Pavement Resilience Assessment Using Pavement Condition Data Before and After Hurricane Harvey

Areas located in the coastal regions are frequently exposed to the increasing risk from coastal disasters like storm surges, flooding, erosion, and rising sea levels which directly threaten civil engineering infrastructures, especially the transportation assets. This research is focused on assessing the impact of Hurricane Harvey on the pavements in the Houston area using historical pavement management system data and statistical and machine learning models. Taking Hurricane Harvey into consideration, pre- and post- Harvey pavement conditions were compared, and statistical and machine learning models were used for assessing distress types, severity levels, and distress distribution across different pavement types, e.g. Asphalt Concrete Pavement (ACP), Joint Concrete Pavement (JCP), and Continuously Reinforced Concrete Pavement (CRCP). Initially, a Wilcoxon signed-rank test was used to confirm the impact of hurricane on the pavement performance which confirmed the network level damage that happened in pavements after Hurricane Harvey. Then a multivariable regression model and random forest model were employed to determine how the attributes influenced the change in pavement performance before and after the event. Regression analysis identified pre-Harvey condition score and pavement type as significant predictors of a post-Harvey condition. Random Forest model analyzed the distress-specific patterns across the pavement types followed by residual analysis which further identified the best performing and worst performing pavement sections. The results showed that CRCP pavements exhibited good resiliency; ACP pavements had localized and distributed failures, and JCP pavements were most vulnerable to flood related damage.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Civil Engineering

Grantor

Texas State University

Year dc:date.issued

2026

Author dc:creator

• Pokharel, Aashima

Advisor dc:contributor.advisor

• Luo, Xiaohua

Committee members dc:contributor.committeemember

• Wang, Feng
• Hong, Feng

• coastal disasters
• resilience
• random forest model
• pavement performance
• maintenance and rehabilitations

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10877/24718

OAI identifier oai:identifier

oai:digital.library.txst.edu:10877/24718