Impact of Curling and Warping on Concrete Pavement, Phase II

Project Details
STATUS

Completed

PROJECT NUMBER

18-659, TR-749

START DATE

05/15/18

END DATE

05/30/23

FOCUS AREAS

Infrastructure

RESEARCH CENTERS InTrans, CP Tech Center, PROSPER
SPONSORS

Iowa Department of Transportation
Iowa Highway Research Board

Researchers
Principal Investigator
Halil Ceylan

Director, PROSPER

Co-Principal Investigator
Sunghwan Kim

Associate Director, PROSPER

About the research

Curling and warping behavior due to temperature and moisture variation has been widely considered an influential factor affecting the smoothness of jointed plain concrete pavement (JPCP). In recent decades, while extensive efforts have been made to quantify the impact of curling and warping-related deflections on the smoothness of JPCP, a standardized method for characterizing the effects of environmental factors on JPCP smoothness is still unavailable. A Phase I study examined curling and warping conditions at six sites using a stationary light detection and ranging (LiDAR) system and developed recommendations to minimize curling and warping based on literature review findings. However, the data collection effort in the Phase I study was limited and was insufficient to validate the recommendations derived from the literature review.

The Phase II study described in this report aimed to evaluate and quantify the impact of curling and warping on Iowa concrete pavements and determine the factors that most influence curling and warping behavior. A high-speed profilometer and a LiDAR device were utilized to execute a large-scale field data collection plan for JPCP sites in Iowa, including Long-Term Pavement Performance (LTPP) Program highways, non-LTPP highways, and county roads and city streets. The variables evaluated in this study included temperature and moisture gradients, seasonal and diurnal effects, slab geometry, pavement structural design, mix design, and construction conditions. A validated MATLAB-based algorithm with two different curve-fitting models was coded to evaluate the degrees of curling and warping in multiple ways. This study also used statistical analyses to select the variables that significantly affect curling and warping behavior. The proposed actionable pavement design and construction recommendations will help minimize curling and warping and correct curling and warping-related performance issues.

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