Development of asphalt dynamic modulus master curve using falling weight deflectometer measurements.

Details

• Creators:
  Gopalakrishnan, Kasthurirangan ; Kim, Sunghwan ; Ceylan, Halil ; Kaya, Orhan
• Corporate Creators:
  Iowa State University. Institute for Transportation
• Corporate Contributors:
  Iowa. Dept. of Transportation ; Iowa. Highway Research Board
• Subject/TRT Terms:
  • Asphalt concrete
  • Dynamic modulus of elasticity
  • Mechanistic-empirical pavement design
  • Curves (Geometry)
  • Falling weight deflectometers
  • Neural networks
  • Viscoelasticity
  • Coefficients
  • Pavement layers
• Publication/ Report Number:
  IHRB Project TR-659
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Iowa State University. Institute for Transportation
• Abstract:
  The asphalt concrete (AC) dynamic modulus (|E*|) is a key design parameter in mechanistic-based pavement design ; methodologies such as the American Association of State Highway and Transportation Officials (AASHTO) MEPDG/Pavement-ME Design. The objective of this feasibility study was to develop frameworks for predicting the AC |E*| master curve from ; falling weight deflectometer (FWD) deflection-time history data collected by the Iowa Department of Transportation (Iowa ; DOT). A neural networks (NN) methodology was developed based on a synthetically generated viscoelastic forward solutions ; database to predict AC relaxation modulus (E(t)) master curve coefficients from FWD deflection-time history data. According to ; the theory of viscoelasticity, if AC relaxation modulus, E(t), is known, |E*| can be calculated (and vice versa) through numerical ; inter-conversion procedures. Several case studies focusing on full-depth AC pavements were conducted to isolate potential ; backcalculation issues that are only related to the modulus master curve of the AC layer. For the proof-of-concept demonstration, ; a comprehensive full-depth AC analysis was carried out through 10,000 batch simulations using a viscoelastic forward analysis ; program. Anomalies were detected in the comprehensive raw synthetic database and were eliminated through imposition of ; certain constraints involving the sigmoid master curve coefficients. ; The surrogate forward modeling results showed that NNs are able to predict deflection-time histories from E(t) master curve ; coefficients and other layer properties very well. The NN inverse modeling results demonstrated the potential of NNs to ; backcalculate the E(t) master curve coefficients from single-drop FWD deflection-time history data, although the current ; prediction accuracies are not sufficient to recommend these models for practical implementation. Considering the complex nature ; of the problem investigated with many uncertainties involved, including the possible presence of dynamics during FWD testing ; (related to the presence and depth of stiff layer, inertial and wave propagation effects, etc.), the limitations of current FWD ; technology (integration errors, truncation issues, etc.), and the need for a rapid and simplified approach for routine ; implementation, future research recommendations have been provided making a strong case for an expanded research study.
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• Format:
  PDF
• Funding:
  InTrans Project 13-471
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/28113/dot_28113_DS1.pdf
• File Type:
  [PDF - 4.51 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:5ddf1da47333219795961ef33480864c0cc26d805fed5fbb152d87a964f433103d99da1cd1ae649921212b98a018bcbd2df775afde0317f683d1bcedb0246db0