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contributor authorXu Yang
contributor authorZhanping You
contributor authorJacob E. Hiller
contributor authorMohd Rosli Mohd Hasan
contributor authorAboelkasim Diab
contributor authorSang Luo
date accessioned2022-01-30T19:13:13Z
date available2022-01-30T19:13:13Z
date issued2020
identifier otherJPEODX.0000182.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4264881
description abstractFive individual climate variables are required as climatic inputs in a pavement design using Pavement ME Design (PMED, a pavement design tool). While it is believed that all five climatic variables affect the distress predictions in pavement design, the detailed effect of these factors on the design of jointed plain concrete pavement (JPCP) has not been well researched. This study aims to investigate the effect of the five individual climate variables on the performance predictions of JPCP using PMED. The one-at-a-time approach was used to change individual climate variables. Four weather stations located in different climate zones (cold, warm, humid hot, and dry hot) in the United States were selected for analysis. The effect of individual climatic variables on the JPCP distress predictions were analyzed first. Then a normalized sensitivity index was adopted to analyze the sensitivity of performance predictions to individual climate variables. The effect results showed that with a 10% increase in the values of the climatic variables, the average temperature and daily temperature range have a positive effect on the values of the transverse cracking prediction. The effect of temperature on the international roughness index and faulting may not be consistent in different climate zones, and wind speed has a negative effect on the values of all three distresses. The sunshine percent and relative humidity have a positive effect on the values of all three distresses, and the effect of precipitation is negligible. The occurrence of probability of the temperature gradient within the concrete slab after the change in variables was also obtained and plotted to help interpret the findings. The sensitivity analysis showed that sunshine percent is the most influential climatic variable, followed by temperature, wind speed, relative humidity, and precipitation.
publisherASCE
titleSensitivity of Rigid Pavement Performance Predictions to Individual Climate Variables using Pavement ME Design
typeJournal Paper
journal volume146
journal issue3
journal titleJournal of Transportation Engineering, Part B: Pavements
identifier doi10.1061/JPEODX.0000182
page04020028
treeJournal of Transportation Engineering, Part B: Pavements:;2020:;Volume ( 146 ):;issue: 003
contenttypeFulltext


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