Analyses and prediction of granular layer rutting trends in airport pavements due to heavy aircraft wheel loading and wander patterns

Sarker, Priyanka

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https://hdl.handle.net/2142/107949 Copy

Description

Title

Analyses and prediction of granular layer rutting trends in airport pavements due to heavy aircraft wheel loading and wander patterns

Author(s)

Sarker, Priyanka

Issue Date

2020-05-01

Director of Research (if dissertation) or Advisor (if thesis)

Tutumluer, Erol

Doctoral Committee Chair(s)

Tutumluer, Erol

Committee Member(s)

• Al-Qadi, Imad L
• Roesler, Jeffery R
• Thompson, Marshall R
• Garg, Navneet

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Airport pavements
• Rutting
• Unbound granular layer
• Accelerated pavement testing
• Rut predictions
• Statistical modeling
• Performance based evaluation
• Load wander
• Multi-depth deflectometer (MDD)
• NAPTF

Abstract

This study focuses on establishing a better understanding of the combined influences of various aircraft with complex gear configuration and the gear load wanders on the rut (i.e., permanent deformation) accumulation in unbound layers of airfield flexible pavement. As such, the challenge is to investigate and propose proper rut prediction models that can capture such influences from full-scale accelerated pavement test studies. This was achieved by studying pavement test sections constructed and full scale pavement tested at Federal Aviation Administration’s (FAA’s) National Airport Pavement Test Facility in New Jersey. Referred to herein as the Construction Cycle 5 (CC5) experiment, CC5 sections were built with different subbase materials (crushed quarry screenings and dense graded aggregates) with varying thicknesses over a low-strength subgrade and were trafficked by six-wheel and 10-wheel landing gears with wander. These pavement sections were instrumented with various sensors such as multi-depth deflectometer (MDD), pressure sensor, and asphalt strain gauge. The MDD sensors provided the most valuable data for this research since both elastic (or resilient) and plastic deformation response values of individual layers were measured in the pavement system due to the passages of both six-wheel and 10-wheel landing gears applied with wander. Analyses of the MDD data indicated that the effects of load wander were evident on the residual (non-recoverable) deformation accumulations because changes in wander locations influenced the directional nature (either upward or downward) of residual deformation values. The residual deformation data separation showed that the first pass on each wander position in the west to east direction typically caused the highest deformation response and the return pass along the same wander position showed significantly less residual deformation. This finding clearly indicated the presence of the so-called shakedown effect with load wander governing the behavior of unbound aggregate layers. Also, it was noted that shakedown was more readily happening when the wander width was kept narrow. Especially in the 6-wheel sections, the residual deformations were not increasing much due to increased traffic. An observation was made to clearly show the presence of anti-shakedown in granular layers subjected to loading with wander by calculating individual pavement layer permanent deformation values with traffic passes from the MDD sensor collected data at the MDD location. It was observed that in all cases for all the available sections, the contribution of rutting from the rather thick subbase layers (34 and 38 in.) were significant when compared to those of the other layers. Furthermore, accumulations of permanent deformation in subbase layers did not slow down but rather increased as traffic progressed. This phenomenon contradicted the shakedown theory according to which all unbound layers are expected to undergo shakedown with increasing traffic. Post traffic trenching study showed that the subgrade layers in sections with 38 in. subbase layers did not show any significant rutting after trafficking was concluded and most of the rutting in these sections were limited to base and subbase layers. However, rutting did occur to some extent in subgrade layers of sections that were all built with a 34 in. of subbase layer. It was also observed that the thin HMA layer thickness did not change much, i.e. no HMA rutting in any section during the full-scale pavement testing study. Additionally, an attempt was made to predict the rut depths in airport pavements due to realistic air traffic using the MDD data from the NAPTF CC5 test sections. Using the multi-depth deflectometer (MDD) database, a method of using only two critical wander locations was developed to establish a transverse profile for each pass and then calculate the transverse profile created by multiple passes. This method was based on the relationship between the maximum residual MDD readings due to various wander positions. By utilizing the MDD calculated transverse profiles and the periodically measured transverse field surface profiles, a rut prediction model was developed using general linear models in the forms of power and sigmoidal function distributions to determine realistic surface profiles of the CC5 test sections. It was observed that both the power and sigmoidal models could predict field surface rut profiles accurately up to 15,000 passes. However, at higher gear/wheel passes the sigmoidal model predictions were more accurate than those of the power predicted ones. The proposed methodology was further validated by using two test sections from CC1 test series. It was observed that for CC1 test sections, the power model predictions were more accurate than those of the sigmoidal ones. It was concluded that in events, where pavement sections are expected to deteriorate quickly, a power model is expected to more accurately determine the future deformation values. However, if the wheel loading is increased intermittently like in the case of CC5 test sections, a sigmoidal model will likely perform better to capture the changes in rut accumulation rates.

Graduation Semester

2020-05

Type of Resource

Thesis

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http://hdl.handle.net/2142/107949

Copyright and License Information

Copyright 2020 Priyanka Sarker

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