Semester of Graduation

Summer 2019

Degree

Master of Science in Civil Engineering (MSCE)

Department

Department of Civil and Environmental Engineering

Document Type

Thesis

Abstract

Many state agencies have recognized the importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation (M&R) strategies along with functional indices. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The Rolling Wheel Deflectometer (RWD) and Traffic Speed Deflectometer (TSD) have emerged as continuous pavement deflection-measuring devices as they operate at traffic speed and reduces lane closure and user delays.

The research objective of this study was to assess the feasibility of using TSD measurements at the network-level for pavement conditions structural evaluation in Louisiana. To achieve the objectives of the study, TSD and Falling Weight Deflectometer (FWD) measurements were collected in District 05 of Louisiana and data were available from experimental programs conducted at the MnROAD research test facility and in Idaho. TSD measurements were compared with FWD deflection measurements to evaluate the level of agreement and difference between the two devices. Based on this evaluation, an SN predictive model was developed and validated to assess the structural conditions of in-service pavements based on TSD measurements. The model was then used to identify structurally sound and structurally deficient in-service pavements. This study also assessed whether the use of surface indices only or the declining rates of these indices to identify structurally damaged sections is feasible instead of relying on RWD and TSD estimated pavement structural indices.

Based on the results of the analysis, it is concluded that the deflection reported by both FWD and TSD for the same locations are statistically different, which was expected given the differences in loading characteristics and load type between the two devices. It is also concluded that surface roughness has a notable effect on the TSD field measured deflections.

The present study successfully developed and validated a model to predict in-service SN based on TSD deflections at 0.01-mile intervals of a road section. Core samples showed that the sections that were predicted to be structurally deficient from the model suffered from asphalt stripping and debonding problems. Yet, some of these sections were in very good conditions according to their functional indices.

Findings suggest that structural deficiency, rates of deterioration, and surface indices were correlated to a certain extent. Yet, surface indices cannot be used as a reliable predictor of structural capacity. For RWD tested sections, the most accurate surface index, which was the alligator cracking surface index, erroneously identified 35% of structurally sound sections as structurally deficient and 51.5% of structurally deficient sections as structurally sound. Similar results were also obtained for the TSD tested sections. The cost implication associated with misinterpreted sections from functional indices was investigated. The incorporation of structural indices is expected to provide significant savings to state agencies.

Committee Chair

Mostafa Elseifi

Available for download on Saturday, June 06, 2020

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