Development of a Pavement Maintenance Management System. Volume 9. Development of Airfield Pavement Performance Prediction Models.

Abstract

Extensive data were collected from 327 airfield pavement features at 12 U.S. Air Force bases. The data, which provided a wide range of information on designs, materals, traffic, and climate, were used to develop PCI and key distress prediction models for both asphalt-concrete and jointed-concrete-surfaced pavements. Four satisfactory models were developed for predicting PCI for PCC and AC/PCC pavements, corner breaks in PCC pavements, and reflection cracking in AC/PCC pavements. Additional data were collected from 101 airfield pavement features at five of the Air Force bases originally surveyed to evaluate the four prediction models. The evaluation showed that the PCI prediction models are satisfactory. The reflection cracking model also provided reasonable prediction of eight pavement features. However, verification of the corner break model showed that it has a high standard deviation of prediction. Evaluation of the models for each of the five bases showed that predictions for some of the bases were much better than others, possibly because some of the material properties, climatic factors, and traffic conditions in certain bases were not well represented in the overall model. Thus, it was concluded that localized modeling could provide much more accurate predictions.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1984
Accession Number
ADA146150

Entities

People

  • G. R. Nelson
  • J. M. Becker
  • M. Y. Shahin
  • S. D. Kohn

Organizations

  • Construction Engineering Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Counter WMD

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Aircrafts
  • California
  • Computer Programs
  • Concrete
  • Construction
  • Data Science
  • Databases
  • Engineering
  • Granular Materials
  • Information Science
  • Maintenance Management
  • Predictive Modeling
  • Regression Analysis
  • Statistical Data
  • Surveys

Readers

  • Computational Modeling and Simulation
  • Facility/Structural Engineering.