Citation

Kulkarni AR, Shafei B. Transp. Res. Rec. 2018; 2672(48): 45-57.

Affiliation

Institute for Transportation, Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA Corresponding Author: Address correspondence to Behrouz Shafei: shafei@iastate.edu

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118795006

PMID

unavailable

Abstract

Iowa's roadway network is an important part of the state's transportation infrastructure and plays a critical role in the functionality and economic development of the entire state. This network primarily consists of three interstate highways that pass through Iowa, connecting it to the neighboring states and eventually Canada. Various businesses are located near this roadway network and rely on it for everyday operation. In recent years, however, the growth of agricultural and biofuel industries has intensified the demand on the roads and bridges in Iowa. The state's roads and bridges have also witnessed a number of flooding events, which have caused extensive traffic disruptions and economic losses. Thus, it is imperative to develop a fundamental approach to evaluate the impact of extreme events on the transportation infrastructure of Iowa and other similar states. Towards this goal, the current study investigates the existing condition of Iowa's transportation infrastructure, possibility of occurrence of extreme weather events, and scenarios that may lead to the failure of transportation infrastructure components. For this purpose, the capabilities of Bayesian belief networks are utilized to quantify the effects of extreme precipitation and extreme temperature on the performance of transportation infrastructure and then predict the probability of damage to roads and bridges. This will be achieved through the identification of the most influential factors using a set of sensitivity analyses, assessment of overall vulnerability with evidence-based propagation analyses, and quantification of response to extreme weather events, taking into consideration climate projections.

Language: en