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Scenario based evaluation of a cost risk model through sensitivity analysis

Author(s):

Medium: journal article
Language(s): English
Published in: Engineering, Construction and Architectural Management, , n. 4, v. 22
Page(s): 403-423
DOI: 10.1108/ecam-09-2014-0121
Abstract:

Purpose

Actual costs frequently deviate from the estimated costs in either favorable or adverse direction in construction projects. Conventional cost evaluation methods do not take the uncertainty and correlation effects into account. In this regard, a simulation-based cost risk analysis model, the Correlated Cost Risk Analysis Model, previously has been proposed to evaluate the uncertainty effect on construction costs in case of correlated costs and correlated risk-factors. The purpose of this paper is to introduce the detailed evaluation of the Cost Risk Analysis Model through scenario and sensitivity analyses.

Design/methodology/approach

The evaluation process consists of three scenarios with three sensitivity analyses in each and 28 simulations in total. During applications, the model’s important parameter called the mean proportion coefficient is modified and the user-dependent variables like the risk-factor influence degrees are changed to observe the response of the model to these modifications and to examine the indirect, two-sided and qualitative correlation capturing algorithm of the model. Monte Carlo Simulation is also applied on the same data to compare the results.

Findings

The findings have shown that the Correlated Cost Risk Analysis Model is capable of capturing the correlation between the costs and between the risk-factors, and operates in accordance with the theoretical expectancies.

Originality/value

Correlated Cost Risk Analysis Model can be preferred as a reliable and practical method by the professionals of the construction sector thanks to its detailed evaluation introduced in this paper.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1108/ecam-09-2014-0121.
  • About this
    data sheet
  • Reference-ID
    10576476
  • Published on:
    26/02/2021
  • Last updated on:
    26/02/2021
 
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