A Weibull Reliability Model of Bridge Deck Deterioration

Bridge deck deterioration can be difficult to predict, especially when environmental factors such as high-moisture and frequent freeze-thaw cycles are considered. For this reason, it is important to accurately assess bridge deck attributes that contribute to deterioration and create tools that can be used in practice to predict bridge deck deterioration over time. This paper utilizes bridge deck inspection data (dated 1985-2014) from PennDOT for over 22,000 bridges across Pennsylvania to develop a practically implementable model to compare the deterioration of different types of bridge deck materials and bridge structures. Bridge decks evaluated in this research include different concrete and asphalt mixes, different number of spans supporting the bridge, treated and bare rebar reinforcement, and construction year of bridges. A Weibull distribution model is further proposed to quantitively analyze the deterioration pattern of bridges with different attributes. Predictive models that consider single attributes are incorporated into a combined model using weight factors to comprehensively assess the reliability of a bridge deck considering several attributes. The results provide a detailed analysis of the reliability of bridges with different attributes and will help to calibrate the decision-making in the process of infrastructure system management.

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Work Title A Weibull Reliability Model of Bridge Deck Deterioration
Access
Open Access
Creators
  1. Jonathan Hydock
  2. Muyang Lu
  3. S. Ilgin Guler
  4. Aleksandra Radlińska
Keyword
  1. Bridge deck deterioration
  2. Cumulative truck traffic
  3. Weibull distribution
  4. Reliability prediction
License In Copyright (Rights Reserved)
Work Type Conference Proceeding
Publication Date 2021
Source
  1. Proceedings of 2021 TRB Annual Meeting
Deposited July 24, 2023

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Version 1
published

  • Created
  • Added Weibull_Reliability_of_Bridge_Deck_Manuscript.docx
  • Added Creator Jonathan Hydock
  • Added Creator Muyang Lu
  • Added Creator S Guler
  • Added Creator Aleksandra Radlinska
  • Published
  • Updated Source, Keyword, Subtitle, and 2 more Show Changes
    Source
    • Proceedings of 2021 TRB Annual Meeting
    Keyword
    • Bridge deck deterioration, Cumulative truck traffic, Weibull distribution, Reliability prediction
    Subtitle
    • Proceedings of 2021 TRB Annual Meeting
    Description
    • A Weibull Reliability Model of Bridge Deck Deterioration
    • Bridge deck deterioration can be difficult to predict, especially when environmental factors such as high-moisture and frequent freeze-thaw cycles are considered. For this reason, it is important to accurately assess bridge deck attributes that contribute to deterioration and create tools that can be used in practice to predict bridge deck deterioration over time. This paper utilizes bridge deck inspection data (dated 1985-2014) from PennDOT for over 22,000 bridges across Pennsylvania to develop a practically implementable model to compare the deterioration of different types of bridge deck materials and bridge structures. Bridge decks evaluated in this research include different concrete and asphalt mixes, different number of spans supporting the bridge, treated and bare rebar reinforcement, and construction year of bridges. A Weibull distribution model is further proposed to quantitively analyze the deterioration pattern of bridges with different attributes. Predictive models that consider single attributes are incorporated into a combined model using weight factors to comprehensively assess the reliability of a bridge deck considering several attributes. The results provide a detailed analysis of the reliability of bridges with different attributes and will help to calibrate the decision-making in the process of infrastructure system management.
    Publication Date
    • 2021-01-01
    • 2021
  • Renamed Creator S. Ilgin Guler Show Changes
    • S Guler
    • S. Ilgin Guler
  • Renamed Creator Aleksandra Radlińska Show Changes
    • Aleksandra Radlinska
    • Aleksandra Radlińska
  • Updated