rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: A
    title: Risk- and Performance-based Infrastructure Asset Management Framework
  name:
    name part: Hessami, Amir Rowshan
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Anderson, Stuart
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Asset managers who maintain the infrastructure can improve the efficiency of their practices by making use of decision-support frameworks. These models use performance data to help maximize the outcomes of maintenance strategies and financial allocations. In this research a new decision-support framework for asset managers was developed and tested. This framework improves upon existing models in two respects: (a) it provides an effective and practical method of accounting for uncertainty/risk, and (b) it includes a method for predicting asset performance over time in situations where there is limited historical data. Outcome-based scenario analysis was chosen as the most effective approach to model risk in asset management. The proposed framework presents managers with "best-case," "most-likely case," and "worst-case" scenarios, which are defined by applying quantile regression analysis to the asset-performance data. For situations in which there is a lack of adequate historical performance data, an elicitation model was developed based on the Delphi technique. This approach provides a rigorous method for estimating asset performance using information solicited from a panel of experts. The elicited data was aggregated by a Bayesian hierarchical model and the Markov Chain Monte Carlo algorithm. A case study was conducted to demonstrate the applicability of the decision-support model. While the proposed framework is generic and could be used for any type of asset, this study involved pavement condition on the city streets of Bryan, Texas. The results indicated that using a traditional deterministic model (rather than a scenario-based approach) could lead to significant over- or under-estimation of the budgets required to achieve certain asset-performance results. This demonstrates the urgent need for asset managers to use a practical model that can provide them with information about uncertainty and risk in asset-performance assessments. The case study also demonstrat…
  note: by Amir Rowshan Hessami.
  note: "Major Subject: Civil Engineering"
  note: Includes vita.
  note: Doctor of Philosophy Texas A & M University 2015 http://hdl.handle.net/1969.1/155664
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major civil engineering
  subject:
    topic: Infrastructure Asset Management
  subject:
    topic: Outcome-based Scenario Analysis
  subject:
    topic: Asset Management Levels of Uncertainty
  subject:
    topic: Data Elicitation
  subject:
    topic: Bayesian Hierarchical Model
  identifier: http://hdl.handle.net/1969.1/155664
  record info:
    record content source: TXA
    record creation date: 161028
    record change date: 20170926150343.0
    record identifier: in00003752710
    language of cataloging:
      language term: eng