Development of Asphalt Dynamic Modulus Master Curve Using Falling Weight Deflectometer Measurements, TR-659 Gopalakrishnan, Kasthurirangan Kim, Sunghwan Ceylan, Halil Ceylan, Halil Kaya, Orhan
dc.contributor.department Institute for Transportation 2018-02-15T03:03:02.000 2020-06-30T04:51:23Z 2020-06-30T04:51:23Z 2014-10-14 2014-06-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>See also the related 3-page Tech Transfer Summary under the same title.</p>
dc.format.mimetype PDF
dc.identifier archive/
dc.identifier.articleid 1068
dc.identifier.contextkey 6235389
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath intrans_reports/69
dc.language.iso English
dc.relation.ispartofseries IHRB Project TR-659
dc.source.bitstream archive/|||Sat Jan 15 01:30:15 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.keywords Asphalt concrete
dc.subject.keywords Dynamic modulus of elasticity
dc.subject.keywords Falling weight deflectometers
dc.subject.keywords Hot mix asphalt
dc.subject.keywords neural networks
dc.subject.keywords Viscoelasticity
dc.subject.keywords FWD
dc.subject.keywords HMA
dc.subject.keywords mater curve
dc.subject.keywords relaxation modulus
dc.title Development of Asphalt Dynamic Modulus Master Curve Using Falling Weight Deflectometer Measurements, TR-659
dc.type article
dc.type.genre report
dspace.entity.type Publication
relation.isAuthorOfPublication 3cb73d77-de43-4880-939a-063f9cc6bdff
relation.isOrgUnitOfPublication 0cffd73a-b46d-4816-85f3-0f6ab7d2beb8
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