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A Probabilistic Approach to Flexible Aircraft Pavement Thickness Determination
Conference paper   Peer reviewed

A Probabilistic Approach to Flexible Aircraft Pavement Thickness Determination

Gregory W White
Bearing capacity of roads, railways and airfields : proceedings of the 8th International Conference on the Bearing Capacity of Roads, Railways and Airfields, Champaign, Illinois, June 29-July 2, 2009, Vol.2, pp.889-895
International Bearing Capacity of Road, Railways and Airfields Conference (BCR2A), 8th (Champaign, United States, 29-Jun-2009–02-Jul-2009)
CRC Press
2009
DOI: https://doi.org/10.1201/9780203865286
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https://doi.org/10.1201/9780203865286View
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Abstract

Civil Engineering Transportation and Freight Services airport runways California bearing ratio computer models field tests flexible pavements monte carlo method pavement design simulation subgrade (pavements) taxiways thickness
Whilst most pavement design input parameters are stochastic in nature, pavement thickness design remains deterministic, with the designer responsible for the selection of a single value of each parameter to represents the aggregate of all the variable values over the design life. Whilst pavement thickness design has been proven by field performance over many years to be highly reliable, the actual reliability of the deterministic methods was not known. Through Monte Carlo type simulation and comparison to deterministic analytical values, the reliability of the traditional approach to thickness design of aircraft pavements was demonstrated to be in the order of 92% reliable. Such high reliability resulted from the adoption of the 5 percentile CBR test result for conversion to the design subgrade modulus. If other common CBR value selection methods are used, the reliability changes significantly. The methodology presented could be combined with current mechanistic-empirical design methods to allow routine modeling of thickness design reliability. Significant further improvement in the modeling of pavement thickness reliability could be achieved by investigation and development of more complex models for the various design input parameters.

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