determining the optimal maintenance timing and maintenance alternatives, so as to provide a guideline for further improve in scientific maintenance planning system.Seven categories of factors affecting road roughness was summed up based on various studies have been conducted and mechanism analysis of road roughness, and they are road surface distresses, pavement age, traffic load, pavement structure, climate, initial roughness, and maintenance activities. Combined with data types in Long-term Asphalt Pavement Performance research database, all the data related to road roughness was extracted. And then, the roughness sample database of asphalt pavement was reconstructed through data preprocessing. Finally, statistical method was used to obtain statistical law and comparative analysis of real data.Of the seven categories of variables considered, three cross-section variables were selected as candidate variables in variable analysis, and they are road surface distresses, traffic load, and pavement structure, furthermore they are subdivided into 11 specific variables. Through comparative study of factor analysis and cluster analysis, cluster analysis based on SAS statistical analysis software was determined. As the result showed, distress rate, traffic and structural number were determined as input variables to develop the model that is supposed to predict the output variable:roughness.This paper developed a mixed-effects logistic model for describing the evolution law of road roughness deterioration and for identifying the effects of several factors on pavement behavior. And the process was achieved by NLMIXED module of SAS. Random effects in the model explain differences between pavement sections. Furthermore, this model made optimum use of the data by taking into account covariate. Consequently, this study showed the effectiveness of the logistic mixed-effects model as a new approach to explain the pavement roughness data.On the basis of nonlinear mixed-effects modeling, the evolution law of road roughness after maintenance activities was described. The model also quantified the impact of maintenance alternatives, IRI value before maintenance and structural number on road roughness evolution. Based on the analysis above, combined with maintenance threshold analysis and cost-benefit analysis, a maintenance planning system based on road roughness was established ultimately.