Non-Corrosive Tie Reinforcing and Dowel Bars for Highway Pavement Slabs, HR-343, 1993

The use of non-metallic load transfer and reinforcement devices for concrete highway pavements is a possible alternative to avoid corrosion problems related to the current practice of steel materials. Laboratory and field testing of highway pavement dowel bars, made of both steel and fiber composite materials, and fiber composite tie rods were carried out in this research investigation. Fatigue, static, and dynamic testing was performed on full-scale concrete pavement slabs which were supported by a simulated subgrade and which included a single transverse joint. The bahavior of the full-scale specimens with both steel and fiber composite dowels placed in the test joints was monitored during several million load cycles which simulated truck traffic at a transverse joint. Static bond tests were conducted on fiber composite tie rods to determine the required embedment length. These tests took the form of bending tests which included curvature and shear in the embedment zone and pullout tests which subjected the test specimen to axial tension only. Fiber composite dowel bars were placed at two transverse joints during construction of a new concrete highway pavement in order to evaluate their performance under actual field conditions. Fiber composite tie rods were also placed in the longitudinal joint between the two fiber composite doweled transverse joints.

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.