Cover of Improved Methods of Using Pavement Deflection Data in the Design of Rehabilitation Treatments
Improved Methods of Using Pavement Deflection Data in the Design of Rehabilitation Treatments
  • Publication no: AP-T350-19
  • ISBN: 978-1-925854-49-7
  • Published: 11 October 2019

New methods of using pavement deflection data were developed in the context of pavement structural rehabilitation treatment design. The project focused on two main components. The first was to assess the potential use of traffic speed deflectometer (TSD) deflection for the design of pavement structural treatments. The second component included formalising the pavement layer moduli back-calculation algorithm for use in mechanistic‑empirical pavement treatment design.

Using two databases of paired falling weight deflectometer (FWD) and TSD, a regression analysis of the deflection bowls allowed the determination of a robust relationship between maximum deflections measured by the two devices. The regression analysis did not allow the development of a process to predict deflection bowls equivalent to FWD measured bowls from TSD measured data. However, the regression parameters obtained for maximum deflections formed the basis of a deflection standardisation factor for the TSD proposed for the empirical design method of granular overlay.

The algorithm suitable for the determination of pavement layer moduli from pavement surface deflection is detailed and described in the report based on previously used back-calculation methods. The project further developed the algorithm by adding optional features for the subgrade layer moduli and the sublayering process for granular layers. The proposed algorithm was tested using deflection data from case studies. The benefits of the optional features were also demonstrated.


  • 1.      Introduction
    • 1.1    Background
    • 1.2    Purpose and Scope
    • 1.3    Report Structure
  • 2.      Feasibility of Using TSD Data for the Structural Design of Pavement Treatments
    • 2.1    General
      • 2.1.1      Background and Scope of Work
      • 2.1.2      Methodology
      • 2.1.3      Previous TSD vs FWD Deflection Relationships
    • 2.2    Data and Regression Analysis Method
      • 2.2.1      Paired TSD-FWD Data
      • 2.2.2      Linear Regression Analysis
    • 2.3    Use of TSD Data to Design Granular Overlays
      • 2.3.1      Simplified Model for Sprayed Seal Surfaced Granular Pavements
      • 2.3.2      TSD Deflection Standardised Factor
    • 2.4    Use of TSD Deflection Bowl for Mechanistic Empirical Treatment Design
    • 2.5    Conclusions Related to Using TSD Deflections in Treatment Design
  • 3.      Pavement and Subgrade Modulus Back‑calculation
    • 3.1    Scope
    • 3.2    Development of EfromD
    • 3.3    Back-calculation Algorithm
    • 3.4    Models for Subgrade and Granular Layers
      • 3.4.1      Constraining Subgrade Modulus with Depth
      • 3.4.2      Granular Layers Multilayering Algorithm
  • 4.      Modulus Back-calculation Case Studies
    • 4.1    Introduction
    • 4.2    Input Data
      • 4.2.1      Pavement Layers
      • 4.2.2      Subgrade Materials
      • 4.2.3      Back-calculation Termination Parameters
    • 4.3    Case 1: Asphalt Surfaced Granular Pavement
      • 4.3.1      Pavement Configuration
      • 4.3.2      Deflection Data and Composite Modulus
      • 4.3.3      Back-calculation Input Parameters
      • 4.3.4      Back-calculation Results
    • 4.4    Case 2: Thick Asphalt on Cemented Subbase Pavement
      • 4.4.1      Pavement Configuration
      • 4.4.2      Deflection Data and Composite Modulus
      • 4.4.3      Initial Moduli
      • 4.4.4      Back-calculation Results
      • 4.4.5      Composite Modulus vs Back-calculated Subgrade Moduli
    • 4.5    Selection of Deflection Bowls for Modulus Back-calculation
    • 4.6    Summary of the Findings
  • 5.      Summary and Conclusions
  • References
  • Appendix A          Data Overview and Charts
  • Appendix B          Statistical and Regression Equations
  • Appendix C          Deming Regression for Deflection Bowls
  • Appendix D          Algorithm for Determining the Critical Point
  • Appendix E          Back-calculation Data and Results