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Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

Year 2022, Volume: 33 Issue: 5, 12577 - 12600, 01.09.2022
https://doi.org/10.18400/tekderg.930076

Abstract

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.

References

  • Alaloul, W. S., Hasaniyah, M. W., Tayeh, B. A., A Comprehensive Review of Disputes Prevention and Resolution in Construction Projects. 2nd Conference for Civil Engineering Research Networks, Bandung, Indonesia, 2019.
  • Chong, H. Y., Zin, R. M., Selection of Dispute Resolution Methods: Factor Analysis Approach. Engineering Construction and Architectural Management, 19(4), 428–443, 2012.
  • Awwad, R., Barakat, B., Menassa, C., Understanding Dispute Resolution in the Middle East Region from Perspectives of Different Stakeholders. Journal of Management in Engineering, 32(6), 2016.
  • Parikh, D., Joshi, G. J., Patel, D.A., Development of Prediction Models for Claim Cause Analyses in Highway Projects. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 11(4), 2019.
  • Ustuner, Y. A., Tas, E., An Examination of the Mediation Processes of International ADR Institutions and Evaluation of the Turkish Construction Professionals’ Perspectives on Mediation. Eurasian Journal of Social Sciences, 7(4),11–27, 2019.
  • Kisi, K. P., Lee, N., Kayastha, R., Kovel, J., Alternative Dispute Resolution Practices in International Road Construction Contracts. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12(2), 2020.
  • Lee, C. K., Yiu, T. W., Cheung, S.O., Selection and Use of Alternative Dispute Resolution (ADR) in Construction Projects - Past and Future Research. International Journal of Project Management, 34(3), 494–507, 2016.
  • Cheung, S. O., Au-Yeung, R. F., Wong, V. W. K, A CBR Based Dispute Resolution Process Selection System. International Journal of IT in Architecture Engineering and Construction, 2(2),129-145, 2004.
  • Cheung, S. O., Suen, H. C. H., A Multi-Attribute Utility Model for Dispute Resolution Strategy Selection. Construction Management and Economics, 20(7), 557–568, 2002.
  • Chou, J. S., Cheng, M. Y., Wu, Y. W., Improving Classification Accuracy of Project Dispute Resolution using Hybrid Artificial Intelligence and Support Vector Machine Models. Expert Systems with Applications, 40(6), 2263–2274, 2013.
  • İlter, D., Opinions of Legal Professionals Regarding the Selection of Appropriate Resolution Method in Construction Disputes. RICS COBRA Annual Construction Building and Real Estate Research Conference, Paris, France, 2010.
  • Siam, A., Ezzeldin, M., El-Dakhakhni, W., Machine Learning Algorithms for Structural Performance Classifications and Predictions: Application to Reinforced Masonry Shear Walls. Structures, 22, 252–265, 2019.
  • Ayhan, M., Dikmen, I., Birgonul, M. T., Predicting the Occurrence of Construction Disputes using Machine Learning Techniques. Journal of Construction Engineering and Management, 147(4), 2021.
  • Çevikbaş, M., Köksal, A., An Investigation of Litigation Process in Construction Industry in Turkey, Teknik Dergi, 29(6), 8715–8729, 2018.
  • Pulket, T., Arditi, D., Construction Litigation Prediction System using Ant Colony Optimization. Construction Management and Economics, 27(3), 241–251, 2009.
  • Harmon, K. M. J., Resolution of Construction Disputes: A Review of Current Methodologies. Leadership and Management in Engineering, 3(4), 187–201, 2003.
  • King, L. S., Kamarazaly, M. A. H., Hashim, N., Yaakob, A. M., Man, N.H., Analysis on the Issues of Construction Disputes and the Ideal Dispute Resolution Method. Malaysian Construction Research Journal, 7(2), 153–165, 2019.
  • Illankoon, I. M. C. S., Tam, W. V. Y., Le, N. K., Ranadewa, K. A. T. O., Causes of Disputes, Factors Affecting Dispute Resolution and Effective Alternative Dispute Resolution for Sri Lankan Construction Industry. International Journal of Construction Management, 1–11, 2019.
  • Sinha, A. K., Jha, K. N., Dispute Resolution and Litigation in PPP Road Projects: Evidence from Select Cases. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12(1), 2020.
  • Chou, J. S., Cheng, M. Y., Wu, Y. W., Pham, A. D., Optimizing Parameters of Support Vector Machine using Fast Messy Genetic Algorithm for Dispute Classification. Expert Systems with Applications, 41(8), 3955–3964, 2014.
  • Chen, J. H., KNN Based Knowledge-Sharing Model for Severe Change Order Disputes in Construction. Automation in Construction, 17(6), 773–779, 2008.
  • Liu, J., Li, H., Skitmore, M., Zhang, Y., Experience Mining Based on Case-Based Reasoning for Dispute Settlement of International Construction Projects. Automation in Construction, 97, 181–191, 2019.
  • Chau, K. W., Application of PSO-Based Neural Network in Analysis of Outcomes of Construction Claims. Automation in Construction, 16(5), 642–646, 2007.
  • Chen, J. H., Hsu, S. C., Hybrid ANN-CBR Model for Disputed Change Orders in Construction Projects. Automation in Construction, 17(1), 56–64, 2007.
  • Arditi, D., Oksay, F. E., Tokdemir, O. B., Predicting the Outcome of Construction Litigation using Neural Networks. Computer-Aided Civil and Infrastructure Engineering, 13(2), 75–81, 1998.
  • Arditi, D., Pulket, T., Predicting the Outcome of Construction Litigation using Boosted Decision Trees. Journal of Computing in Civil Engineering, 19(4), 387–393, 2005.
  • Arditi, D., Pulket, T., Predicting the Outcome of Construction Litigation using an Integrated Artificial Intelligence Model. Journal of Computing in Civil Engineering, 24(1), 73–80, 2010.
  • Pulket, T., Arditi, D., Universal Prediction Model for Construction Litigation. Journal of Computing in Civil Engineering, 23(3), 178–187, 2009.
  • Mahfouz, T., Kandil, A., Davlyatov, S., Identification of Latent Legal Knowledge in Differing Site Condition (DSC) Litigations. Automation in Construction, 94, 104–111, 2018.
  • Chaphalkar, N. B., Iyer, K. C., Patil, S. K., Prediction of Outcome of Construction Dispute Claims using Multilayer Perceptron Neural Network Model. International Journal of Project Management, 33(8), 1827–1835, 2015.
  • Chou, J. S., Comparison of Multilabel Classification Models to Forecast Project Dispute Resolutions. Expert Systems with Applications, 39(11), 10202–10211, 2012.
  • Chou, J. S., Hsu, S. C., Lin, C. W., Chang, Y. C., Classifying Influential Information to Discover Rule Sets for Project Disputes and Possible Resolutions. International Journal of Project Management, 34(8), 1706–1716, 2016.
  • Ayhan, M., Development of Dispute Prediction and Resolution Method Selection Models for Construction Disputes. Ph.D. Thesis, Middle East Technical University, Ankara, 2019.
  • Weisburd, D., Britt, C., Statistics in Criminal Justice, 3rd ed, Boston. Springer, 2007.
  • Arasu, B. S., Seelan, B. J. B., Thamaraiselvan, N., A Machine Learning-Based Approach to Enhancing Social Media Marketing. Computers & Electrical Engineering, 86, 2020.
  • Witten, H. W., Frank, E., Hall, M. A., Pal, C. J., Data Mining: Practical Machine Learning Tools and Techniques, 4th ed, Burlington. Morgan Kaufmann, 2016.
  • Hssina, B., Merbouha, A., Ezzikouri, H., Erritali, M., A Comparative Study of Decision Tree ID3 and C4.5. International Journal of Advanced Computer Sciences and Applications, 4(2), 13–19, 2014.
  • Febriantono, M. A., Pramono, S. H., Rahmadwati, R., Naghdy, G.), Classification of Multiclass Imbalanced Data using Cost-Sensitive Decision Tree C5.0. IAES International Journal of Artificial Intelligence, 9(1), 65–72, 2020.
  • Cortes, C., Vapnik, V., Support-vector networks. Machine Learning, 20(3), 273–297, 1995.
  • Alpaydin, E., Introduction to Machine Learning, 2nd ed, Cambridge. MIT Press, 2010.
  • Hsu, C. W., Lin, C. J., A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks, 13(2), 415–425, 2002.
  • Dietterich, T. G., Bakiri, G., Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research, 2, 263–286, 1994.
  • McHugh, M. L., The Chi-Square Test of Independence. Biochemia Medica, 23(2), 143–149, 2013.
  • Akoglu, H., User’s Guide to Correlation Coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93, 2018.
  • Pollock III, P.H., An SPSS Companion to Political Analysis, 4th ed, Washington, DC. CQ Press, 2011.
  • Vanwinckelen, G., Blockeel, H., On Estimating Model Accuracy with Repeated Cross-Validation. 21st Belgian-Dutch Conference on Machine Learning, Ghent, Belgium, 2012.
  • Lingard, H., Brown, K., Bradley, L., Bailey, C., Townsend, K., Improving Employees’ Work-Life Balance in the Construction Industry: Project Alliance Case Study. Journal of Construction Engineering and Management, 133(10), 807–815, 2007.
  • İlter, O., Çelik, T., Investigation of Organizational and Regional Perceptions on the Changes in Construction Projects. Teknik Dergi, 32(6), 2021.
  • Yu, W. D., Hybrid Soft Computing Approach for Mining of Complex Construction Databases. Journal of Computing in Civil Engineering, 21(5), 343–352, 2007.

Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions

Year 2022, Volume: 33 Issue: 5, 12577 - 12600, 01.09.2022
https://doi.org/10.18400/tekderg.930076

Abstract

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.

References

  • Alaloul, W. S., Hasaniyah, M. W., Tayeh, B. A., A Comprehensive Review of Disputes Prevention and Resolution in Construction Projects. 2nd Conference for Civil Engineering Research Networks, Bandung, Indonesia, 2019.
  • Chong, H. Y., Zin, R. M., Selection of Dispute Resolution Methods: Factor Analysis Approach. Engineering Construction and Architectural Management, 19(4), 428–443, 2012.
  • Awwad, R., Barakat, B., Menassa, C., Understanding Dispute Resolution in the Middle East Region from Perspectives of Different Stakeholders. Journal of Management in Engineering, 32(6), 2016.
  • Parikh, D., Joshi, G. J., Patel, D.A., Development of Prediction Models for Claim Cause Analyses in Highway Projects. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 11(4), 2019.
  • Ustuner, Y. A., Tas, E., An Examination of the Mediation Processes of International ADR Institutions and Evaluation of the Turkish Construction Professionals’ Perspectives on Mediation. Eurasian Journal of Social Sciences, 7(4),11–27, 2019.
  • Kisi, K. P., Lee, N., Kayastha, R., Kovel, J., Alternative Dispute Resolution Practices in International Road Construction Contracts. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12(2), 2020.
  • Lee, C. K., Yiu, T. W., Cheung, S.O., Selection and Use of Alternative Dispute Resolution (ADR) in Construction Projects - Past and Future Research. International Journal of Project Management, 34(3), 494–507, 2016.
  • Cheung, S. O., Au-Yeung, R. F., Wong, V. W. K, A CBR Based Dispute Resolution Process Selection System. International Journal of IT in Architecture Engineering and Construction, 2(2),129-145, 2004.
  • Cheung, S. O., Suen, H. C. H., A Multi-Attribute Utility Model for Dispute Resolution Strategy Selection. Construction Management and Economics, 20(7), 557–568, 2002.
  • Chou, J. S., Cheng, M. Y., Wu, Y. W., Improving Classification Accuracy of Project Dispute Resolution using Hybrid Artificial Intelligence and Support Vector Machine Models. Expert Systems with Applications, 40(6), 2263–2274, 2013.
  • İlter, D., Opinions of Legal Professionals Regarding the Selection of Appropriate Resolution Method in Construction Disputes. RICS COBRA Annual Construction Building and Real Estate Research Conference, Paris, France, 2010.
  • Siam, A., Ezzeldin, M., El-Dakhakhni, W., Machine Learning Algorithms for Structural Performance Classifications and Predictions: Application to Reinforced Masonry Shear Walls. Structures, 22, 252–265, 2019.
  • Ayhan, M., Dikmen, I., Birgonul, M. T., Predicting the Occurrence of Construction Disputes using Machine Learning Techniques. Journal of Construction Engineering and Management, 147(4), 2021.
  • Çevikbaş, M., Köksal, A., An Investigation of Litigation Process in Construction Industry in Turkey, Teknik Dergi, 29(6), 8715–8729, 2018.
  • Pulket, T., Arditi, D., Construction Litigation Prediction System using Ant Colony Optimization. Construction Management and Economics, 27(3), 241–251, 2009.
  • Harmon, K. M. J., Resolution of Construction Disputes: A Review of Current Methodologies. Leadership and Management in Engineering, 3(4), 187–201, 2003.
  • King, L. S., Kamarazaly, M. A. H., Hashim, N., Yaakob, A. M., Man, N.H., Analysis on the Issues of Construction Disputes and the Ideal Dispute Resolution Method. Malaysian Construction Research Journal, 7(2), 153–165, 2019.
  • Illankoon, I. M. C. S., Tam, W. V. Y., Le, N. K., Ranadewa, K. A. T. O., Causes of Disputes, Factors Affecting Dispute Resolution and Effective Alternative Dispute Resolution for Sri Lankan Construction Industry. International Journal of Construction Management, 1–11, 2019.
  • Sinha, A. K., Jha, K. N., Dispute Resolution and Litigation in PPP Road Projects: Evidence from Select Cases. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12(1), 2020.
  • Chou, J. S., Cheng, M. Y., Wu, Y. W., Pham, A. D., Optimizing Parameters of Support Vector Machine using Fast Messy Genetic Algorithm for Dispute Classification. Expert Systems with Applications, 41(8), 3955–3964, 2014.
  • Chen, J. H., KNN Based Knowledge-Sharing Model for Severe Change Order Disputes in Construction. Automation in Construction, 17(6), 773–779, 2008.
  • Liu, J., Li, H., Skitmore, M., Zhang, Y., Experience Mining Based on Case-Based Reasoning for Dispute Settlement of International Construction Projects. Automation in Construction, 97, 181–191, 2019.
  • Chau, K. W., Application of PSO-Based Neural Network in Analysis of Outcomes of Construction Claims. Automation in Construction, 16(5), 642–646, 2007.
  • Chen, J. H., Hsu, S. C., Hybrid ANN-CBR Model for Disputed Change Orders in Construction Projects. Automation in Construction, 17(1), 56–64, 2007.
  • Arditi, D., Oksay, F. E., Tokdemir, O. B., Predicting the Outcome of Construction Litigation using Neural Networks. Computer-Aided Civil and Infrastructure Engineering, 13(2), 75–81, 1998.
  • Arditi, D., Pulket, T., Predicting the Outcome of Construction Litigation using Boosted Decision Trees. Journal of Computing in Civil Engineering, 19(4), 387–393, 2005.
  • Arditi, D., Pulket, T., Predicting the Outcome of Construction Litigation using an Integrated Artificial Intelligence Model. Journal of Computing in Civil Engineering, 24(1), 73–80, 2010.
  • Pulket, T., Arditi, D., Universal Prediction Model for Construction Litigation. Journal of Computing in Civil Engineering, 23(3), 178–187, 2009.
  • Mahfouz, T., Kandil, A., Davlyatov, S., Identification of Latent Legal Knowledge in Differing Site Condition (DSC) Litigations. Automation in Construction, 94, 104–111, 2018.
  • Chaphalkar, N. B., Iyer, K. C., Patil, S. K., Prediction of Outcome of Construction Dispute Claims using Multilayer Perceptron Neural Network Model. International Journal of Project Management, 33(8), 1827–1835, 2015.
  • Chou, J. S., Comparison of Multilabel Classification Models to Forecast Project Dispute Resolutions. Expert Systems with Applications, 39(11), 10202–10211, 2012.
  • Chou, J. S., Hsu, S. C., Lin, C. W., Chang, Y. C., Classifying Influential Information to Discover Rule Sets for Project Disputes and Possible Resolutions. International Journal of Project Management, 34(8), 1706–1716, 2016.
  • Ayhan, M., Development of Dispute Prediction and Resolution Method Selection Models for Construction Disputes. Ph.D. Thesis, Middle East Technical University, Ankara, 2019.
  • Weisburd, D., Britt, C., Statistics in Criminal Justice, 3rd ed, Boston. Springer, 2007.
  • Arasu, B. S., Seelan, B. J. B., Thamaraiselvan, N., A Machine Learning-Based Approach to Enhancing Social Media Marketing. Computers & Electrical Engineering, 86, 2020.
  • Witten, H. W., Frank, E., Hall, M. A., Pal, C. J., Data Mining: Practical Machine Learning Tools and Techniques, 4th ed, Burlington. Morgan Kaufmann, 2016.
  • Hssina, B., Merbouha, A., Ezzikouri, H., Erritali, M., A Comparative Study of Decision Tree ID3 and C4.5. International Journal of Advanced Computer Sciences and Applications, 4(2), 13–19, 2014.
  • Febriantono, M. A., Pramono, S. H., Rahmadwati, R., Naghdy, G.), Classification of Multiclass Imbalanced Data using Cost-Sensitive Decision Tree C5.0. IAES International Journal of Artificial Intelligence, 9(1), 65–72, 2020.
  • Cortes, C., Vapnik, V., Support-vector networks. Machine Learning, 20(3), 273–297, 1995.
  • Alpaydin, E., Introduction to Machine Learning, 2nd ed, Cambridge. MIT Press, 2010.
  • Hsu, C. W., Lin, C. J., A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions on Neural Networks, 13(2), 415–425, 2002.
  • Dietterich, T. G., Bakiri, G., Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research, 2, 263–286, 1994.
  • McHugh, M. L., The Chi-Square Test of Independence. Biochemia Medica, 23(2), 143–149, 2013.
  • Akoglu, H., User’s Guide to Correlation Coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93, 2018.
  • Pollock III, P.H., An SPSS Companion to Political Analysis, 4th ed, Washington, DC. CQ Press, 2011.
  • Vanwinckelen, G., Blockeel, H., On Estimating Model Accuracy with Repeated Cross-Validation. 21st Belgian-Dutch Conference on Machine Learning, Ghent, Belgium, 2012.
  • Lingard, H., Brown, K., Bradley, L., Bailey, C., Townsend, K., Improving Employees’ Work-Life Balance in the Construction Industry: Project Alliance Case Study. Journal of Construction Engineering and Management, 133(10), 807–815, 2007.
  • İlter, O., Çelik, T., Investigation of Organizational and Regional Perceptions on the Changes in Construction Projects. Teknik Dergi, 32(6), 2021.
  • Yu, W. D., Hybrid Soft Computing Approach for Mining of Complex Construction Databases. Journal of Computing in Civil Engineering, 21(5), 343–352, 2007.
There are 49 citations in total.

Details

Primary Language English
Subjects Civil Engineering
Journal Section Articles
Authors

Murat Ayhan 0000-0002-2011-4190

İrem Toker 0000-0002-6988-7557

Talat Birgönül 0000-0002-1638-2926

Publication Date September 1, 2022
Submission Date April 29, 2021
Published in Issue Year 2022 Volume: 33 Issue: 5

Cite

APA Ayhan, M., Toker, İ., & Birgönül, T. (2022). Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions. Teknik Dergi, 33(5), 12577-12600. https://doi.org/10.18400/tekderg.930076
AMA Ayhan M, Toker İ, Birgönül T. Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions. Teknik Dergi. September 2022;33(5):12577-12600. doi:10.18400/tekderg.930076
Chicago Ayhan, Murat, İrem Toker, and Talat Birgönül. “Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions”. Teknik Dergi 33, no. 5 (September 2022): 12577-600. https://doi.org/10.18400/tekderg.930076.
EndNote Ayhan M, Toker İ, Birgönül T (September 1, 2022) Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions. Teknik Dergi 33 5 12577–12600.
IEEE M. Ayhan, İ. Toker, and T. Birgönül, “Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions”, Teknik Dergi, vol. 33, no. 5, pp. 12577–12600, 2022, doi: 10.18400/tekderg.930076.
ISNAD Ayhan, Murat et al. “Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions”. Teknik Dergi 33/5 (September 2022), 12577-12600. https://doi.org/10.18400/tekderg.930076.
JAMA Ayhan M, Toker İ, Birgönül T. Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions. Teknik Dergi. 2022;33:12577–12600.
MLA Ayhan, Murat et al. “Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions”. Teknik Dergi, vol. 33, no. 5, 2022, pp. 12577-00, doi:10.18400/tekderg.930076.
Vancouver Ayhan M, Toker İ, Birgönül T. Comparing Performances of Machine Learning Techniques to Forecast Dispute Resolutions. Teknik Dergi. 2022;33(5):12577-600.