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Framework for incorporating rutting prediction model in the reliability-based design of flexible pavements.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Framework for incorporating rutting prediction model in the reliability-based design of flexible pavements.
作者:
Kim, Hyung-Bae.
面頁冊數:
143 p.
附註:
Adviser: Neeraj J. Buch.
附註:
Source: Dissertation Abstracts International, Volume: 60-06, Section: B, page: 2832.
Contained By:
Dissertation Abstracts International60-06B.
標題:
Engineering, Civil.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9936567
ISBN:
0599376295
Framework for incorporating rutting prediction model in the reliability-based design of flexible pavements.
Kim, Hyung-Bae.
Framework for incorporating rutting prediction model in the reliability-based design of flexible pavements.
[electronic resource] - 143 p.
Adviser: Neeraj J. Buch.
Thesis (Ph.D.)--Michigan State University, 1999.
Based on the probabilistic techniques introduced in the reliability analysis of pavement performance, two practical reliability-based M-E flexible pavement design procedures are introduced; Reliability Factor Design (RFD) approach and Load and Resistance Factor Design (LRFD) approach. In order to characterize the uncertainties of the design procedure, the RFD approach employs the overall standard deviation that is the first order combination of the standard deviations due to uncertainties of design parameters and systematic errors in the design model, while the LRFD approach considers the overall safety factor based on the partial safety factors of all design variables and a professional factor, which is a ratio of measured to predicted rut-depth. Both approaches use an iterative algorithm where the computation is continued until the limit-state function converges to zero in order to produce an optimal pavement cross-section.
ISBN: 0599376295Subjects--Topical Terms:
212394
Engineering, Civil.
Framework for incorporating rutting prediction model in the reliability-based design of flexible pavements.
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Based on the probabilistic techniques introduced in the reliability analysis of pavement performance, two practical reliability-based M-E flexible pavement design procedures are introduced; Reliability Factor Design (RFD) approach and Load and Resistance Factor Design (LRFD) approach. In order to characterize the uncertainties of the design procedure, the RFD approach employs the overall standard deviation that is the first order combination of the standard deviations due to uncertainties of design parameters and systematic errors in the design model, while the LRFD approach considers the overall safety factor based on the partial safety factors of all design variables and a professional factor, which is a ratio of measured to predicted rut-depth. Both approaches use an iterative algorithm where the computation is continued until the limit-state function converges to zero in order to produce an optimal pavement cross-section.
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In the reliability analysis of pavement performance, a model to accurately evaluate the reliability of flexible pavement structure is introduced by incorporating practical engineering probabilistic techniques. The uncertainties associated with the design procedure are categorized into four parts based on the sources of uncertainty: spatial variation, imprecision in quantifying site conditions and traffic, model bias, and statistical error. The first two are integrated into uncertainties of design parameters; the latter two into uncertainties that stem from systematic errors.
520
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Most state highway agencies (SHA's) are being encouraged to change their design procedures for flexible pavements from an empirical-based procedure such as the AASHTO guide to mechanistic-based approaches. A successful implementation of mechanistic-empirical (M-E) pavement design requires (i) the development of robust transfer functions to accurately predict pavement performance, and (ii) the characterization and control of the uncertainties associated with M-E design procedures, including variations in material and cross-sectional properties, inaccuracy of traffic estimation, and model bias.
520
#
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The new rutting prediction model has a nonlinear form developed by considering the combination of mechanistic factors such as primary pavement responses and empirical factors such as pavement material and cross-sectional properties and environmental components. The model is validated using data from eleven in-service flexible pavements and the data from twenty four general pavement sections from the Long Term Pavement Performance (LTPP-GPS) database.
520
#
$a
This study presents the procedure and results of calibrating the existing rutting prediction model in MICHPAVE, a computer program for linear and non-linear finite element analysis of the pavement structure, using field data from thirty-nine in-service flexible pavement sections in Michigan, and a framework for a reliability-based M-E flexible pavement design approach using the calibrated rutting prediction model.
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