Assessing impacts of climate change on flexible pavement service life based on Falling Weight Deflectometer measurements

Yaning Qiao, Yang Zhang, Yuefeng Zhu, Trent Lemkus, Anne M.K. Stoner, Jizhe Zhang, Yuanlong Cui

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Flexible pavements are typically designed using historical climate but are challenged by future climate change. Quantifying impacts of climate change on pavement service life can assist road authorities in planning for climate adaptation and, eventually, build climate resilience into road infrastructure design and management. In this study, a novel data-driven methodology is developed in order to quantify impacts of climate change on pavement service life in locations where Falling Weight Deflectometer (FWD) data are continuously measured, by means of: 1) training a supervised model (linear regression or Artificial Neural Networks, ANN) using historical climate data, maintenance, and traffic data as the candidate inputs and pavement layer stiffness back calculated from FWD testing as the outputs; 2) predicting layer stiffness using statistically downscaled future climate projections for three Coupled Model Intercomparison Project Phase 5 global climate models and three greenhouse gas concentration scenarios for four future 20-year periods; and 3) estimating changes in pavement stiffness and service lives due to climate change. A case study performed on a pavement section in Minnesota has shown that pavement layer stiffness will have a long-term reduction under future climate and the investigated pavement will lose up to 22.5% service life at the end of the century (2080–2099) from the 20 years’ service life compared to the baseline climate (1979–1998).

Original languageEnglish
Article number102908
JournalPhysics and Chemistry of the Earth
Volume120
DOIs
StatePublished - Dec 2020

Keywords

  • Artificial neural networks
  • CMIP5
  • Flexible pavements
  • Resilience
  • Stiffness

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