United States Department of Transportation
i
The Impact of Hurricane Harvey on Pavement Structures in the South East Texas and South West Louisiana
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2019-10-01
[PDF-1.86 MB]
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Edition:Mar. 2018 – Mar. 2019
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Abstract:This study developed a methodology to estimate the damage caused by flooding, such that caused by Hurricane Harvey, on a road or street network. The flooded street or pavement sections are identified using Geographic Information System (GIS) flood maps with street GIS maps used for pavement management systems (PMS) by cities or state authorities. Then the damage caused by flooding directly through the increase moisture in foundation layers or indirectly due to the increase heavy traffic during the relief effort is estimated. An example Excel macro was created to illustrate the estimation process. The methodology estimates the increase in rehabilitation costs since the flooding imposes that many rehabilitation works must be done earlier than anticipated before the flooding. The methodology also estimated the increase in fuel consumption caused by the increased in pavement roughness if the rehabilitation works are done when anticipated before the flooding. The methodology and the Excel macro can also be used to identify the pavement structures with better resilience to the flooding by grouping sections based on the flooding duration (no flooding, single and multiple day flooding) and on design features such as pavement type, functional class, age or time from the most recent resurfacing or reconstruction, subgrade soil type, traffic volume, layer thickness. In the case of networks with large number of sections, grouping done based on multiple criteria can allow detailed comparison and identification of design feature with more impact on the resiliency to flooding. ANOVA and MANOVA technique can be used to compare the Z-score values calculated for the reduction in Pavement Condition Index (PCI) due to flooding for sections in different groups. The methodology compares for each street or pavements section the measured condition after the flooding with that predicted based on data collected in multiple condition surveys before the flooding. It considers each road or street section as having unique design features, in-service conditions and therefore, unique performance.
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