Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Probabilistic approach to characterise laboratory rutting behaviour of asphalt concrete mixtures
Even under extremely controlled laboratory conditions, wheel tracking test results shows significant scatter. This scatter can be attributed to variations in constituent materials, specimen fabrication, aggregate skeleton and testing practices. This paper presents a probabilistic approach to characterise the scatter found in rutting test results. For this purpose, two distinct AC mixtures were designed and evaluated. Several slabs were tested for rutting susceptibility using wheel tracking device. Initially, wheel rut test data from each slab was fitted with Francken model to smoothen the data. The smoothened data was further used to predict (i) rut depth or (ii) number of passes at prespecified locations. The data thus generated (at a specific rut depth or number of passes) was fitted with normal, log normal and Weibull distribution. Using predicted values (rut depth or number of passes) made for a particular probability, probabilistic rutting curve (PRC) were constructed. Based on location of PRCs, it can be concluded that PRCs obtained assuming Weibull distribution results in conservative rut estimates while those based on lognormal distribution results in overestimation. Further, significant differences were found between conventional ordinary least squares approach and PRC’s developed in this research. The proposed methodology combines advantages of traditional testing protocol and probabilistic approaches.
Probabilistic approach to characterise laboratory rutting behaviour of asphalt concrete mixtures
Even under extremely controlled laboratory conditions, wheel tracking test results shows significant scatter. This scatter can be attributed to variations in constituent materials, specimen fabrication, aggregate skeleton and testing practices. This paper presents a probabilistic approach to characterise the scatter found in rutting test results. For this purpose, two distinct AC mixtures were designed and evaluated. Several slabs were tested for rutting susceptibility using wheel tracking device. Initially, wheel rut test data from each slab was fitted with Francken model to smoothen the data. The smoothened data was further used to predict (i) rut depth or (ii) number of passes at prespecified locations. The data thus generated (at a specific rut depth or number of passes) was fitted with normal, log normal and Weibull distribution. Using predicted values (rut depth or number of passes) made for a particular probability, probabilistic rutting curve (PRC) were constructed. Based on location of PRCs, it can be concluded that PRCs obtained assuming Weibull distribution results in conservative rut estimates while those based on lognormal distribution results in overestimation. Further, significant differences were found between conventional ordinary least squares approach and PRC’s developed in this research. The proposed methodology combines advantages of traditional testing protocol and probabilistic approaches.
Probabilistic approach to characterise laboratory rutting behaviour of asphalt concrete mixtures
Singh, Priyansh (Autor:in) / Swamy, Aravind Krishna (Autor:in)
International Journal of Pavement Engineering ; 21 ; 384-396
23.02.2020
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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