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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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.
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
Arditi D, Pulket T (2005) Predicting the outcome of construction litigation using boosted decision trees. J Comput Civ Eng 19(4):387–393
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
Arditi D, Tokdemir OB (1999) Comparison of case-based reasoning and artificial neural networks. J Comput Civ Eng 13(3):162–169
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
Chau K (2007) Application of a PSO-based neural network in analysis of outcomes of construction claims. Autom Constr 16(5):642–646
Cheeks JR (2003) Multistep dispute resolution in design and construction industry. J Prof Issues Eng Educ Pract 129(2):84–91
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
Chen J-H, Hsu S (2007) Hybrid ANN-CBR model for disputed change orders in construction projects. Autom Constr 17(1):56–64
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
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
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
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
Gunning D (2017) Explainable artificial intelligence (xai). Defense Advanced Research Projects Agency (DARPA), nd Web 2(2)
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
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
Jervis BM, Levin P (1988) Construction law, principles and practice. McGraw-Hill College, New York
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
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
Marcotte P (1990) Hastening justice—Biden committee studies task force plan to cut trial delay. Am Bar Assoc J 76(1):40
McClelland JL, Rumelhart DE, Group PR (1986) Parallel distributed processing. Explor Microstruct Cogn 2:216–271
Medsker LR (2012) Hybrid neural network and expert systems. Springer, New York
Merrill PG (2006) Construction dispute review board+ settlement panels: Save time, money,+ headaches. Contract Management Magazine, pp 38–43
Mohebi M, Kakavand M (2014) Selected Arbitral Awards of Arbitration Center of Iran Chamber, Shahr Danesh Pub., Tehran
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
Pena-Mora F, Sosa CE, McCone DS (2003) Introduction to construction dispute resolution. Prentice Hall, New Jersey
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
Powell MJD (1981) Approximation theory and methods. Cambridge University Press, Cambridge
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
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1988) Numerical recipes in C. Cambridge University Press, Cambridge
Pulket T, Arditi D (2009) Construction litigation prediction system using ant colony optimization. Constr Manag Econ 27(3):241–251
Ren Z, Anumba C, Ugwu O (2001) Construction claims management: towards an agent-based approach. Eng Constr Archit Manag 8(3):185–197
Richter I (1983) International construction claims: avoiding and resolving disputes. McGraw-Hill, New York
Salzberg SL (1994) C4. 5: Programs for machine learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993, Kluwer Academic Publishers
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132
Treacy TB (1995) Use of alternative dispute resolution in the construction industry. J Manag Eng 11(1):58–63
Wang L-X, Mendel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans Neural Netw 3(5):807–814
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)
This research did not receive any specific grant funding agencies.
Conflict of interest
The authors declare that there is no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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
- Construction project
- Artificial intelligence
- Judicial decisions
- Fuzzy expert systems