Approximating an analytic solution for the optimal design of asphalt pavement


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Date

2017-06-29

Publication Type

Conference Paper

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yes

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Abstract

This article considers how the expected service life of asphalt pavement can be modelled taking into consideration different climates, different amounts of traffic and an opportunity for optimal design. A methodology is proposed that follows the steps used by economists to describe how an optimizing producer would determine optimal inputs to use in a generic production process. Because realistic mechanistic models of thermal cracking and rutting are employed, the standard steps followed by economists are mathematically infeasible. Various approximation techniques are used to circumvent these difficulties so that, in the end, it is possible to present a completely analytic expression for the expected service life where the variables describing the climate, traffic and quality requirement remain variables. Further, it is also possible to derive a design function to determine the optimal choice of the asphalt’s softening point. These are the economist’s factor demand functions. Together the two functions provide a third function for the optimal service life, which is the equivalent of a profit function. Four examples of applications of the optimal service life function are given: for benchmarking, for estimating expected renewal cost, for calculating cost shares for heavy vehicles and for empirically testing mechanistic models of asphalt behavior on in-situ condition state data. A demonstration of the latter confirms the optimal service life function’s ability to predict in-situ service lives using data from Switzerland.

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published

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1 - 32

Publisher

ETH Zurich

Event

2nd International Symposium on Infrastructure Asset Management (SIAM 2017)

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03859 - Adey, Bryan T. / Adey, Bryan T. check_circle
02226 - NSL - Netzwerk Stadt und Landschaft / NSL - Network City and Landscape
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG

Notes

Conference lecture held on June 29, 2017. On request of the submitter, the full text had been removed on October 23, 2017.

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