Analyzing Pre- and Post-Coastal Hazard Pavement Conditions to Optimize Response Strategies for Coastal Infrastructure Resilience

Texas' coastline faces increasing risks from natural hazards, necessitating efficient and effective infrastructure response strategies to mitigate impacts and ensure rapid recovery. This research aims to investigate the effects of coastal hazards on pavement conditions and to use network analysis for optimizing pavement infrastructure response, maintenance decisions, and treatment allocation to achieve equitable and resilient coastal communities. The study focuses on Houston which is a key urban center exposed to frequent coastal hazards. Hurricane Harvey was selected as a case study for in-depth analysis. Initially, the research team will conduct a comprehensive literature review of existing studies focusing on methods used for evaluating pavement conditions before and after coastal hazards. This review aims to identify best practices and effective methodologies for enhancing pavement durability and performance. Following this, the team will analyze historical pavement condition data from Houston before Hurricane Harvey, focusing on different pavement types (ACP, CRCP, JCP) and utilizing statistical models to understand data variability and characteristics. Subsequently, the team will analyze pavement conditions in Houston following Hurricane Harvey. This analysis will involve comparing pre- and post-Harvey data to assess the impact on pavement performance. Statistical methods will be applied to evaluate distress distribution and severity of pavements. Additionally, the research will evaluate the effectiveness of pavement condition analysis models for better maintenance prioritization post-coastal hazards. This step aims to understand how maintenance strategies evolve post-disaster and to enhance decision-making for maintenance planning. The final phase of the research focuses on developing tailored strategies for improving infrastructure resilience. This involves reviewing existing strategies including customizing them for Texas' coastal context and assessing their effectiveness through scenario analysis. The expected outcome of this research is to provide valuable insights into pre- and post-coastal hazard pavement conditions in Houston. By leveraging network analysis models, the study aims to inform maintenance decisions that prioritize fair and efficient response measures. The findings will contribute to developing strategies that enhance resilience for coastal pavement infrastructure.

    Language

    • English

    Project

    • Status: Active
    • Funding: $110988
    • Contract Numbers:

      CREATE UTC Contract Number 69A3552348330

    • Sponsor Organizations:

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590

      Coastal Research and Education Actions for Transportation Equity

      Texas State University
      San Marcos, TX  United States  77666

      Texas State University, San Marcos

      JCK Building, Suite 489
      San Marcos, TX  United States 
    • Managing Organizations:

      Texas State University, San Marcos

      JCK Building, Suite 489
      San Marcos, TX  United States 
    • Project Managers:

      Bruner, Britain

      Kulesza, Stacey

    • Performing Organizations:

      Texas State University, San Marcos

      JCK Building, Suite 489
      San Marcos, TX  United States 
    • Principal Investigators:

      Luo, Xiaohua

      Wang, Feng

      Hong, Feng

      Hong, Feng

      Gong, Haitao

    • Start Date: 20240901
    • Expected Completion Date: 20260228
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01930051
    • Record Type: Research project
    • Source Agency: Coastal Research and Education Actions for Transportation Equity
    • Contract Numbers: CREATE UTC Contract Number 69A3552348330
    • Files: UTC, RIP
    • Created Date: Sep 12 2024 3:33PM