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A Development Model for Identifying the Uncertainty Sources and Their Impacts on Bridge Construction Projects

Author(s): ORCID
ORCID
ORCID
ORCID
Medium: journal article
Language(s): English
Published in: The Baltic Journal of Road and Bridge Engineering, , n. 1, v. 18
Page(s): 140-166
DOI: 10.7250/bjrbe.2023-18.592
Abstract:

Bridge construction projects are rife with uncertainty because of their unique features, from execution of the work, time estimation, inspection and assessment to fund allocation. Therefore, a critical step is recognise and categorise the uncertainties associated in bridge building in order to meet project objectives in terms of quality, cost, schedule, environmental, safety, and technical indicators. Various models, however, have been created to detect and prioritise the uncertainty. One of the most commonly used approaches for dealing with uncertainty is the spherical fuzzy set. To formulate an issue, this technique uses a mathematical procedure. The analytic hierarchy process (AHP), on the other hand, is a computer technique that solves a complicated problem by breaking it down into numerous basic problems. A hybrid model based on spherical fuzzy sets and AHP (SAHP) can benefit from both approaches. This study proposes a SAHP based on group decision making (GSAHP) to prioritise the sources of uncertainty in bridge construction projects. Likewise, a modified algorithm is proposed for checking the consistency of the spherical fuzzy matrices. To show the model potential, a real case study is illustrated and evaluated. The model demonstrates its capabilities in modelling uncertainty under an environment with a number of unknown components. The findings reveal that the “delays” factor is of the highest, and the “project team conflicts” parameter is of the least importance. The research findings could be used by decision makers and managers to develop preventive measures.

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: 10.7250/bjrbe.2023-18.592.
  • About this
    data sheet
  • Reference-ID
    10725613
  • Published on:
    30/05/2023
  • Last updated on:
    30/05/2023
 
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