Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

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.


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Disputes, frequently encountered in construction projects, can substantially affect project success, necessitating a clear understanding of how and why disputes occur. Previous studies on disputes mostly yielded exhaustive lists or hierarchies of possible causes of disputes, which can hardly be used to understand how these causes come together to form a dispute. To address this gap, this study provides an alternative approach to understand the underlying causes of disputes, and their relationship within a s...
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In this study, results of a comparison on benchmark test problems are presented to investigate the performance of Primavera V.4.1 with its two resource allocation priority rules and MS Project 2003. Resource allocation capabilities of the packages are measured in terms of deviation from the upper bound of the minimum makespan. Resource constrained project scheduling problem instances are taken from PSPLIB which are generated under a factorial design from ProGen. Statistical tests are applied to the results ...
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Construction industry is overwhelmed by increasing number and severity of disputes proving that current practices are insufficient in avoidance. This research argues that in order to forestall and mitigate construction disputes, prediction models should be developed by utilizing machine learning algorithms. The research suggests developing three distinct models; (1) dispute occurrence prediction model, (2) potential compensation prediction model, and (3) resolution method selection model. For this reason, a...
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Organizational memory formation and its effective utilization is a source of competitive advantage especially in project-based industries such as construction industry as it may eliminate potential problems in projects leading to higher profitability and less errors. However, project-based nature also poses challenges for establishing organizational memory as it is hard to capture knowledge of temporary project teams and transfer different types of knowledge between projects. This study presents a “Lessons ...
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Published inactivation data of Listeria innocua by high hydrostatic pressure (HHP) were used to compare the prediction capabilities of response surface methodology (RSM) and the proposed Weibull model. A quadratic function was used for the secondary modeling of the reduced Weibull model's time constant parameter so that this model consisted of pressure, temperature and time just like RSM equation. Application of each model was shown separately. The usability, differences and similarities of these models wer...
Citation Formats
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: