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Improved Reliability of FWD Test Results and Correlations With Resilient Modulus

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    Final Report
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    Resilient modulus (MR) is a key factor in the Mechanistic Empirical Pavement Design Guide (MEPDG) which was adopted by INDOT in January 2009. The resilient modulus can be determined in new pavement projects from subgrade soil samples collected at the site. However, for a pavement rehabilitation project, it becomes difficult to obtain soil information, and coring for samples may not be feasible because of traffic. The literature is rich with correlations between the MR of the subgrade obtained in the laboratory with that estimated from FWD tests in the field. However, the review conducted shows very contradictory and inconsistent findings, thus existing correlations seem to apply only to the cases investigated, i.e., they are ad hoc correlations and cannot be generalized. To improve the interpretation of the FWD data and enhance the reliability of the results in Indiana, FWD and GPR tests were performed at five different road construction projects at the same locations where soil samples were collected and tested in the laboratory for resilient modulus. The selected sites included roads with rigid pavement and treated subgrade and flexible pavement with untreated subgrade. The study showed that (1) FWD backcalculation is greatly affected by pavement thickness; (2) GPR can provide actual thicknesses and can identify discrepancies between as-built and design pavement thickness;(3)for flexible pavements, a one-to-one correlation exists between FWD modulus and laboratory resilient modulus values for untreated subgrade soils; (4) MODTAG or ELMOD codes can both be used to estimate the resilient modulus of the subgrade inflexible pavements; (5) for rigid pavements, results of FWD backcalculation analysis using ELMOD or MODTAG greatly overestimate the resilient modulus of the soil, with back calculated moduli 1.3 to 6 times higher than laboratory results; (6)ELMOD is recommended for routine analysis of FWD data, while MODTAG is recommended for research or to evaluate the quality of the data, when needed.
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    urn:sha256:47d0f38bbbd49cf0af42746995a142e851eb8cdf25f722584087e05cf904bf17
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