Date of Award

Spring 2022

Project Type

Dissertation

Program or Major

Civil Engineering

Degree Name

Doctor of Philosophy

First Advisor

Jo E Sias

Second Advisor

Jo E Sias

Third Advisor

Eshan V Dave

Abstract

Most cold In-place recycled (CIR) construction use asphalt emulsion or foamed asphalt with or without active fillers as a stabilizing agent. To ensure the CIR layer gains appreciable stiffness and strength to support traffic, the stabilizing agents have to undergo curing (to gradually develop strength). If traffic is allowed on the CIR layer before sufficient strength and structural capacity is gained, premature damage will occur. Lack of a fast and reliable procedure to determine the extent of in-situ curing significantly increases the risk of such damage. Present construction specifications rely on empirically based time recommendations to ensure sufficient curing. Current empirical time estimates do not account for material variations, climatic inputs and construction process differences. This research used a combination of in-situ testing of actual CIR construction projects and supplementary laboratory tests to develop a model for pavement engineers and practitioners to reliably predict the recommended time (as a function of mechanical property) for allowing traffic and/or placing of overlay on CIR layers. The prediction model incorporates the critical factors that influence curing in CIR including stabilizer type and amount, stabilizer type and amount, initial moisture content, in-situ density, curing temperature and relative humidity. Rigorous regression analysis and machine learning approaches were employed to develop the model, which was further converted into a user-interactive web-based tool (available at: https://annits-predictor.netlify.app/).

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