Early Prediction of Construction Disputes: Decision Support Systems with Machine Learning Techniques

Year 2026, Volume: 37 Issue: 2

Mahmut Sarı , Savaş Bayram , Emrah Aydemir

https://doi.org/10.18400/tjce.1618975

Abstract

This study aims to predict the outcomes of construction disputes before they proceed to litigation and to foster a constructive environment between parties. Within the scope of the study, a total of 24 legal factors; 14 legal factors were identified through extensive literature review and 10 legal factors were identified through content analysis. These legal factors were used in three stages: Pre-Litigation (A, B) and Post-Litigation. Legal factors with significant relationships were tested with 24 different machine learning algorithms. NB Tree, Logit Boost and LMT algorithms achieved 63.79%, 63.66% and 86.90% accuracy for models A, B and C, respectively.

Keywords

Construction disputes , machine learning algorithms , dispute resolution

Ethical Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supporting Institution

the Scientific and Technological Research Council of Turkiye (TUBITAK)

Project Number

122G126

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 the Innovative Solutions Research Projects Support Program in Social Sciences and Humanities (3005) under grant 122G126.

References

• Giang, D.T.H. and L. Sui Pheng, Role of construction in economic development: Review of key concepts in the past 40 years. Habitat International, 2011. 35(1): p. 118-125.
• Ringen, K. and A. Englund, The Construction Industry. Annals of the New York Academy of Sciences, 2006. 1076(1): p. 388-393.
• Iliev, B.Z., World Construction Market. Review of Business and Economics Studies, 2019. 7: p. 32-36.
• Lean, C.S., Empirical tests to discern linkages between construction and other economic sectors in Singapore. Construction Management and Economics, 2001. 19(4): p. 355-363.
• Jaber, F.K., N.A. Jasim, and F.M. Al-Zwainy, Forecasting techniques in construction industry: earned value indicators and performance models. Scientific Review Engineering and Environmental Sciences (SREES), 2020. 29(2): p. 234-243.
• Li, J., D. Greenwood, and M. Kassem, Blockchain in the built environment and construction industry: A systematic review, conceptual models and practical use cases. Automation in construction, 2019. 102: p. 288-307.
• Mengistu, D.G. and G. Mahesh, Challenges in developing the Ethiopian construction industry. African Journal of Science, Technology, Innovation and Development, 2020. 12(4): p. 373-384.
• Fenn, P., D. Lowe, and C. Speck, Conflict and dispute in construction. Construction Management and Economics, 1997. 15(6): p. 513-518.
• Sarı, M., B. Sayın, and C. Akçay, Classification and resolution procedure for disputes in public construction projects. Revista de la Construcción. Journal of Construction, 2021. 20(2): p. 259-276.
• Dobrucali, E., et al., Exploring the Impact of COVID-19 on the United States Construction Industry: Challenges and Opportunities. IEEE Transactions on Engineering Management, 2022: p. 1-13.
• Sarı, M., Savaş Bayram, and E. Aydemir, Construction-Related Disputes: A Comprehensive Bibliometric Investigation in The 8th International Project and Construction Management Conference (IPCMC 2024), S.U. Kerim Koç, Serkan Kıvrak, Editor. 2024: İstanbul. p. 507-518.
• Cheung, S.O. and T.W. Yiu, Are Construction Disputes Inevitable? IEEE Transactions on Engineering Management, 2006. 53(3): p. 456-470.
• Arcadis, C., Global Construction Disputes Report 2020, in Global Construction Disputes Report. 2020, Arcadis Company
• Arcadis, C., Global Construction Disputes Report 2022, in Global Construction Disputes Report 2022.
• Tanriverdi, C., et al., Causal mapping to explore emergence of construction disputes. Journal of Civil Engineering and Management, 2021. 27(5): p. 288-302.
• Kumaraswamy, M.M., Conflicts, claims and disputes in construction. Engineering, Construction and Architectural Management, 1997. 4(2): p. 95-111.
• Heue, L. and S. Penrod, Predicting the Outcomes of Disputes: Consequences for Disputant Reactions to Procedures and Outcomes. Journal of Applied Social Psychology, 1994. 24(3): p. 260-283.
• Zheng, X., 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, 2021. 22: p. 320-345.
• Ayhan, M., I. Dikmen, and M.T. Birgönül, Predicting the Occurrence of Construction Disputes Using Machine Learning Techniques. Journal of Construction Engineering and Management, 2021. 147(4).
• Arditi, D., F.E. Oksay, and O.B. Tokdemir, Predicting the Outcome of Construction Litigation Using Neural Networks. Computer-Aided Civil and Infrastructure Engineering, 1998. 13(2): p. 75-81.
• Arditi, D. and O.B. Tokdemir, Using Case-Based Reasoning to Predict the Outcome of Construction Litigation. Computer-Aided Civil and Infrastructure Engineering, 1999. 14(6): p. 385-393.
• Mahfouz, T. and A. Kandil, Factors Affecting Litigation Outcomes of Differing Site Conditions (DSC) Disputes: A Logistic Regression Models (LRM), in Building a Sustainable Future. 2009. p. 239-248.
• Mahfouz, T. and A. Kandil, Litigation Outcome Prediction of Differing Site Condition Disputes through Machine Learning Models. Journal of Computing in Civil Engineering, 2012. 26: p. 298-308.
• Chaphalkar, N.B., 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, 2015. 33(8): p. 1827-1835.
• Treacy, T.B., Use of Alternative Dispute Resolution in the Construction Industry. Journal of Management in Engineering, 1995. 11(1): p. 58-63.
• Justice, T.M.o., General Directorate of Judicial Records and Statistics. 2023.
• Gill, A., et al., Comparison of the effects of litigation and ADR in South-East Queensland. International Journal of Construction Management, 2015. 15(3): p. 254-263.
• Parikh, D., G.J. Joshi, and D.A. Patel, Development of Prediction Models for Claim Cause Analyses in Highway Projects. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 2019. 11(4): p. 04519018.
• Hall, M.A. and R.F. Wright, Systematic Content Analysis of Judicial Opinions. Calif. L. Rev., 2008. 96: p. 63-63.
• Do, S.T., V.T. Nguyen, and N.H. Nguyen, Relationship networks between variation orders and claims/disputes causes on construction project performance and stakeholder performance. Engineering, Construction and Architectural Management, 2022.
• Francis, M., T. Ramachandra, and S. Perera, Disputes in Construction Projects: A Perspective of Project Characteristics. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 2022. 14(2).
• Echternach--Jaubert, M., R. Pellerin, and L. Joblot, Litigation management process in construction industry. Procedia Computer Science, 2021. 181: p. 678-684.
• Tazelaar, F. and C. Snijders, Dispute resolution and litigation in the construction industry. Evidence on conflicts and conflict resolution in The Netherlands and Germany. Journal of Purchasing and Supply Management, 2010. 16(4): p. 221-229.
• Haugen, T. and A. Singh, Dispute Resolution Strategy Selection. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 2015. 7(3).
• Arditi, D. and T. Pulket, Predicting the Outcome of Construction Litigation Using Boosted Decision Trees. Journal of Computing in Civil Engineering, 2005. 19(4): p. 387-393.
• Chau, K.W. Predicting Construction Litigation Outcome Using Particle Swarm Optimization. in Innovations in Applied Artificial Intelligence. 2005. Springer Berlin Heidelberg.
• Chau, K.W. Prediction of Construction Litigation Outcome – A Case-Based Reasoning Approach. in Advances in Applied Artificial Intelligence. 2006. Springer Berlin Heidelberg.
• Chen, J.-H. and S.C. Hsu, Hybrid ANN-CBR model for disputed change orders in construction projects. Automation in Construction, 2007. 17(1): p. 56-64.
• Pulket, T. and D. Arditi, Construction litigation prediction system using ant colony optimization. Construction Management and Economics, 2009. 27(3): p. 241-251.
• Pulket, T. and D. Arditi, Universal Prediction Model for Construction Litigation. Journal of Computing in Civil Engineering, 2009. 23(3): p. 178-187.
• Arditi, D. and T. Pulket, Predicting the Outcome of Construction Litigation Using an Integrated Artificial Intelligence Model. Journal of Computing in Civil Engineering, 2010. 24(1): p. 73-80.
• Chou, J.-S., C. Tsai, and Y. Lu, Project Dispute Prediction by Hybrid Machine Learning Techniques. Journal of Civil Engineering and Management, 2013. 19(4): p. 505-517.
• Hyett, N., A. Kenny Dr, and V. Dickson-Swift Dr, Methodology or method? A critical review of qualitative case study reports. International Journal of Qualitative Studies on Health and Well-being, 2014. 9(1): p. 23606.
• Sarı, M. and S. Bayram, İnşaat Anlaşmazlıklarının Önlenmesi ve Çözümünde Etkili Faktörler: Erken Aşamalarda ve Resmi Yargı Sürecindeki Rolü. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 2025. 41(1): p. 133-142.
• Sarı, M. and S. Bayram. From courts to boards: Comparative legal analysis of early- stage dispute management in public construction disputes. in 4th International Civil Engineering & Architecture Conference (ICEARC'25). 2025. Trabzon, Türkiye.
• Cevikbas, M., Identification of dispute sources in the construction industry via court files. Turkish Journal of Civil Engineering, 2023. 34(2): p. 57-76.
• Künkcü, H., et al., Operational barriers against the use of smart contracts in construction projects. Turkish Journal of Civil Engineering, 2023. 34(5): p. 81-106.
• Koc, K. and A.P. Gurgun, Causal relationships of readability risks in construction contracts. Teknik Dergi, 2022. 33(2): p. 11823-11846.
• ÇAKMAK, P.I., Causes of disputes in the Turkish construction industry: Case of public sector projects. Istanbul Technical University, AZ, 2016. 13(3): p. 109-118.
• Sarı, M., S. Bayram, and E. Aydemir, When defendants speak: Quantifying the predictive value of defence arguments in construction litigation. Journal of Construction Engineering, Management & Innovation, 2025. 8(1): p. 64-88.
• Finfgeld-Connett, D., Use of content analysis to conduct knowledge-building and theory-generating qualitative systematic reviews. Qualitative Research, 2014. 14(3): p. 341-352.
• Dumay, J. and L. Cai, A review and critique of content analysis as a methodology for inquiring into IC disclosure. Journal of Intellectual Capital, 2014. 15(2): p. 264-290.
• Hukukturk. Database. 2024 [cited 2024 22.03.2024]; Available from: https://www.hukukturk.com/yargitay-kararlari.
• Court of Cassation, Average Case Processing Time in 2023. 2023, Court of Cassation. [cited 2023]; Available from: https://www.yargitay.gov.tr/item/51/istatistikler
• Holsti, O.R., Content analysis for the social sciences and humanities. First ed. Reading. MA: Addison-Wesley (content analysis). 1969.
• Smith, H., Strategies of social research: The methodological imagination (Prentice-Hall methods of social science series). 1975: Prentice-Hall. 509.
• Yıldırım, A. and H. Simsek, Sosyal bilimlerde nitel araştırma yöntemleri (11 baski: 1999-2018). 1999.
• Falkingham, L.T. and R. Reeves, Context analysis—A technique for analysing research in a field, applied to literature on the management of R&D at the section level. Scientometrics, 1998. 42(2): p. 97-120.
• Kisi, K., R. Kayastha, and Y. Chitrakar, Construction Claims and Payment Disputes Analysis: Alternative Dispute Resolution to Litigation. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 2023. 15(1): p. 06522005.
• Stipanowich, T.J., Managing construction conflict: unfinished revolution, continuing evolution. Constr. Law., 2014. 34: p. 13.
• Pearson, K., X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1900. 50(302): p. 157-175.
• Olanusi, J. and S. Samuel, Application of Chi-Square Test to Determine Architectural Impact on Church Patronage. International Journal of Research and Innovation in Social Science, 2023. 7(6): p. 605-616.
• Ihekuna, S.O. and K.C. Dozie, Chi-square test in time series data. Asian Journal of Probability and Statistics, 2022. 20(3): p. 49-63.
• Lieberson, S., Measuring population diversity. American Sociological Review, 1969: p. 850-862.
• Agresti, A., Categorical data analysis. 3rd Edition ed. Vol. 792. 2013: John Wiley & Sons. 742.
• Akoglu, H., User's guide to correlation coefficients. Turkish Journal of Emergency Medicine, 2018. 18(3): p. 91-93.
• Somers, R.H., A new asymmetric measure of association for ordinal variables. American sociological review, 1962: p. 799-811.
• Raschka, S., Model evaluation, model selection, and algorithm selection in machine learning. arXiv.org preprint arXiv:1811.12808, 2018.
• He, H. and E.A. Garcia, Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 2009. 21(9): p. 1263-1284.
• Kaur, H., H.S. Pannu, and A.K. Malhi, A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions. ACM Comput. Surv., 2019. 52(4): p. Article 79.
• Assunção, G.O., R. Izbicki, and M.O. Prates, Is Augmentation Effective in Improving Prediction in Imbalanced Datasets? Journal of Data Science, 2024: p. 1-16.
• Landwehr, N., M. Hall, and E. Frank, Logistic model trees. Machine learning, 2005. 59: p. 161-205.
• Sutton, C.D., Classification and regression trees, bagging, and boosting. Handbook of Statistics, 2005. 24: p. 303-329.
• Witten, I., E. Frank, and M. Hall, Chapter 8 - Ensemble Learning, in Data Mining: Practical Machine Learning Tools and Techniques (Third Edition), I.H. Witten, E. Frank, and M.A. Hall, Editors. 2011, Morgan Kaufmann: Boston. p. 351-373.
• Alpaydin, E., Introduction to machine learning. Fourth ed. 2020: MIT Press.
• Friedman, J., T. Hastie, and R. Tibshirani, Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics, 2000. 28(2): p. 337-407.
• Kohavi, R. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 1996. Portland, Oregon: AAAI Press.
• Uncuoglu, E., et al., Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems. Applied Soft Computing, 2022. 129.
• Ting, K.M., Confusion Matrix, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 209.
• Kakas, A., Accuracy, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 9-10.
• Ting, K.M., Precision, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 780.
• Ting, K.M., Precision and Recall, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 781.
• Goutte, C. and E. Gaussier. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. in Advances in Information Retrieval: 27th European Conference on Information Retrieval Research. 2005. Santiago de Compostela, Spain: Springer.
• Hoo, Z.H., J. Candlish, and D. Teare, What is an ROC curve? Emergency Medicine Journal, 2017. 34(6): p. 357-359.
• Kumar, N. and S. Khatri. Implementing WEKA for medical data classification and early disease prediction. in 3rd International Conference On Computational Intelligence & Communication Technology. 2017. IEEE.
• Davis, J. and M. Goadrich. The relationship between Precision-Recall and ROC curves. in 23rd International Conference On Machine Learning. 2006.
• Matthews, B.W., Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta, 1975. 405(2): p. 442-451.
• Liu, Y., et al., Research on the Matthews correlation coefficients metrics of personalized recommendation algorithm evaluation. International Journal of Hybrid Information Technology, 2015. 8(1): p. 163-172.
• Bulut, F., Örnek tabanlı sınıflandırıcı topluluklarıyla yeni bir klinik karar destek sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 2017. 32(1).
• Collenette, J., K. Atkinson, and T. Bench-Capon, Explainable AI tools for legal reasoning about cases: A study on the European Court of Human Rights. Artificial Intelligence, 2023. 317: p. 103861.
• BuiltInChicago. Relativity Reaches $3.6B Valuation After Recent Investment From Silver Lake. 2021 [cited 2025; Available from: https://www.builtinchicago.org/articles/relativity-silver-lake-funding-3b-valuation?utm_source=chatgpt.com.
• Evans, D.L., et al., Dispute Resolution Enhanced: How Arbitrators and Mediators Can Harness Generative AI. Dispute Resolution Journal, 2024. 78(1).
• Moran, H. Theo Ai Secures 4.2MM Seed Round to Advance AI-Powered Settlement Prediction for Big Law. 2025 [cited 2025; Available from: https://legalfundingjournal.com/theo-ai-secures-4-2mm-seed-round-to-advance-ai-powered-settlement-prediction-for-big-law/.