Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes

Abstract

The dynamic nature and increasing complexity of the construction industry have led to increased conflicts in construction projects. An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more money through litigation. This study aims to introduce a two-layered fuzzy logic model for predicting court decisions in construction contract disputes. 100 cases of construction contract disputes are selected from the courts of Iran. A questionnaire survey is then conducted to extract a set of fuzzy rules for identifying important decision parameters and expert knowledge. Accordingly, a two-layered fuzzy logic-based decision-making architecture is proposed for the prediction model. Furthermore, the fuzzy system is trained based on 10-fold cross-validation. Analysis of results indicates that 51 out of the 100 cases are filed after the dissolution and termination of the contract show a significant impact of these clauses as the root cause in construction contract disputes. Our results present a proposed hierarchical fuzzy system that can correctly predict nearly 60% of the test data. Also, we demonstrate a methodology of using argument before ML to establish interpretable AI models. Based on our findings, a fuzzy model with a hierarchical structure may be used as a simple and efficient method for predicting court decisions in construction contract disputes.

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Data availability statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Arditi D, Oksay FE, Tokdemir OB (1998) Predicting the outcome of construction litigation using neural networks. Comput Aided Civ Infrastruct Eng 13(2):75–81

    Article  Google Scholar 

  2. Arditi D, Pulket T (2005) Predicting the outcome of construction litigation using boosted decision trees. J Comput Civ Eng 19(4):387–393

    Article  Google Scholar 

  3. Arditi D, Pulket T (2010) Predicting the outcome of construction litigation using an integrated artificial intelligence model. J Comput Civ Eng 24(1):73–80

    Article  Google Scholar 

  4. Arditi D, Tokdemir OB (1999) Comparison of case-based reasoning and artificial neural networks. J Comput Civ Eng 13(3):162–169

    Article  Google Scholar 

  5. Besold TR, Kühnberger K-U (2015) Towards integrated neural–symbolic systems for human-level AI: two research programs helping to bridge the gaps. Biol Inspired Cogn Archit 14:97–110

    Google Scholar 

  6. Chau K (2007) Application of a PSO-based neural network in analysis of outcomes of construction claims. Autom Constr 16(5):642–646

    Article  Google Scholar 

  7. Cheeks JR (2003) Multistep dispute resolution in design and construction industry. J Prof Issues Eng Educ Pract 129(2):84–91

    Article  Google Scholar 

  8. Chehayeb A, Al-Hussein M, Flynn P (2007) An integrated methodology for collecting, classifying, and analyzing Canadian construction court cases. Can J Civ Eng 34(2):177–188

    Article  Google Scholar 

  9. Chen J-H, Hsu S (2007) Hybrid ANN-CBR model for disputed change orders in construction projects. Autom Constr 17(1):56–64

    Article  Google Scholar 

  10. Chen S, Billings SA, Luo W (1989) Orthogonal least squares methods and their application to non-linear system identification. Int J Control 50(5):1873–1896

    Article  Google Scholar 

  11. Chou J-S, Cheng M-Y, Wu Y-W (2013) Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models. Expert Syst Appl 40(6):2263–2274

    Article  Google Scholar 

  12. Egemen M, Mohamed AN (2007) A framework for contractors to reach strategically correct bid/no bid and mark-up size decisions. Build Environ 42(3):1373–1385

    Article  Google Scholar 

  13. Garcez ADA, Gori M, Lamb LC, Serafini L, Spranger M, Tran SN (2019) Neural-symbolic computing: an effective methodology for principled integration of machine learning and reasoning. arXiv preprint arXiv:1905.06088

  14. Gunning D (2017) Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2)

  15. Idrus A, Nuruddin MF, Rohman MA (2011) Development of project cost contingency estimation model using risk analysis and fuzzy expert system. Expert Syst Appl 38(3):1501–1508

    Article  Google Scholar 

  16. Ilkou E, Maria K (2020) Symbolic vs. sub-symbolic ai methods: friends or enemies? Eleni Ilkou, Maria Koutraki. In: Proceedings of the CIKM 2020 workshops, 19–20 October 2020, Galway, Ireland

  17. Jervis BM, Levin P (1988) Construction law, principles and practice. McGraw-Hill College, New York

    Google Scholar 

  18. Lee CK, Yiu TW, Cheung SO (2016) Selection and use of alternative dispute resolution (ADR) in construction projects—past and future research. Int J Proj Manag 34(3):494–507

    Article  Google Scholar 

  19. Mahfouz T, Kandil A (2012) Litigation outcome prediction of differing site condition disputes through machine learning models. J Comput Civ Eng 26(3):298–308

    Article  Google Scholar 

  20. Marcotte P (1990) Hastening justice—Biden committee studies task force plan to cut trial delay. Am Bar Assoc J 76(1):40

    Google Scholar 

  21. McClelland JL, Rumelhart DE, Group PR (1986) Parallel distributed processing. Explor Microstruct Cogn 2:216–271

    Google Scholar 

  22. Medsker LR (2012) Hybrid neural network and expert systems. Springer, New York

    Google Scholar 

  23. Merrill PG (2006) Construction dispute review board+ settlement panels: Save time, money,+ headaches. Contract Management Magazine, pp 38–43

  24. Mohebi M, Kakavand M (2014) Selected Arbitral Awards of Arbitration Center of Iran Chamber, Shahr Danesh Pub., Tehran

  25. Ntoutsi E, Fafalios P, Gadiraju U, Iosifidis V, Nejdl W, Vidal ME, Ruggieri S, Turini F, Papadopoulos S, Krasanakis E (2020) Bias in data-driven artificial intelligence systems—an introductory survey. Wiley Interdiscip Rev Data Min Knowl Discov 10(3):e1356

    Article  Google Scholar 

  26. Pena-Mora F, Sosa CE, McCone DS (2003) Introduction to construction dispute resolution. Prentice Hall, New Jersey

    Google Scholar 

  27. Powell MJD (1987) Radial Basis Functions for Multivariable Interpolation: A Review. In: Mason JC, Cox MG (eds) Algorithms for Approximation. Carendon Press, Oxford, pp 143–167

    Google Scholar 

  28. Powell MJD (1981) Approximation theory and methods. Cambridge University Press, Cambridge

    Google Scholar 

  29. Prentzas N, Nicolaides A, Kyriacou E, Kakas A, Pattichis C (2019) Integrating machine learning with symbolic reasoning to build an explainable AI model for stroke prediction. In: 2019 IEEE 19th international conference on bioinformatics and bioengineering (BIBE). IEEE

  30. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1988) Numerical recipes in C. Cambridge University Press, Cambridge

    Google Scholar 

  31. Pulket T, Arditi D (2009) Construction litigation prediction system using ant colony optimization. Constr Manag Econ 27(3):241–251

    Article  Google Scholar 

  32. Ren Z, Anumba C, Ugwu O (2001) Construction claims management: towards an agent-based approach. Eng Constr Archit Manag 8(3):185–197

    Google Scholar 

  33. Richter I (1983) International construction claims: avoiding and resolving disputes. McGraw-Hill, New York

    Google Scholar 

  34. Salzberg SL (1994) C4. 5: Programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993, Kluwer Academic Publishers

  35. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132

    Article  Google Scholar 

  36. Treacy TB (1995) Use of alternative dispute resolution in the construction industry. J Manag Eng 11(1):58–63

    Article  Google Scholar 

  37. Wang L-X, Mendel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans Neural Netw 3(5):807–814

    Article  Google Scholar 

  38. Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th international conference on machine learning (ICML-03)

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This research did not receive any specific grant funding agencies.

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Correspondence to Mehdi Ravanshadnia.

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Bagherian-Marandi, N., Ravanshadnia, M. & Akbarzadeh-T, MR. Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes. Artif Intell Law (2021). https://doi.org/10.1007/s10506-021-09281-9

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Keywords

  • Construction project
  • Litigation
  • Artificial intelligence
  • Judicial decisions
  • Fuzzy expert systems