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 Coefficients Curves (Geometry) Dynamic Modulus Of Elasticity Falling Weight Deflectometers Mechanistic-empirical Pavement Design Neural Networks Pavement Layers Viscoelasticity
• 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.
  
  
• Format:
  PDF
• Funding:
  InTrans Project 13-471
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:5ddf1da47333219795961ef33480864c0cc26d805fed5fbb152d87a964f433103d99da1cd1ae649921212b98a018bcbd2df775afde0317f683d1bcedb0246db0
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/28113/dot_28113_DS1.pdf
• File Type:
  [PDF - 4.51 MB ]