Simplified empirical approach for predicting the remaining strength factor used in pavement rehabilitation applications
This paper presents a simplified empirical model for predicting the asphaltic remaining strength factor to be used in estimating the resurfacing thickness for both thin and thick asphaltic surfaces. The proposed model for predicting the asphaltic remaining strength factor in the case of thin asphalt...
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Taylor & Francis Group
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e8cb10aa61fb4e5c97c4142537157c10 |
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| Sumario: | This paper presents a simplified empirical model for predicting the asphaltic remaining strength factor to be used in estimating the resurfacing thickness for both thin and thick asphaltic surfaces. The proposed model for predicting the asphaltic remaining strength factor in the case of thin asphaltic surface is mainly a function of key performance indicators and calibration constant (K). In the case of thick asphaltic surface, an average remaining strength factor is proposed which is a function of the existing asphaltic surface thickness, cold milling thickness, and the remaining strength factor associated with thin asphaltic surface. The proposed remaining strength factor is to be used in estimating the resurfacing thickness component due to the strength loss endured by the asphaltic surface. Two case studies are presented to predict the remaining strength factor. The first one applies the remaining strength factor model to estimate the resurfacing thicknesses for two sample projects considering variable rehabilitation scheduling time, while the second one calibrates the remaining strength factor model for a local roadway sample using minimization of the sum of squared errors. The sample results indicate that the remaining strength factor values (0.45–0.94) are lower for thin asphaltic surface compared to the corresponding values (0.72–0.97) for thick surface considering 6–12 years rehabilitation scheduling time, and they are lower for inferior pavement performance compared to a superior one. The sample results also indicate that the optimal (K) values for thin asphaltic surface (0.71–1.24) are considerably lower than the corresponding optimal (K) values for thick surface (2.08–3.83). |
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