Measuring Pavement Profile at the Slip-Form Paver, May 2005

Pavement profile or smoothness has been identified nationally as a good measure of highway user satisfaction. This has led highway engineers to measure profiles of both operating and new highways. Operational highway profiles are often measured with high-speed inertial profilers. New highway profiles are usually measured with profilographs in order to establish incentives or disincentives for pavement construction. In most cases, these two processes do not measure the same value from the “cradle to grave” life of pavements. In an attempt to correct the inconsistency between measuring techniques, lightweight profilers intended to produce values to be used for construction acceptance are being made that measure the same profile as high-speed inertial profilers. Currently, two profiler systems have been identified that can measure pavement profile during construction. This research has produced a field evaluation of the two systems. The profilers evaluated in this study are able to detect roughness in the final profile, including localized roughness and roughness at joints. Dowel basket ripple is a significant source of pavement surface roughness. The profilers evaluated in this study are able to detect dowel basket ripple with enough clarity to warn the paving crew. String-line disturbances degrade smoothness. The profilers evaluated in this study are able to detect some string-line disturbances during paving operations. The profilers evaluated in this study are not currently able to produce the same absolute International Roughness Index (IRI) values on the plastic concrete that can be measured by inertial profilers on the hardened concrete. Construction application guidelines are provided.

Development of Asphalt Dynamic Modulus Master Curve Using Falling Weight Deflectometer Measurements, TR-659

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.