Interpreting FWD Tests of Curled and/or Warped PCC Pavements

Principal Investigator(s):

Mark Snyder, Former University Researcher, Civil, Environmental and Geo-Engineering

Project summary:

Falling Weight Deflectometer (FWD) tests are used to evaluate the performance and structural condition of concrete pavements in order to better manage pavement networks and develop future pavement designs. However, both moisture and temperature gradients in the pavement can affect the results of FWD tests. The objective of this research is to improve the structural evaluation of concrete pavements using Falling Weight Deflectometer (FWD) test results by determining the effects of moisture and temperature gradients on the behavior of concrete pavements subjected to dynamic loads. This goal will be accomplished through a combination of field-testing, laboratory testing and analytical work. FWD test data will be collected from selected concrete pavement test sections at MnROAD during times of day and seasons that cover the range of expected temperature and moisture conditions in Minnesota. The FWD measurements will be computer-modeled and the computed stresses/strains will be compared with those measured during testing using the MnROAD instrumentation. MnROAD concrete specimens will be obtained and tested to determine their temperature and moisture-related properties (e.g., coefficient of thermal expansion, elastic modulus, etc,). These values will then be analyzed to determine expected temperature and moisture gradients in the various pavement sections. Measured pavement stresses (due to temperature and moisture effects as well as combined stresses that result from the addition of vehicle loads) will be compared with those obtained by analyzing the FWD data. Selected structural and climatic models will be used to provide the best interpretation of the pavement response to vehicle and environmental loads. A procedure will then be developed to assist in interpreting FWD tests of PCC pavements subjected to temperature and moisture gradients. This will complete the first year of work. The second year will be spent in expanding the study across the state, verifying/calibrating the proposed model, and developing an implementation guide.

Project details: