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Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions
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Date
2022-9-01
Author
AYHAN, MURAT
Dikmen Toker, İrem
Birgönül, Mustafa Talat
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This paper compares classification performances of machine learning (ML) techniques for forecasting dispute resolutions in construction projects, thereby mitigating the impacts of potential disputes Findings revealed that resolution cost and duration, contractor type, dispute source, and occurrence of changes were the most influential factors on dispute resolution method (DRM) preferences. The promising accuracy of the majority voting classifier (89.44%) indicates that the proposed model can provide decision-support in identification of potential resolutions. Decision-makers can avoid unsatisfactory processes using these forecasts. This paper demonstrated the effectiveness of ML techniques in classification of DRMs, and the proposed prediction model outperformed previous studies.
Subject Keywords
Construction disputes
,
dispute resolution methods
,
multiclass classification
,
dispute management
,
ARTIFICIAL-INTELLIGENCE
,
CONSTRUCTION-INDUSTRY
,
NEURAL-NETWORK
,
PREDICTION
,
MODEL
,
KNOWLEDGE
URI
https://hdl.handle.net/11511/101081
Journal
TEKNIK DERGI
DOI
https://doi.org/10.18400/tekderg.930076
Collections
Department of Civil Engineering, Article
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BibTeX
M. AYHAN, İ. Dikmen Toker, and M. T. Birgönül, “Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions,”
TEKNIK DERGI
, vol. 33, no. 5, pp. 12577–12600, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/101081.