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MDOT Pavement Management System : Prediction Models and Feedback System
  • Published Date:
    2000-10-01
  • Language:
    English
Filetype[PDF-680.08 KB]


Details:
  • Alternative Title:
    Prediction Models and Feedback System
  • Creators:
  • Publication/ Report Number:
  • Resource Type:
  • Geographical Coverage:
  • Edition:
    Final; Aug. 1995-Oct. 2000.
  • NTL Classification:
    NTL-HIGHWAY/ROAD TRANSPORTATION-Materials ; NTL-HIGHWAY/ROAD TRANSPORTATION-Pavement Management and Performance ;
  • Abstract:
    As a primary component of a Pavement Management System (PMS), prediction models are crucial for one or more of the following analyses: maintenance planning, budgeting, life-cycle analysis, multi-year optimization of maintenance works program, and authentication of design alternatives. The main focus of the study is to develop pavement deterioration models. Four cycles of pavement condition data and the required inventory data are compiled from the Mississippi Department of Transportation (MDOT) PMS database. Though regression is the primary tool for developing models, Bayesian regression is also employed whenever feasible. Expert opinion regarding the major distresses in pavements are compiled, augmenting the field data. The study begins with a review of relevant literature with the aim of identifying the commonly employed explanatory variables and various model forms. Five pavement families are identified for the model development: original flexible, overlaid flexible, composite, jointed concrete, and continuously reinforced concrete pavements. Models for each family are developed for predicting distresses, roughness, and a composite condition index (Pavement Condition Rating). The database employed is divided into ‘in-sample’ data constituting a major portion (70 percent) with ‘out-of-sample’ data comprising the remaining. Totally 26 models are developed, with the in-sample data: six each for original flexible, overlaid flexible, and composite, and four each for jointed concrete and continuously reinforced concrete pavements. The models are subsequently verified with the ‘outof- sample’ data. Among the scores of model forms attempted, power form or some variation of it fits all of the models while satisfying crucial boundary conditions. The out-of-sample data provides an independent database to verify the validity of the models. A sensitivity analysis of the model equation is presented in each case, substantiating the predictive capability of the model. In seven cases, incorporating expert opinion in the field data, employing Bayesian regression, resulted in better prediction models. While these equations form a nucleus for condition prediction of MDOT pavement network, for project level analyses, a shift adjustment of the prediction should be made to match the current observation. The feedback program developed in this study computes load index of original pavements of all types and overlaid flexible pavements. Load index is the ratio of the actual ESAL sustained by the pavement and the design ESAL. Also included is a routine to verify/substantiate the prediction models by comparing the actual to the predicted distresses
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