Study and Evaluation of Material Response in Hot Mix Asphalt based on Field Instrumentation (Phase II)
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2019-06-01
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Edition:June 2015 – June 2019
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Abstract:This report presents the results of a research study conducted to observe the variations in mechanistic responses, e.g. stress-strain, of a flexible pavement through embedded sensors. The research program was divided into two phases. During the first phase, an instrumentation section was constructed at I-40 near Albuquerque, New Mexico with an array of 32 sensors. Data collection and analysis were continued until the end of the second phase. Pavement responses were collected in various climatic conditions under actual traffic loads and routine Falling Weight Deflectometer (FWD) test loads. Field distress data were collected at regular intervals. Field cored Asphalt Concrete (AC) samples were also collected for laboratory testing and comparing with the field sensor data. From the results, it is observed that pavement responses initially increased immediately after the construction, but they started to decrease with time. AC backcalculated modulus is also observed to be decreasing with time. The reason for this decrease is damage. By combining sensor data with FWD modulus and Weigh-In-Motion (WIM) data, damage in pavement layers were quantified. Pavement response frequency from sensor data under traffic loadings is compared with the AASHTOWare ME predicted loading frequency. It is observed that the AASHTOWare ME always overpredicts loading frequency, which in turn overpredicts the dynamic modulus value of the AC layer, leading to over design of the AC layer. A good agreement is observed between the viscoelastic parameters of the AC layer obtained directly from field sensor data and laboratory tested data. No major distresses, such as cracking and rutting, were observed in the driving lane but delamination between the AC lifts is observed. Thus, to have a meaningful calibration of the distress models and to study the pavement performance variations under various distresses, a continuation of the data collection is recommended.
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