DIRECTORATE OF HIGH TECHNICS BOARD DECISIONS IN PUBLIC CONSTRUCTION PROJECTS: A MACHINE LEARNING APPROACH TO DISPUTE RESOLUTION

Abstract

Disputes arising in public construction projects can lead to time and cost overruns, which negatively impact project processes. This study developed a machine-learning model to predict such disputes by analyzing decisions from the Directorate of High Technics Board of Public Works. Through a literature review and content analysis, a total of 18 legal factors were identified. Using the CorrelationAttributeEvaluation method, 11 out of the 17 input legal factors were selected as target legal factors for inclusion in the model. The MultiBoostAB algorithm demonstrated the best performance, achieving an accuracy rate of 70.41%. The content analysis method employed in the model ensured an objective identification of legal factors specific to the legal context and played a significant role in interpreting the decision texts. However, the limited size of the dataset was identified as a limitation, affecting the model’s generalizability. Future studies are recommended to expand the dataset, apply over-sampling techniques, and integrate natural language processing methods. In addition, analysing decision texts using methods such as Natural Language Processing (NLP), BERT and RoBERTa to identify legal factors and establish prediction models can strengthen the identification of factors specific to the legal context and the accuracy of predictions.

Keywords

• Construction disputes
• content analysis
• dispute resolution
• machine learning
• prediction models.

Supporting Institution

Scientific and Technological Research Council of Turkiye (TUBITAK)

Project Number

224K720

Ethical Statement

The authors declare that all ethical guidelines including authorship, citation, data reporting, and publishing original research are followed.

Thanks

This work, created from the Ph.D. thesis of Mahmut SARI, was supported by the Scientific and Technological Research Council of Turkiye (TUBITAK) through 1002 - B Emergency Support Module under grant 224K720.

References

1. M. Cevikbas, "Identification of dispute sources in the construction industry via court files," Turkish Journal of Civil Engineering, vol. 34, no. 2, pp. 57-76, 2023.
2. M. Sarı, B. Sayın, and C. Akçay, "Classification and resolution procedure for disputes in public construction projects," Revista de la Construcción. Journal of Construction, vol. 20, no. 2, pp. 259-276, 08/19 2021, doi: https://doi.org/10.7764/RDLC.20.2.259.
3. M. Sarı, S. Bayram, and E. Aydemir, "When defendants speak: Quantifying the predictive value of defence arguments in construction litigation," Journal of Construction Engineering, Management & Innovation, vol. 8, no. 1, pp. 64-88, 2025.
4. A. L. Campos Gutierrez, K. Panuwatwanich, and A. Walker, "Learning from the past: analysis of factors contributing to construction project disputes in Australia," in CIB World Building Congress 2013, 2013: Queensland University of Technology.
5. S. Alshahrani, "Dispute resolution methods in the construction industry sector in the Kingdom of Saudi Arabia," in MATEC web of conferences, 2017, vol. 138: EDP Sciences, p. 02015.
6. H. Aladağ, G. Demirdöğen, and Z. Işık, "Mücbir sebep faktörü olarak COVID-19 salgınının inşaat projelerinde oluşturduğu uyuşmazlık etmenleri ve etkilerinin incelenmesi," (in tr), Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 36, no. 3, pp. 1213-1228, May 2021, doi: 10.17341/gazimmfd.784968.
7. S. El-Sayegh, I. Ahmad, M. Aljanabi, R. Herzallah, S. Metry, and O. El-Ashwal, "Construction disputes in the UAE: Causes and resolution methods," Buildings, vol. 10, no. 10, p. 171, 2020.
8. R. Awwad, B. Barakat, and C. Menassa, "Understanding dispute resolution in the Middle East region from perspectives of different stakeholders," Journal of Management in Engineering, vol. 32, no. 6, p. 05016019, 2016.

9. M. Sarı and S. Bayram, "İnşaat Anlaşmazlıklarının Önlenmesi ve Çözümünde Etkili Faktörler: Erken Aşamalarda ve Resmi Yargı Sürecindeki Rolü," (in tr), Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, vol. 41, no. 1, pp. 133-142, April 2025. [Online]. Available: https://dergipark.org.tr/tr/pub/erciyesfen/issue/91637/1603230.
10. E. Erdis and S. A. Ozdemir, "Analysis of technical specification-based disputes in construction industry," KSCE Journal of Civil Engineering, vol. 17, no. 7, pp. 1541-1550, 2013/11/01 2013, doi: 10.1007/s12205-013-0016-7.
11. I. Cakmak Pinar and E. Cakmak, "An Analysis of Causes of Disputes in the Construction Industry Using Analytical Hierarchy Process (AHP)," in AEI 2013, (Proceedings, 2013, pp. 94-102.
12. M. Mishmish and S. M. El-Sayegh, "Causes of claims in road construction projects in the UAE," International Journal of Construction Management, vol. 18, no. 1, pp. 26-33, 2018/01/02 2018, doi: 10.1080/15623599.2016.1230959.
13. E. Temiz, "Türkiye'deki yapım projelerinde anlaşmazlıklara neden olan faktörlerin analizi," Master’s Thesis, Institute of Science, Istanbul Technical University, 2020. [Online]. Available: https://polen.itu.edu.tr:8443/server/api/core/bitstreams/6e610957-da3d-4055-b644-3fb9e63d87d5/content#page=32.20
14. M. Abeynayake, "Special Features and Experiences of the Construction Industry Arbitration in Sri Lanka," in meeting of International Conference on Building Education and Research, Salford, United Kingdom, 2008.
15. D. Ilter, "Identification of the relations between dispute factors and dispute categories in construction projects," International Journal of Law in the Built Environment, vol. 4, no. 1, pp. 45-59, 2012, doi: 10.1108/17561451211211732.
16. S. Assaf, M. A. Hassanain, A. Abdallah, A. M. Sayed, and A. Alshahrani, "Significant causes of claims and disputes in construction projects in Saudi Arabia," Built Environment Project and Asset Management, vol. 9, no. 5, pp. 597-615, 2019.
17. D. Treacy, J. P. Spillane, and P. Tansey, "Construction disputes in small to medium enterprise’s in Ireland during recession: Identification of critical factors," International Journal of Law in the Built Environment, vol. 8, no. 1, pp. 21-41, 2016.
18. A. Barman and C. Charoenngam, "Decisional uncertainties in construction projects as a cause of disputes and their formal legal interpretation by the courts: Review of legal cases in the United Kingdom," Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, vol. 9, no. 3, p. 04517011, 2017.
19. S. Almutairi, J. Kashiwagi, D. Kashiwagi, and K. Sullivan, "Factors causing construction litigation in Saudi Arabia," Journal for the Advancement of Performance Information and Value, vol. 7, no. 1, pp. 58-58, 2015.
20. S. O. Cheung and K. H. Y. Pang, "Anatomy of construction disputes," Journal of construction engineering and management, vol. 139, no. 1, pp. 15-23, 2013.
21. M. B. Jelodar, T. W. Yiu, and S. Wilkinson, "Dispute manifestation and relationship quality in practice," Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, vol. 8, no. 1, p. C4515003, 2016.
22. M. M. Kumaraswamy, "Conflicts, claims and disputes in construction," Engineering, Construction and Architectural Management, vol. 4, no. 2, pp. 95-111, 1997, doi: https://doi.org/10.1108/eb021042.
23. C. Semple, F. T. Hartman, and G. Jergeas, "Construction Claims and Disputes: Causes and Cost/Time Overruns," Journal of Construction Engineering and Management, vol. 120, no. 4, pp. 785-795, 1994, doi: doi:10.1061/(ASCE)0733-9364(1994)120:4(785).
24. I. Illankoon, V. W. Tam, K. N. Le, and K. Ranadewa, "Causes of disputes, factors affecting dispute resolution and effective alternative dispute resolution for Sri Lankan construction industry," International Journal of Construction Management, vol. 22, no. 2, pp. 218-228, 2022.
25. Arcadis, "14th Annual Construction Disputes Report: Managing Construction Disputes in the Digital Age," 2024. [Online]. Available: [suspicious link removed]
26. General Specifications for Construction Works – Article 51, M. o. E. Republic of Türkiye, Urbanization and Climate Change Regulation 51, 2024.
27. Law No. 4735 on Public Procurement Contracts – Additional Article 2, M. o. E. Republic of Türkiye, Urbanization and Climate Change Law Additional Article 2, 2020.
28. B. Un, E. Erdis, S. Aydınlı, O. Genc, and O. Alboga, "Forecasting the outcomes of construction contract disputes using machine learning techniques," Engineering, Construction and Architectural Management, vol. ahead-of-print, no. ahead-of-print, 2024, doi: 10.1108/ECAM-05-2023-0510.
29. N. Bagherian-Marandi, M. Ravanshadnia, and M.-R. Akbarzadeh-T, "Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes," Artificial Intelligence and Law, vol. 29, no. 4, pp. 453-484, 2021/12/01 2021, doi: 10.1007/s10506-021-09281-9.
30. D. Arditi, F. E. Oksay, and O. B. Tokdemir, "Predicting the Outcome of Construction Litigation Using Neural Networks," Computer-Aided Civil and Infrastructure Engineering, vol. 13, no. 2, pp. 75-81, 1998/03/01 1998, doi: https://doi.org/10.1111/0885-9507.00087.
31. N. B. Chaphalkar, K. C. Iyer, and S. K. Patil, "Prediction of outcome of construction dispute claims using multilayer perceptron neural network model," International Journal of Project Management, vol. 33, no. 8, pp. 1827-1835, 2015/11/01/ 2015, doi: https://doi.org/10.1016/j.ijproman.2015.09.002.
32. O. Alshboul, A. Shehadeh, R. E. A. Mamlook, G. Almasabha, A. S. Almuflih, and S. Y. Alghamdi, "Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects," Sustainability, vol. 14, no. 15, p. 9303, 2022. [Online]. Available: https://www.mdpi.com/2071-1050/14/15/9303.
33. H. Anysz, M. Apollo, and B. Grzyl, "Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools," Symmetry, vol. 13, no. 5, p. 744, 2021. [Online]. Available: https://www.mdpi.com/2073-8994/13/5/744.
34. J.-S. Chou and C. Lin, "Predicting Disputes in Public-Private Partnership Projects: Classification and Ensemble Models," Journal of Computing in Civil Engineering, vol. 27, pp. 51-60, 2013, doi: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000197.
35. M. Ayhan, I. Dikmen, and M. T. Birgönül, "Classifying Compensations in Construction Disputes Using Machine Learning Techniques," Journal of Engineering Research, 2021.
36. D. o. H. T. B. DHTB, DHTB Decisions and Opinions 2005-2020. Turkish Republic Ministry of Environment and Urban Planning, 2020.
37. U. H. Graneheim, B.-M. Lindgren, and B. Lundman, "Methodological challenges in qualitative content analysis: A discussion paper," Nurse Education Today, vol. 56, pp. 29-34, 2017/09/01/ 2017, doi: https://doi.org/10.1016/j.nedt.2017.06.002.
38. D. Arditi and O. B. Tokdemir, "Using Case-Based Reasoning to Predict the Outcome of Construction Litigation," Computer-Aided Civil and Infrastructure Engineering, vol. 14, no. 6, pp. 385-393, 1999/11/01 1999, doi: https://doi.org/10.1111/0885-9507.00157.
39. T. Mahfouz and A. Kandil, "Factors Affecting Litigation Outcomes of Differing Site Conditions (DSC) Disputes: A Logistic Regression Models (LRM)," in Building a Sustainable Future, 2009, pp. 239-248.
40. T. Mahfouz and A. Kandil, "Litigation Outcome Prediction of Differing Site Condition Disputes through Machine Learning Models," Journal of Computing in Civil Engineering, vol. 26, pp. 298-308, 2012, doi: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000148.
41. X. Zheng, Liu, Y., Jiang, J., Thomas, L.M., Su, N., "Predicting The Litigation Outcome of PPP Project Disputes Between Public Authority and Private Partner Using an Ensemble Model," Journal of Business Economics and Management, vol. 22, pp. 320-345, 2021, doi: https://doi.org/10.3846/jbem.2021.13219.
42. C. A. Johnson, "Content-Analytic Techniques and Judicial Research," American Politics Quarterly, vol. 15, no. 1, pp. 169-197, 1987, doi: 10.1177/1532673x8701500108.
43. M. A. Hall and R. F. Wright, "Systematic Content Analysis of Judicial Opinions," Calif. L. Rev., vol. 96, pp. 63-63, 2008. [Online]. Available: http://lawcat.berkeley.edu/record/1121706/files/fulltext.pdf.
44. E. H. Frank, M.; Holmes, G. "CorrelationAttributeEval (weka.attributeSelection)." University of Waikato. https://weka.sourceforge.io/doc.dev/weka/attributeSelection/CorrelationAttributeEval.html (accessed 06.10.2025, 2025).
45. E. P. M. de Sousa, C. Traina, A. J. M. Traina, L. Wu, and C. Faloutsos, "A fast and effective method to find correlations among attributes in databases," Data Mining and Knowledge Discovery, vol. 14, no. 3, pp. 367-407, 2007/06/01 2007, doi: 10.1007/s10618-006-0056-4.
46. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," Journal of machine learning research, vol. 3, no. Mar, pp. 1157-1182, 2003.
47. M. A. Hall, "Correlation-based feature selection for machine learning," The University of Waikato, 1999.
48. M. Dash and H. Liu, "Feature selection for classification," Intelligent Data Analysis, vol. 1, no. 1, pp. 131-156, 1997/01/01/ 1997, doi: https://doi.org/10.1016/S1088-467X(97)00008-5.
49. N. Pudjihartono, T. Fadason, A. W. Kempa-Liehr, and J. M. O'Sullivan, "A review of feature selection methods for machine learning-based disease risk prediction," Frontiers in bioinformatics, vol. 2, p. 927312, 2022.
50. C. J. Ferguson, "An effect size primer: a guide for clinicians and researchers," Professional psychology: Research and practice, vol. 40, no. 5, p. 532, 2009.
51. J. Cohen, "A power primer," (in eng), Psychol Bull, vol. 112, no. 1, pp. 155-9, Jul 1992, doi: 10.1037//0033-2909.112.1.155.
52. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Ijcai, 1995, vol. 14, no. 2: Montreal, Canada, pp. 1137-1145.
53. J.-H. Kim, "Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap," Computational Statistics & Data Analysis, vol. 53, no. 11, pp. 3735-3745, 2009/09/01/ 2009, doi: https://doi.org/10.1016/j.csda.2009.04.009.
54. G. I. Webb, "MultiBoosting: A Technique for Combining Boosting and Wagging," Machine Learning, vol. 40, no. 2, pp. 159-196, 2000/08/01 2000, doi: 10.1023/A:1007659514849.
55. N. C. Oza and S. J. Russell, "Online Bagging and Boosting," presented at the Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, 2001. [Online]. Available: https://proceedings.mlr.press/r3/oza01a.html.
56. K. M. Ting, "Confusion Matrix," in Encyclopedia of Machine Learning, C. Sammut and G. I. Webb Eds. Boston, MA: Springer US, 2010, p. 209.
57. M. Ayhan, I. Dikmen, and M. T. Birgönül, "Predicting the Occurrence of Construction Disputes Using Machine Learning Techniques," Journal of Construction Engineering and Management, vol. 147, no. 4, 2021, doi: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002027.
58. J.-S. Chou, C. Tsai, and Y. Lu, "Project Dispute Prediction by Hybrid Machine Learning Techniques," Journal of Civil Engineering and Management, vol. 19, no. 4, pp. 505-517, 2013, doi: https://doi.org/10.3846/13923730.2013.768544.
59. D. Arditi and T. Pulket, "Predicting the Outcome of Construction Litigation Using Boosted Decision Trees," Journal of Computing in Civil Engineering, vol. 19, no. 4, pp. 387-393, 2005/10/01 2005, doi: https://doi.org/10.1061/(ASCE)0887-3801(2005)19:4(387).
60. T. Pulket and D. Arditi, "Construction litigation prediction system using ant colony optimization," Construction Management and Economics, vol. 27, no. 3, pp. 241-251, 2009/03/01 2009, doi: https://doi.org/10.1080/01446190802714781.
61. T. Pulket and D. Arditi, "Universal Prediction Model for Construction Litigation," Journal of Computing in Civil Engineering, vol. 23, no. 3, pp. 178-187, 2009, doi: https://doi.org/10.1061/(ASCE)0887-3801(2009)23:3(178).
62. D. Arditi and T. Pulket, "Predicting the Outcome of Construction Litigation Using an Integrated Artificial Intelligence Model," Journal of Computing in Civil Engineering, vol. 24, no. 1, pp. 73-80, 2010, doi: https://doi.org/10.1061/(ASCE)0887-3801(2010)24:1(73).
63. K. W. Chau, "Predicting Construction Litigation Outcome Using Particle Swarm Optimization," in Innovations in Applied Artificial Intelligence, M. Ali and F. Esposito, Eds., 2005: Springer Berlin Heidelberg, pp. 571-578, doi: https://doi.org/10.1007/11504894_80.
64. K. W. Chau, "Prediction of Construction Litigation Outcome – A Case-Based Reasoning Approach," in Advances in Applied Artificial Intelligence, M. Ali and R. Dapoigny, Eds., 2006: Springer Berlin Heidelberg, pp. 548-553, doi: https://doi.org/10.1007/11779568_59.
65. J.-H. Chen and S. C. Hsu, "Hybrid ANN-CBR model for disputed change orders in construction projects," Automation in Construction, vol. 17, no. 1, pp. 56-64, 2007/11/01/ 2007, doi: https://doi.org/10.1016/j.autcon.2007.03.003.
66. R. Pandey, M. Dhoundiyal, and A. Kumar, "Correlation analysis of big data to support machine learning," in 2015 Fifth International Conference on Communication Systems and Network Technologies, 2015: IEEE, pp. 996-999, doi: https://doi.org/10.1109/CSNT.2015.32.
67. J. Kolluri, V. K. Kotte, M. S. B. Phridviraj, and S. Razia, "Reducing Overfitting Problem in Machine Learning Using Novel L1/4 Regularization Method," in 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184), 15-17 June 2020 2020, pp. 934-938, doi: 10.1109/ICOEI48184.2020.9142992.