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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.
Inspired from the legacy of the previous four 3DFEM conferences held in Delft and Athens as well as the successful 2018 AM3P conference held in Doha, the 2020 AM3P conference continues the pavement mechanics theme including pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance. The AM3P conference is organized by the Standing International Advisory Committee (SIAC), at the time of this publication chaired by Professors Tom Scarpas, Eyad Masad, and Amit Bhasin. Advances in Materials and Pavement Performance Prediction II includes over 111 papers presented at the 2020 AM3P Conference. The technical topics covered include: - rigid pavements - pavement geotechnics - statistical and data tools in pavement engineering - pavement structures - asphalt mixtures - asphalt binders The book will be invaluable to academics and engineers involved or interested in pavement engineering, pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance.
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.
This volume highlights the latest advances, innovations, and applications in bituminous materials and structures and asphalt pavement technology, as presented by leading international researchers and engineers at the RILEM International Symposium on Bituminous Materials (ISBM), held in Lyon, France on December 14-16, 2020. The symposium represents a joint effort of three RILEM Technical Committees from Cluster F: 264-RAP “Asphalt Pavement Recycling”, 272-PIM “Phase and Interphase Behaviour of Bituminous Materials”, and 278-CHA “Crack-Healing of Asphalt Pavement Materials”. It covers a diverse range of topics concerning bituminous materials (bitumen, mastics, mixtures) and road, railway and airport pavement structures, including: recycling, phase and interphase behaviour, cracking and healing, modification and innovative materials, durability and environmental aspects, testing and modelling, multi-scale properties, surface characteristics, structure performance, modelling and design, non-destructive testing, back-analysis, and Life Cycle Assessment. The contributions, which were selected by means of a rigorous international peer-review process, present a wealth of exciting ideas that will open novel research directions and foster new multidisciplinary collaborations.
In the recent past, new materials, laboratory and in-situ testing methods and construction techniques have been introduced. In addition, modern computational techniques such as the finite element method enable the utilization of sophisticated constitutive models for realistic model-based predictions of the response of pavements. The 7th RILEM International Conference on Cracking of Pavements provided an international forum for the exchange of ideas, information and knowledge amongst experts involved in computational analysis, material production, experimental characterization, design and construction of pavements. All submitted contributions were subjected to an exhaustive refereed peer review procedure by the Scientific Committee, the Editors and a large group of international experts in the topic. On the basis of their recommendations, 129 contributions which best suited the goals and the objectives of the Conference were chosen for presentation and inclusion in the Proceedings. The strong message that emanates from the accepted contributions is that, by accounting for the idiosyncrasies of the response of pavement engineering materials, modern sophisticated constitutive models in combination with new experimental material characterization and construction techniques provide a powerful arsenal for understanding and designing against the mechanisms and the processes causing cracking and pavement response deterioration. As such they enable the adoption of truly "mechanistic" design methodologies. The papers represent the following topics: Laboratory evaluation of asphalt concrete cracking potential; Pavement cracking detection; Field investigation of pavement cracking; Pavement cracking modeling response, crack analysis and damage prediction; Performance of concrete pavements and white toppings; Fatigue cracking and damage characterization of asphalt concrete; Evaluation of the effectiveness of asphalt concrete modification; Crack growth parameters and mechanisms; Evaluation, quantification and modeling of asphalt healing properties; Reinforcement and interlayer systems for crack mitigation; Thermal and low temperature cracking of pavements; and Cracking propensity of WMA and recycled asphalts.
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.
Bituminous Mixtures and Pavements VIII contains 114 papers as presented at the 8th International Conference ‘Bituminous Mixtures and Pavements’ (8th ICONFBMP, 12-14 June 2024, Thessaloniki, Greece). The contributions reflect the research and practical experience of academics and practicing engineers from thirty-four (34) different countries, and cover a wide range of topics: Session I: Bitumen, Modified binders, Aggregates, and Subgrade Session II: Bituminous mixtures (Design, Construction, Testing, Performance) Session III: Pavements (Design, Construction, Maintenance, Sustainability, Energy and Environmental consideration) Session IV: Pavement management and Geosynthetics Session V: Pavement recycling Session VI: Pavement surface characteristics, Pavement performance monitoring, Safety Session VII: Biomaterials in pavement engineering Session VIII: Prediction models of pavement performance Bituminous Mixtures and Pavements VIII covers recent advances in highway materials technology and pavement engineering, and will be of interest to scientists and professionals involved or interested in these areas. The ICONFBMP-conferences have been organized every four years since 1992. This 8th conference was jointly organized by: Laboratory of Highway Engineering, Aristotle University of Thessaloniki, Greece; Built Environment Research Institute (BERI), University of Ulster, UK; University of Texas San Antonio (UTSA), USA; Laboratory for Advanced Construction Technology (LACT), Technological Institute of Iowa, USA; Technological University of Delft (TUDelft), The Netherlands, and University of Antwerp, (UA), Belgium.