Browsing by Author "Matias de Oliveira, Jhenyffer"

Now showing 1 - 3 of 3
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    Innovative Materials and Advanced Technologies for a Sustainable Pavement Infrastructure
    (Minnesota Department of Transportation, 2021-06) Le, Jia-Liang; Marasteanu, Mihai; Zanko, Lawrence M.; Matias de Oliveira, Jhenyffer; Calhoon, Thomas; Turos, Mugurel; Stricherz, Tyler; Hopstock, David M.; Hegg, Vern
    It is widely acknowledged that early detection of material damage and timely rehabilitation can lead to a significant reduction in the life-cycle cost of asphalt pavements. This research investigates the capabilities of damage detection and healing of graphite nanoplatelet (GNP)-taconite modified asphalt materials. The first part of the research is concerned with the application of GNP-taconite modified asphalt materials for damage detection using electrical conductivity. It is shown that, as compared to conventional asphalt materials, the GNP-taconite modified asphalt materials exhibit an improved electrical conductivity due to the electron hopping mechanism. Based on the mathematical analogy between the elastostatic field and the electrostatic field, a theoretical model is derived to relate the change of electrical conductivity to the damage extent of the material. Although, in principle, the material damage can be accessed using the electrical conductivity, the practical application of this method is complicated by the fact that the conductivity is influenced by the moisture content. The second part of the research investigates the damage healing capability of GNP-taconite modified asphalt materials heated by microwave. GNP-taconite modified asphalt materials can effectively absorb the heat generated by the microwave, and the rising temperature can effectively heal the microcracks in the binder. This damage-healing mechanism is verified by a set of semi-circular beam tests. Finally, microwave heating technology is applied to the tack coat system. It is shown that, with microwave heating, the GNP-taconite modified asphalt material can effectively improve the bond strength of the interface of the tack coat system.
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    Remaining Service Life Asset Measure, Phase 1
    (Minnesota Department of Transportation, 2018-07) Kumar, Ravi; Matias de Oliveira, Jhenyffer; Schultz, Arturo; Marasteanu, Mihai
    There is a critical need to use a common metric, such as a service life parameter, across many different types of infrastructure assets. MnDOT has used the remaining service life (RSL) measure for pavement condition for several years and is starting to use it for bridge condition. In this study, researchers examined what has been done to date and what tools and methodologies are available nationally and internationally, and made recommendations on a future measure that can be used to show the "true" condition of the system. First, a literature review was performed to summarize current methods used in asset management and life-cycle cost analyses. A survey was also performed to collect information from agencies around the country. An assessment of current practice used by MnDOT Bridge Office and Materials and Road Research Office was performed next to identify similarities and differences between the two approaches. Based on the information collected, suggestions for a common method were presented and guidelines for a work plan for a follow-up phase 2 were developed.
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    Remaining Service Life Asset Measure, Phase 2
    (Minnesota Department of Transportation, 2022-02) Matias de Oliveira, Jhenyffer; Khani, Alireza; Davis, Gary; Marasteanu, Mihai
    The main objectives of phase 2 of this project were to obtain relevant data to calculate the percent remaining service life interval (PRSI) and two additional metrics and to perform Markov chain analysis and dynamic programming to determine how much time and funding is required to bring the system to a stable configuration, which allows for more consistent planning. First, relevant pavement management data was obtained from MnDOT and preliminary data analyses were performed. The prediction models and optimization process currently used by MnDOT were investigated and summarized. Next, two additional metrics, Asset Sustainability Ratio and Deferred Preservation Liability, were calculated for MnDOT’s network. Then details of the estimation process of state-to-state transition probabilities to be used in the Markov chain model were presented. To allow for site-specific variation, ordinal logistic regression models were incorporated in the Markov chain model. The results were used in a dynamic programming optimization methodology to obtain baseline and optimal policies for different scenarios and a user-friendly excel spreadsheet tool was developed. Finally, a summary of the work performed followed by conclusions and recommendations was presented.