Quantification of Uncertainty in the Remedial Investigation/Feasibility Studies Process

Abstract

This thesis developed a method to bound cost estimates with a prediction interval of costs for the Remedial Investigation/Feasibility Study (RI/FS) phase of the Installation Remediation Program (IRP) process. The prediction interval provides a reasonableness cross check for RI/FS project cost estimates. To develop the cost bounds, three major activities occurred. First, a database was developed from RI/FS projects managed by the Army Corps of Engineers. Second, a regression cost model was developed from the observations in the database. Third, a prediction interval specified at the 70 percent confidence level was derived from the cost model. This prediction interval provides a method to cross check RI/FS cost estimates. The prediction interval also provides a heuristic to bound RI/FS point estimates to incorporate uncertainty. There are limitations to the cost model which affect the use of the cost intervals. The observations used to develop the cost model were limited to RI/FS projects whose field activities only included soil boring and monitoring well activities. The cost intervals should only be applied to similar type projects. Cost Uncertainty, RI/FS, ENVEST, Installation Restoration Program (IRP), Environmental cost estimating

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA273924

Entities

People

  • Kurt C. Held
  • Perry J. Shepler

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Civil Engineering
  • Computer Programming
  • Computer Programs
  • Computers
  • Cost Analysis
  • Databases
  • Environmental Restoration And Remediation
  • Feasibility Studies
  • Groundwater
  • Hazardous Waste
  • Hygiene
  • Information Science
  • Medical Personnel
  • Standards
  • Surveys
  • Waste Disposal Facilities

Readers

  • Computational Modeling and Simulation
  • Environmental Remediation and Restoration.
  • Operations Research