MODELS FOR PAVEMENT DETERIORATION USING LTPP

As pavement condition grows to be one of the crucial problems facing the national highway system, a new challenge emerges in developing pavement deterioration prediction models that are reliable yet easily applicable by highway pavement management systems in state departments of transportation and other agencies. The significant contribution of the research presented in this report lies in the fact that it utilizes the most comprehensive database of pavement conditions that is readily available and promises to provide the sought data in future years. The long term pavement project (LTTP) database developed by the Federal Highway Administration was chosen to provide the required data of related parameters for the model development. The first part of this report reviews the existing literature covering related topics including pavement roughness, the LLTP background, artificial neural networks, regression analysis and the existing pavement deterioration models. The second part discusses the work done in data analysis and data manipulation in addition to the development of the training of the neural network model. The third part deals with various aspects of the model development using neural networks and regression analysis. The next part concludes the research with summarizing the results of model development. The models developed in this research are then compared to some existing models by applying the models to similar data sets and performing statistical analysis of the results. The neural network and linear regression models are shown to perform better than any other pavement deterioration model studied in this report. Recommendations for future research areas are suggested.

• Record URL:
  http://cait.rutgers.edu/files/FHWA-NJ-1999-030.pdf
• 
• Supplemental Notes:
  • This research was supported by a grant from the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Rutgers University, Piscataway

  Department of Civil and Environmental Engineering, 623 Bowser Road
  Piscataway, NJ  United States  08854

  New Jersey Department of Transportation

  1035 Parkway Avenue
  Trenton, NJ  United States  08625
• Authors:
  • Ozbay, Kaan
  • Laub, R
• Publication Date: 2001-10

Language

• English

Media Info

• Features: Appendices; Figures; References; Tables;
• Pagination: 146 p.

Subject/Index Terms

• TRT Terms: Databases; Deterioration; Highway maintenance; Linear regression analysis; Literature reviews; Mathematical models; Mathematical prediction; Neural networks; Pavement maintenance; Pavement management systems; Roughness
• Identifier Terms: U.S. Federal Highway Administration
• Subject Areas: Data and Information Technology; Highways; Pavements; I23: Properties of Road Surfaces;

Filing Info

• Accession Number: 00941196
• Record Type: Publication
• Report/Paper Numbers: FHWA-NJ-1999-030,, Final Report
• Files: TRIS, ATRI, STATEDOT
• Created Date: Apr 18 2003 12:00AM