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Making decision toward overseas construction projects

An application based on adaptive neuro fuzzy system

Autor(en):




Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Engineering, Construction and Architectural Management, , n. 2, v. 26
Seite(n): 285-302
DOI: 10.1108/ecam-01-2018-0016
Abstrakt:

Purpose

The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS).

Design/methodology/approach

This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed.

Findings

The result shows that optimum ANFIS model indicating RMSE andRscores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate.

Research limitations/implications

The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity.

Originality/value

This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1108/ecam-01-2018-0016.
  • Über diese
    Datenseite
  • Reference-ID
    10576707
  • Veröffentlicht am:
    26.02.2021
  • Geändert am:
    26.02.2021
 
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