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Risk Based Pavement Structural Evaluation and Rehabilitation Design Dr Wei Liu Senior Engineer Fugro PMS Ltd
Background <ul><li>Pavement evaluations are conducted to determine functional and structural conditions of a highway secti...
Background <ul><li>Nondestructive testing has become an integral part of pavement structural evaluation and rehabilitation...
Background <ul><li>Traditional Pavement Structural Evaluation Method </li></ul><ul><ul><li>Modulus of pavement layers is b...
New Method <ul><li>Characterizing Pavement Layer Thickness Variability by GPR Data </li></ul><ul><ul><li>The variability i...
New Method <ul><li>Risk Based Backcalculation Analysis </li></ul><ul><ul><li>A Risked based backcalculation procedure that...
Implementation Example <ul><li>In January 2007, Fugro PMS Ltd was contracted by  Grey District Council  to carry out a FWD...
Implementation Example <ul><li>Pavement Layer Thickness Characterization by GPR Data </li></ul><ul><ul><li>Surface thickne...
Implementation Example <ul><li>Layer modulus distribution for each FWD point by risk based backcalculation analysis  </li>...
Implementation Example <ul><li>Risk based backcalculation analysis results </li></ul><ul><ul><li>There are significant dif...
Implementation Example <ul><li>Risk Based Overlay Design Results </li></ul><ul><ul><li>Significant differences occur at th...
Summary and Conclusion <ul><li>In this paper, a risk-based pavement evaluation methodology was introduced to account for t...
Thank you! If any question or comment, please feel free to contact us [email_address] [email_address] Phone: 07-8470499
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Risk Based Pavement Structural Evaluation And Rehabilitation Design

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The risk concept provides a means of incorporating some degree of certainty into the process to ensure that the outcomes of the process will provide acceptable levels of service until the end of the intended design life. In pavement design and evaluation, the risk concept is applicable for the input parameters with a high degree of uncertainty and that have an impact on the final outcome of the design process. The 2004 AUSTROADS Pavement Design Guide emphasized that much of the misunderstanding of pavement design, and resulting pavement failures over the past 20 years has been associated with uncertainty and resulting lack of reliability in design. Pavement structural evaluation and rehabilitation designs are highly dependent on the in-situ layer properties. Pavement layer thickness is an essential input in backcalculation analysis performed on measured surface deflections by Falling Weight Deflectometer (FWD) survey. Inaccurate layer thickness information may lead to significant errors in the backcalculated layer moduli and hence in the rehabilitation design. Since the pavement layer thickness has some degree of variability, it is important to consider this variability in the backcalculation analysis and rehabilitation design. In this paper, a risk-based pavement evaluation methodology will be introduced to account for the variability of pavement layer thickness through integration of FWD and GPR data. It is be demonstrated that the proposed methodology can help RCAs more accurately assess the pavement structural condition of road network with more confidence. The proposed procedure is also applicable in project level for the construction acceptance testing of new or rehabilitation pavement.

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Transcript of "Risk Based Pavement Structural Evaluation And Rehabilitation Design"

  1. 1. Risk Based Pavement Structural Evaluation and Rehabilitation Design Dr Wei Liu Senior Engineer Fugro PMS Ltd
  2. 2. Background <ul><li>Pavement evaluations are conducted to determine functional and structural conditions of a highway section either for purposes of routine monitoring or planned corrective action. </li></ul><ul><ul><li>Functional evaluation is primarily concerned with the ride quality or surface texture of a highway section. </li></ul></ul><ul><ul><li>Structural evaluation is concerned with the structural capacity of the pavement as measured by deflection, layer thickness, and material properties. </li></ul></ul><ul><li>At the network level, routine evaluations can be used to develop performance models and prioritize maintenance or rehabilitation efforts and funding. </li></ul><ul><li>At the project level, evaluations are more focused on establishing the root causes of existing distress in order to determine the best rehabilitation strategies. </li></ul>
  3. 3. Background <ul><li>Nondestructive testing has become an integral part of pavement structural evaluation and rehabilitation strategies in recent years. </li></ul><ul><li>Deflection testing using the Falling Weight Deflectometer (FWD) is commonly used to evaluate in-situ pavement structural capacity. </li></ul><ul><li>Ground Penetrating Radar (GPR) is an accepted electromagnetic evaluation technique used for the transportation infrastructure. The use of GPR for measuring pavement thickness is a well established and verified technique. </li></ul>
  4. 4. Background <ul><li>Traditional Pavement Structural Evaluation Method </li></ul><ul><ul><li>Modulus of pavement layers is back calculated. </li></ul></ul><ul><ul><li>Stresses and strains due to design load are calculated. </li></ul></ul><ul><ul><li>Material characteristics are estimated. </li></ul></ul><ul><ul><li>Remaining life is calculated. </li></ul></ul><ul><ul><li>Overlay design. </li></ul></ul><ul><li>Drawbacks of Traditional Method </li></ul><ul><ul><li>Only peak values of load and deflections are used. </li></ul></ul><ul><ul><li>Linear elastic theory is applied. </li></ul></ul><ul><ul><li>Spot analysis. </li></ul></ul><ul><ul><li>How useful is information on moduli if thickness shows large amount of variation. </li></ul></ul><ul><ul><li>Variability is hardly taken into account. </li></ul></ul>
  5. 5. New Method <ul><li>Characterizing Pavement Layer Thickness Variability by GPR Data </li></ul><ul><ul><li>The variability in pavement layer characteristics (thickness and material) can be classified as high and low. High variability in layer characteristics is addressed in the sectioning process. In this process, long pavement segments are divided into a set of structurally homogeneous sections. </li></ul></ul><ul><ul><li>Assume that GPR pavement layer thickness data from homogeneous sections are normally distributed. </li></ul></ul><ul><ul><li>Characteristics of layer thickness values can be represented by their mean and standard division values. </li></ul></ul>
  6. 6. New Method <ul><li>Risk Based Backcalculation Analysis </li></ul><ul><ul><li>A Risked based backcalculation procedure that accounts for the normal variability in layer thickness within the structurally homogeneous pavement sections. </li></ul></ul><ul><ul><li>It is applicable for both network level and project level analysis. </li></ul></ul>
  7. 7. Implementation Example <ul><li>In January 2007, Fugro PMS Ltd was contracted by Grey District Council to carry out a FWD survey survey of 1460 points of the Haul Route in grey District. All FWD testing was undertaken using a target loading of 40kN and staggered at 50m along the roadway. </li></ul><ul><li>Pavement layer thickness data from a GPR survey conducted by Aperio West Pacific Limited on November 2005 for Grey District Council . </li></ul><ul><li>A 1200m long road from Taylorville Road was selected because it is considered as a homogenous section from its construction history and field performance. </li></ul>
  8. 8. Implementation Example <ul><li>Pavement Layer Thickness Characterization by GPR Data </li></ul><ul><ul><li>Surface thickness is between 0.05m to 0.3m and the base thickness is between 0.1m to 0.5m. </li></ul></ul><ul><ul><li>Both surface thickness results and base thickness results follow the normal distribution. </li></ul></ul><ul><ul><li>The mean and standard derivation of surface thickness were calculated as 0.1875m and 0.0554m and the mean and standard derivation of base thickness were calculated as 0.24898m and 0.0896m. </li></ul></ul>
  9. 9. Implementation Example <ul><li>Layer modulus distribution for each FWD point by risk based backcalculation analysis </li></ul><ul><ul><li>For each FWD test point, 100 pavement layer thickness profiles with different combinations of surface thickness and base thickness were generated. </li></ul></ul><ul><ul><li>These pavement profiles were then used in the backcalculation analysis to derive distribution of layer modulus for each FWD test point. </li></ul></ul><ul><ul><li>The backcalculated pavement layer modulus from the same FWD test point shows quite large variation. Therefore, it would lead to big errors for the modulus results from traditional deterministic backcalculation methods if the pavement layer thickness information is not accurate. </li></ul></ul>
  10. 10. Implementation Example <ul><li>Risk based backcalculation analysis results </li></ul><ul><ul><li>There are significant differences for the backcalculated pavement layer modulus results at different risk level. For surface modulus, the differences between 90% risk level and 10% risk level can be as high as 500% and for the base modulus, the differences between 90% risk level and 10% risk level can be as high as 200%. </li></ul></ul><ul><ul><li>The traditional deterministic backcalculation method produces similar results to those at the risk level of 50%, which have up to 250% difference for surface modulus and 100% difference for base modulus by comparing to 10% (or 90%) risk level. </li></ul></ul>
  11. 11. Implementation Example <ul><li>Risk Based Overlay Design Results </li></ul><ul><ul><li>Significant differences occur at the required overlay thicknesses at different risk level. For the example section, there is an average of 200mm differences between the required overlay thickness at 90% Risk and those at 10% Risk. </li></ul></ul><ul><ul><li>Therefore, it is important for the pavement engineer to select the appropriate level of risk when doing pavement rehabilitation design. </li></ul></ul>
  12. 12. Summary and Conclusion <ul><li>In this paper, a risk-based pavement evaluation methodology was introduced to account for the variability of pavement layer thickness through integration of FWD and GPR data. </li></ul><ul><li>GPR data was analyzed to derive the statistic parameters such as mean and standard derivation of pavement layer thickness. </li></ul><ul><li>These statistic values of pavement layer thickness was further used to generate the distribution of pavement layer thickness profiles, which was used in the risk based pavement backcalculation to compute the modulus distribution for each FWD test points. </li></ul><ul><li>By using the risk based pavement backcalculation results, pavement rehabilitation design can be carried out to determine the required overlay thickness at different risk level. </li></ul><ul><li>It is be demonstrated that the proposed methodology can help RCAs more accurately assess the pavement structural condition at both network network level and project level. </li></ul>
  13. 13. Thank you! If any question or comment, please feel free to contact us [email_address] [email_address] Phone: 07-8470499

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