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An accurate measurement or estimation of dynamic modulus (|E*|) of a hot mix asphalt (HMA) mix is important to understand stress-strain behavior of flexible pavements under loading and unloading conditions. The Mechanistic Empirical Pavement Design Guide (MEPDG) (Transportation Research Board, Washington, D.C., 2004) recommends that |E*| be used in all three levels of design (i.e., Level 1, Level 2, and Level 3). For Level 1, |E*| is measured in the laboratory, while it is estimated using the Witczak models for Level 2 and Level 3 designs. The measurement of |E*| in the laboratory is not always feasible because it requires costly equipment and skilled personnel. Consequently, use of empirical models seems to be a reasonable approach to estimate |E*|. Several researchers have reported that the accuracy of the Witczak models varies with local materials and volumetric properties. The present study was undertaken to compare the measured and the estimated |E*| for some commonly used mixes in Oklahoma. Specifically, |E*| of five different HMA mixes, comprised of aggregates from several sources and sizes, binder grades, and air voids, were measured in the laboratory. The Witczak 1999 model (Andrei , 1999) was used to estimate |E*| for each of these mixes. A comparison was made between the measured and the estimated |E*| at four different levels of air voids, namely, 6%, 8%, 10%, and 12%. It was observed that the Witczak 1999 model overestimates |E*| at all four levels of air voids. To address these overestimates, the Witczak 1999 model was calibrated. The calibrated model was similar in form to the Witczak 1999 model but having different numerical coefficients. Verification of this model was done using a mix that was not used in the calibration process. Furthermore, two full depth field cores were obtained to further verify the accuracy of the calibrated model. Three different criteria, namely, goodness-of-fit statistics, matching the measured and the estimated |E*|, and average relative error (%), revealed that the calibrated model exhibits much better performance compared to the Witczak 1999 model. It is expected that the calibrated model would be useful in estimating |E*| for the Level 2 and Level 3 designs for the implementation of the MEPDG in Oklahoma.
TRB's National Cooperative Highway Research Program (NCHRP) Report 752: Improved Mix Design, Evaluation, and Materials Management Practices for Hot Mix Asphalt with High Reclaimed Asphalt Pavement Content describes proposed revisions to the American Association of State Highway and Transportation Officials (AASHTO) R 35, Superpave Volumetric Design for Hot Mix Asphalt, and AASHTO M 323, Superpave Volumetric Mix Design, to accommodate the design of asphalt mixtures with high reclaimed asphalt pavement contents.
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The in-situ dynamic modulus properties of asphalt mixture play a significant role in assessing pavement mechanical responses under traffic loading, determining the pavement performance and condition, and making optimized maintenance decisions. Several methods, such as the falling weight deflectometer (FWD), have been utilized as a non-destructive test to back-calculate the in-situ pavement modulus and conditions; however, the FWD test can only be performed periodically and has the disadvantage of disturbing traffic due to lane-closure needs. With the recent advancement in data science and sensing technologies, the application of micro-electromechanical system (MEMS) sensors and machine learning techniques in pavement nondestructive tests has attracted more research attention. This research aims to develop an in-situ evaluation system that can automatically collect, process, and interpret data to determine the in-situ dynamic modulus of the asphalt mixture under traffic loads using embedded wireless sensors and machine learning techniques. The proposed system is a self-adaptive process and can predict in-situ dynamic modulus based only on mechanical responses and environmental conditions. Ultimately, the well-trained predictive model can be integrated into the pavement management system for the automated and cost-effective assessment of pavement conditions, facilitating informed decision-making. The research program encompasses three types of dynamic modulus experiments: laboratory uniaxial dynamic modulus tests, the one-third scale model mobile load simulator (MMLS3) tests, and in-situ dynamic modulus tests. Particle-size wireless sensors, SmartKli sensors, were implemented in the laboratory specimens and the pavements to collect data from sine wave loads and moving loads. Finite element models (FEM) were also developed and calibrated to generate pavement mechanical response data for more pavement types. The collected data and the FEM simulations were integrated into a database for a proposed adaptive data processing procedure. In addition, because the data collected by embedded sensors in infrastructure health monitoring are inevitably contaminated with noise, and the data features have a distinct discrepancy in different types of tests, a secondary objective of this research is to propose a data processing method capable of removing noises, recognizing data feature discrepancies, and extracting hidden features. An adaptive data processing procedure was developed by combining an empirical mode decomposition (EMD) method and an intrinsic mode function (IMF) selection processing to enhance the reliability of the pavement dynamic modulus prediction. Different EMD techniques were applied to decompose signals from wireless sensors embedded in the pavements. The maximum normalized cross-correlation (MNCC) and signal noise ratio (SNR) were selected as indices in the K-means classification to select the effective IMFs. The results indicated that ensemble EMD (EEMD) and multivariant EMD (MEMD) methods can extract more information from the mechanical responses and extend data dimensions. The EEMD method gives the lowest mean relative error (MRE). Therefore, the EEMD method was recommended for infrastructure data processing. The K-means method can adaptively select the effective IMFs based on the MNCC and SNR. Finally, three dynamic modulus predictive models were developed for different situations. An artificial neural network (ANN) model was developed based on the laboratory test data. This model verified that the ANN model can predict in-situ dynamic modulus. The second dynamic modulus predictive model was developed using the ensemble ANN model to improve the stability of the ANN model, which was trained and tested by the data collected from the MMLS3 test. The third model was developed to predict the dynamic modulus of various asphalt mixtures by fusing a transfer learning approach and Transformer architecture. Besides, the training database was extended with the FEM simulations. The results indicated that the ensemble ANN model is feasible and robust in predicting the dynamic modulus of the asphalt mixture in the MMLS3 test. The transfer learning model is reasonable and robust in predicting the in-situ dynamic modulus of the asphalt pavement.
This project evaluated the procedures proposed by the Mechanistic-Empirical Pavement Design Guide (MEPDG) to characterize existing hot-mix asphalt (HMA) layers for rehabilitation purposes. Thirty-three cores were extracted from nine sites in Virginia to measure their dynamic moduli in the lab. Falling-weight deflectometer (FWD) testing was performed at the sites because the backcalculated moduli are needed for the Level 1 procedure. The resilient modulus was also measured in the lab because it is needed for the Level 2 procedure. A visual pavement rating was performed based on pavement condition because it is needed for the Level 3 procedure. The selected cores were tested for their bulk densities (Gmb) using the AASHTO T166 procedure and then for their dynamic modulus in accordance with the AASHTO TP62-03 standard test method. Then the cores were broken down and tested for their maximum theoretical specific gravity (Gmm) using the AASHTO T-209 procedure. Finally an ignition test was performed to find the percentage of binder and to reclaim the aggregate for gradation analysis. Volumetric properties were then calculated and used as input for the Witczak dynamic modulus prediction equations to find what the MEPDG calls the undamaged master curve of the HMA layer. The FWD data, resilient modulus data, and pavement rating were used to find the damaged master curve of the HMA layer as suggested for input Levels 1, 2, and 3, respectively. It was found that the resilient modulus data needed for a Level 2 type of analysis do not represent the entire HMA layer thickness, and therefore it was recommended that this analysis should not be performed by VDOT when implementing the design guide. The use of Level 1 data is recommended because FWD testing appears to be the only procedure investigated that can measure the overall condition of the entire HMA layer.