Artificial Neural Networks for Predicting the Response of Unbonded Concrete Overlays in a Faulting Prediction Model

Transverse joint faulting is a common distress in unbonded concrete overlays (UBOLs). However, the current faulting model in Pavement mechanistic-empirical (ME) is not suitable for accurately predicting the response of UBOLs. Therefore, to develop a more accurate faulting prediction model for UBOLs, the first step was to develop a predictive model that would be able to predict the response (deflections) of these structures. To account for the conditions unique to UBOLs, a computational model was developed using the pavement-specific finite element program ISLAB, to predict the response of these structures. The model was validated using falling weight deflectometer (FWD) data from existing field sections at the Minnesota Road Research Facility (MnROAD) as well as sections in Michigan. A factorial design was performed using ISLAB to efficiently populate a database of fictitious UBOLs and their responses. The database was then used to develop predictive models, based on artificial neural networks (ANNs), to rapidly estimate the structural response of UBOLs to environmental and traffic loads. The structural response can be related to damage through the differential energy concept. Future work will include implementation of the ANNs developed in this study into a faulting prediction model for designing UBOLs.

• Record URL:
  • https://doi.org/10.1177/0361198119850466
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • The Standing Committee on General and Emerging Pavement Design (AFD30) peer-reviewed this paper (19-03251). © National Academy of Sciences: Transportation Research Board 2019.
• Authors:
  • DeSantis, John W
  • Vandenbossche, Julie M
  • Sachs, Steven G
• Publication Date: 2019-10

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: pp 762-769
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2673
  • Issue Number: 10
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Composite pavements; Concrete overlays; Concrete pavements; Fault location; Neural networks; Pavement distress; Structural analysis; Transverse joints
• Identifier Terms: ISLAB (Computer model); Minnesota Road Research Facility
• Geographic Terms: Michigan; Minnesota
• Subject Areas: Highways; Pavements; Planning and Forecasting;

Filing Info

• Accession Number: 01711235
• Record Type: Publication
• Report/Paper Numbers: 19-03251
• Files: TRIS, TRB, ATRI
• Created Date: Jul 17 2019 5:51PM