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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Holistic Network-level Assessment of Pavement Flood Damages

Authors: Yong-Cheol Lee; Kunhee Choi; Moeid Shariatfar;

Holistic Network-level Assessment of Pavement Flood Damages

Abstract

After recent catastrophic flood disasters in Louisiana in 2016 and Texas in 2017, roadways in Region 6 areas suffer not only from the flood-inundation, but also from the long-term recovery processes that incur enormous maintenance costs. To assess the impacts of flooding disasters on roadways, various studies have investigated sampled roadway damages with pavement engineering techniques such as a direct damage analysis using cores/bores. However, current methods are time-consuming and labor-intensive. In addition, even though existing methods provide a detailed damage analysis of pavement in a particular location for a particular time period, there is still a large practical knowledge gap in understanding network-level roadway functional/structural damages before-and-after historic flooding as well as assessing flooding impacts on roadways over time. Thus, a holistic perspective and a long-term investigation on roadway damages caused by floods have been rarely addressed, which has resulted in the absence of accurate maintenance cost prediction. The primary objective of this project is to develop a holistic roadway damage assessment method using the flood models and the pavement condition data accumulated over the years. This project also aims to provide a means for Louisiana and Texas (ultimately to all Region 6’s States) to intuitively identify roadway damage patterns at the network level caused by flooding over time as well as predict roadway maintenance tasks. To accomplish the proposed goal, this project examines roadways of parishes and counties in Louisiana and Texas affected by previous flood disasters by using pavement assessment data obtained from the Pavement Management System (PMS) in the Louisiana Department of Transportation and Development (LaDOTD), and the Pavement condition data of the City of Houston. This project is expected to provide a network-level roadway damage assessment and play a pivotal role in reducing the cost of a direct damage analysis such as coring/boring.

Tran-SET Project: 19PLSU13

Related Organizations
Keywords

Pavement performance, PMS, flooding damage, prediction model

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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