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Complex modulus is one of the key parameters in the Mechanistic-Empirical Pavement Design Guide (MEPDG). The purpose of this study is to implement an accurate and high-efficiency mechanical method to measure and calculate the complex modulus gradient of asphalt concrete cores in different field locations. Because field cores are different from the asphalt mixtures made and compacted in the lab, field cores should not be substituted by lab made lab compacted (LMLC) asphalt mixtures perfectly. For field cores complex modulus measuring methods, except some expensive pavement field testers, empirical and semiempirical models are widely used, but an accurate mechanical test method is more desired. In this research, Arizona, Yellowstone National Park and Texas field cores and three types of asphalt mixtures including hot mix asphalt (HMA), foaming warm mix asphalt (FWMA), and Evotherm warm mix asphalt (EWMA) were used. There were nearly forty field cores with different aging times from these three locations have been collected and tested using this new viscoelastic method. The complex modulus at a random depth and the depth of highly aged pavement can be calculated and estimated from these stiffness gradient figures. After analyzing the results, a strong correlation between test results and solar radiation and some other models have also been established which can be used for estimating the complex modulus of an in-service pavement. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151814
Author's abstract: The complex dynamic modulus (|E*|) is a characterization property that defines the stiffness of an asphalt mixture. The dynamic modulus can be found through lab testing or predictions. Since lab testing can be time-consuming and expensive, the prediction method can be used as an alternative method. While a statistical method has been traditionally used for the |E*| prediction such as the Witczak's predictive equations, machine learning (ML) is recently emerging as an alternative way that |E*| predictions can be made. This research attempted to predict the |E*| using several ML techniques including linear regression, support vector machines (SVM), decision trees, random forest, and deep learning. This research includes 3906 laboratory-measured |E*| data points that come from a variety of asphalt mixtures. In the database, there is a group of conventional materials, but most of the data comes from non-conventional materials. These non-conventional materials include reclaimed asphalt pavement (RAP), recycled asphalt shingles (RAS), warm mix asphalt (WMA), asphalt rubber, air-blown asphalt, and polymer-modified. The following evaluation metrics are used to evaluate the results from ML: mean absolute error, mean squared error, root mean squared error, and explained variance score. In this research, two comparisons were made to answer the following questions: 1) Which ML technique would provide a better prediction for |E*|? and 2) Between ML and Witczak's predictive method, which would provide a better prediction for |E*|? It was concluded that decision trees and random forests had the best results, and linear regression results needed the most improvement. When comparing the results of the ML methods (based on the R2 value), it was found that the results of decision trees, random forest, and deep learning outperformed the 1999 and 2006 Witczak's predictive equations. However, the 1999 and 2006 Witczak's predictive equations outperformed the linear regression model (based on the R2 value). The 1999 Witczak's predictive equation outperformed SVM. The results for SVM and the 2006 Witczak predictive equation were close, and it appeared that the SVM may be the better method. The 1999 Witczak's predictive equation outperformed SVM.
The modulus is one of the primary asphalt mixture properties used for the mechanistic performance prediction of asphalt pavements. Dynamic modulus testing is a common method of measuring mixture modulus as a function of loading frequencies and temperatures. This paper presented the results of a ruggedness study of dynamic modulus testing in indirect tension mode to evaluate the factors that were most likely to affect the final results. Specimen thickness, air void content, gauge length, test temperature, and horizontal strain level, which are the critical factors that affect the dynamic modulus of asphalt concrete, were selected for the ruggedness analysis. Two different asphalt mixtures with the participation of two laboratories were used in the study. Based on the selected values for the different variables, air void content was found to be the significant factor that affected dynamic modulus testing and dynamic modulus values. The other factors did not appear to have a major impact on the test results; however, reasonable tolerances were obtained for the other parameters investigated in this paper.
The purpose of this research is to present the results from an analytical/experimental study on the dynamic modulus testing of hot mix asphalt (HMA) using the indirect tension (IDT) mode. The analytical solution for dynamic modulus determination in IDT was developed by Kim (14) using the theory of linear viscoelasticity. To verify the analytical solution, temperature and frequency sweep tests were conducted on 24 asphalt mixtures commonly used in North Carolina, using both axial compression and IDT test methods. In doing so, a modified dynamic modulus test protocol is introduced that reduces the required testing time by using more frequencies and fewer temperatures based on the time-temperature superposition principle. A comparison of results from the axial compression and IDT test methods shows that the dynamic modulus mastercurves and shift factors derived from the two methods are in good agreement. It was also found that Poisson's ratio is a weak function of the loading frequency; its effect on the phase angle mastercurve is discussed. After verification of the analytical solution, another study was conducted to evaluate the effect of aggregate size on the variability of test results, where the coefficient of variation (CV) was computed for each aggregate size and the results were compared. It was found that mixes with a larger nominal maximum size of aggregate (NMSA) had a greater CV than those with a smaller NMSA. Digital image Correlation was used to further support the findings and reveal physical explanations for the results obtained from this statistical analysis.
This STAR on asphalt materials presents the achievements of RILEM TC 206 ATB, acquired over many years of interlaboratory tests and international knowledge exchange. It covers experimental aspects of bituminous binder fatigue testing; the background on compaction methods and imaging techniques for characterizing asphalt mixtures including validation of a new imaging software; it focuses on experimental questions and analysis tools regarding mechanical wheel tracking tests, comparing results from different labs and using finite element techniques. Furthermore, long-term rutting prediction and evaluation for an Austrian road are discussed, followed by an extensive analysis and test program on interlayer bond testing of three different test sections which were specifically constructed for this purpose. Finally, the key issue of manufacturing reclaimed hot mix asphalt in the laboratory is studied and recommendations for laboratory ageing of bituminous mixtures are given.
Keywords: Dynamic Modulus, IDT, Phase Angle, Viscoelasticity.