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A framework to demonstrate the utility and safety of the observational method.

  • Published In: Geomechanik und Tunnelbau, 2024, v. 17, n. 2. P. 91 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Roper, Thomas; Bertuzzi, Robert; Oliveira, David; Karlovšek, Jurij 3 of 3

Abstract

Underground engineering projects are often characterised by significant uncertainties, leading to complex decision‐making when selecting appropriate design methods. Traditional approaches, such as the observational method (OM), have been widely used but rarely explicitly account for the integration of risk, costs, construction timeframes and engineering judgement. Successfully reaching a consensus to implement the OM on tunnelling projects can be challenging and requires ensuring stakeholders understand how the method impacts structural safety. Fostering such understanding can be an arduous process. A risk‐based decision framework is introduced using expected utility theory to aid the decision to choose the OM, or not. The framework integrates risk, cost, construction timelines and engineering judgement within an economic decision model using a probabilistic approach. It is tested on a Sydney, Australia case study. A Monte Carlo model analysis examines contingency actions and structural failure probability when applying the OM. The case study highlights the significance of considering indirect costs in complex decisions and harnesses experienced professionals' insights for a comprehensive risk and cost assessment. We conclude that merging probabilistic‐based design with OM enhances the ability to demonstrate structural safety and economic factors within a unified model. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Geomechanik und Tunnelbau. 2024/04, Vol. 17, Issue 2, p91
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2024
  • ISSN:1865-7362
  • DOI:10.1002/geot.202300041
  • Accession Number:176585893
  • Copyright Statement:Copyright of Geomechanik und Tunnelbau is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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