MDOT Pavement Management System : Prediction Models and Feedback System

Details

• Alternative Title:
  Prediction Models and Feedback System
• Creators:
  George, K. P.
• Corporate Creators:
  University of Mississippi. Dept. of Civil Engineering
• Corporate Contributors:
  Mississippi. Dept. of Transportation. Research Division ; United States. Federal Highway Administration
• Subject/TRT Terms:
  • Mathematical prediction
  • Statistical analysis
  • Pavement performance
  • Pavement distress
  • Pavement management systems
  • Data files
  • Feedback control
  • Prediction model
  • Performance model
  • Distress model
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• Publication/ Report Number:
  FHWA/MS-DOT-RD-00-119
• Resource Type:
  Tech Report
• Geographical Coverage:
  Mississippi
• Edition:
  Final; Aug. 1995-Oct. 2000.
• Corporate Publisher:
  Mississippi. Dept. of Transportation
• 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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• Format:
  PDF
• Funding:
  State Study 119
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24129/dot_24129_DS1.pdf
• File Type:
  [PDF - 680.08 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:5030e7bc4b04613a26074bd2e1bd67477e6d44a65c5e3dbb261be924ecf7d05aab4b7229056f9f32cee0a3d551f82b28d6256f132271775de40c8c411836e13f