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A platform for research: civil engineering, architecture and urbanism
Modelling roughness progression of sealed granular pavements: a new approach
The study reported herein presents a new approach for preparing pavement condition data to use in developing robust multilevel roughness models of sealed granular pavements. Historical time series condition data for 40 highways with a combined length of more than 2300 km have been collected and prepared for use in a multilevel regression analysis. The sample network covers a wide range of operating conditions and environments. The performance parameter used in the modelling is road roughness and the predictor parameters include traffic loading, expansion potential of subgrade soil, climate, condition of drainage system and initial pavement strength. Only sections that are within the gradual deterioration phase of roughness have been used for models’ development. The study shows that heterogeneity is a critical aspect of the data and that it should be considered not only between sections but also between highways and highway classes. For the whole network data-set, the most important predictor of pavement roughness progression is time, followed by initial pavement strength then traffic loading. On average, the roughness grand mean value is 2.47 m/km and the average rate of roughness progression is 0.02 m/km per year for the sample network. Accuracy and reliability of the models have been confirmed when the validation data produced similar model coefficients to those of the initial developed models.
Modelling roughness progression of sealed granular pavements: a new approach
The study reported herein presents a new approach for preparing pavement condition data to use in developing robust multilevel roughness models of sealed granular pavements. Historical time series condition data for 40 highways with a combined length of more than 2300 km have been collected and prepared for use in a multilevel regression analysis. The sample network covers a wide range of operating conditions and environments. The performance parameter used in the modelling is road roughness and the predictor parameters include traffic loading, expansion potential of subgrade soil, climate, condition of drainage system and initial pavement strength. Only sections that are within the gradual deterioration phase of roughness have been used for models’ development. The study shows that heterogeneity is a critical aspect of the data and that it should be considered not only between sections but also between highways and highway classes. For the whole network data-set, the most important predictor of pavement roughness progression is time, followed by initial pavement strength then traffic loading. On average, the roughness grand mean value is 2.47 m/km and the average rate of roughness progression is 0.02 m/km per year for the sample network. Accuracy and reliability of the models have been confirmed when the validation data produced similar model coefficients to those of the initial developed models.
Modelling roughness progression of sealed granular pavements: a new approach
Alaswadko, Nahla (author) / Hassan, Rayya (author) / Meyer, Denny (author) / Mohammed, Bayar (author)
International Journal of Pavement Engineering ; 20 ; 222-232
2019-02-01
11 pages
Article (Journal)
Electronic Resource
English
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