This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

1. Abdul-Samad, Z., & Kulandaisamy, P. P. (2022). Cost Management for Information and Communication Technology Projects. Journal of Engineering, Project, and Production Management, 12(2), 166–178. https://doi.org/10.32738/jeppm-2022-0015
2. Abu Aisheh, Y. I. (2021). Lessons Learned, Barriers, and Improvement Factors for Mega Building Construction Projects in Developing Countries: Review Study. Sustainability, 13(19), 10678. https://doi.org/10.3390/su131910678
3. Aghimien, D. O., Adegbembo, T. F., Aghimien, E. I., et al. (2018). Challenges of Sustainable Construction: A Study of Educational Buildings in Nigeria. International Journal of Built Environment and Sustainability, 5(1). https://doi.org/10.11113/ijbes.v5.n1.244
4. Ahlawat, K., Chug, A., & Singh, A. P. (2021). An Insight on the Class Imbalance Problem and Its Solutions in Big Data. In: Large-Scale Data Streaming, Processing, and Blockchain Security. IGI Global.
5. Antoniou, F., Aretoulis, G., Giannoulakis, D., et al. (2023). Cost and Material Quantities Prediction Models for the Construction of Underground Metro Stations. Buildings, 13(2), 382. https://doi.org/10.3390/buildings13020382
6. Arena, F., Collotta, M., Luca, L., et al. (2021). Predictive Maintenance in the Automotive Sector: A Literature Review. Mathematical and Computational Applications, 27(1), 2. https://doi.org/10.3390/mca27010002
7. Berry, M. J., Linoff, G.S. Data Mining Techniques: For Marketing, Sales, and Customer Support, 2nd ed. John Willey & Sons, Canada.
8. Bilal, M., Oyedele, L. O., Qadir, J., et al. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced Engineering Informatics, 30(3), 500–521. https://doi.org/10.1016/j.aei.2016.07.001
9. Borovskikh, O., Evstafieva, A., & Marfina, L. (2021). Cost management of a construction company based on functional cost analysis. E3S Web of Conferences, 274, 05003. https://doi.org/10.1051/e3sconf/202127405003
10. Boujnouni, M. E. (2022). A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine. 2022 International Conference on Intelligent Systems and Computer Vision (ISCV). https://doi.org/10.1109/iscv54655.2022.9806081
11. Chaitongrat, T. (2021). Causal relationship model of problems in public sector procurement. International Journal of GEOMATE, 20(80). https://doi.org/10.21660/2021.80.6266
12. Chamidah, N., Santoni, M. M., & Matondang, N. (2020). The effect of oversampling on the classification of hypertension with the naive bayes algorithm, decision tree, and artificial neural network. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(4), 635–641. https://doi.org/10.29207/resti.v4i4.2015
13. Chen, F., Deng, P., Wan, J., et al. (2015). Data mining for the internet of things: literature review and challenges, International Journal of Distributed Sensor Networks, 11(8), 431047. https://doi.org/10.1155/ 2015/431047
14. Chen, W. T., Merrett, H. C., Lu, S. T., et al. (2019). Analysis of Key Failure Factors in Construction Partnering—A Case Study of Taiwan. Sustainability, 11(14), 3994. https://doi.org/10.3390/su11143994
15. Dlamini, M., & Cumberlege, R. (2021). The impact of cost overruns and delays in the construction business. IOP Conference Series: Earth and Environmental Science, 654(1), 012029. https://doi.org/10.1088/1755-1315/654/1/012029
16. Fan, C., Xiao, F., Li, Z., et al. (2018). Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review. Energy and Buildings, 159, 296–308. https://doi.org/10.1016/j.enbuild.2017.11.008
17. Faten Albtoush, A. M., Doh, S. I., Abdul Rahman, A. R. B., & Albtoush, J. F. A. A. (2020). Factors effecting the cost management in construction projects. International Journal of Civil Engineering and Technology, 11(1).
18. Gündüz, M., Nielsen, Y., & Özdemir, M. (2013). Quantification of delay factors using the relative importance index method for construction projects in Turkey. Journal of management in engineering, 29(2), 133–139.
19. Gyadu-Asiedu, W., Ampadu-Asiamah, A., & Fokuo-Kusi, A. (2021). A framework for systemic sustainable construction industry development (SSCID). Discover Sustainability, 2(1). https://doi.org/10.1007/s43621-021-00033-y
20. Hansen, D. R., Mowen, M. M., & Heitger, D. L. (2021). Cost management. Cengage Learning.
21. Holm, L., & Schaufelberger, J. E. (2021). Construction cost estimating. Routledge.
22. Hoseini, E., Van Veen, P., Bosch-Rekveldt, M., & Hertogh, M. (2020). Cost performance and cost contingency during project execution: Comparing client and contractor perspectives. Journal of Management in Engineering, 36(4), 05020006.
23. Hu, Y.-X., Huai, L.-B., & Cui, R.-Y. (2019). Research on Teaching Evaluation Model Based on Weighted Naive Bayes. 2019 10th International Conference on Information Technology in Medicine and Education (ITME). https://doi.org/10.1109/itme.2019.00112
24. Hui, S.C., Jha, G. Data mining for customer service support. Information & Management, 38(1), 1–13. https://doi.org/10.1016/S0378-7206(00)00051-3
25. Katarya, R., & Srinivas, P. (2020). Predicting Heart Disease at Early Stages using Machine Learning: A Survey. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). https://doi.org/10.1109/icesc48915.2020.9155586
26. Kim, S., & Lee, H. (2022). Customer Churn Prediction in Influencer Commerce: An Application of Decision Trees. Procedia Computer Science, 199, 1332–1339. https://doi.org/10.1016/j.procs.2022.01.169
27. Kusonkhum, W., Srinavin, K., Leungbootnak, N., et al. (2022). Using a Machine Learning Approach to Predict the Thailand Underground Train’s Passenger. Journal of Advanced Transportation, 2022, 1–15. https://doi.org/10.1155/2022/8789067
28. Larson, E. W., Gray, C. F. (2013). Project management: the managerial process. McGraw Hill Professional.
29. László, K., & Ghous, H. (2020). Efficiency comparison of Python and RapidMiner. Multidiszciplináris Tudományok, 10(3), 212–220.
30. Liu, P., Qingqing, W., & Liu, W. (2021). Enterprise human resource management platform based on FPGA and data mining. Microprocessors and Microsystems, 80, 103330. https://doi.org/10.1016/j.micpro.2020.103330
31. Marzukhi, S., Awang, N., Alsagoff, S. N., et al. (2021). RapidMiner and Machine Learning Techniques for Classifying Aircraft Data. Journal of Physics: Conference Series, 1997(1), 012012. https://doi.org/10.1088/1742-6596/1997/1/012012
32. Mohd, H. N. N., & Shamsul, S. (2011). Critical success factors for software projects: A comparative study. Scientific Research and Essays, 6(10), 2174–2186. https://doi.org/10.5897/sre10.1171
33. Monteiro, F. P., Sousa, V., Meireles, I., et al. (2021). Cost Modeling from the Contractor Perspective: Application to Residential and Office Buildings. Buildings, 11(11), 529. https://doi.org/10.3390/buildings11110529
34. Mumali, F. (2022). Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review. Computers & Industrial Engineering, 165, 107964. https://doi.org/10.1016/j.cie.2022.107964
35. Pertiwi, M. W., Kusmira, M., Rezkiani, R., et al. (2022). Naïve Bayes Classification Model for the Producer Price Index Prediction. SISTEMASI, 11(1), 171. https://doi.org/10.32520/stmsi.v11i1.1669
36. Plebankiewicz, E. (2018). Model of Predicting Cost Overrun in Construction Projects. Sustainability, 10(12), 4387. https://doi.org/10.3390/su10124387
37. Raj, P. V., Teja, P. S., Siddhartha, K. S., & Rama, J. K. (2021). Housing with low-cost materials and techniques for a sustainable construction in India-A review. Materials Today: Proceedings, 43, 1850–1855.
38. Shehadeh, A., Alshboul, O., Al Mamlook, R. E., & Hamedat, O. (2021). Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction, 129, 103827.
39. Sinsom, B., S. (2019). An efficiency comparison in prediction of imbalanced data classification with data mining techniques. ai Journal of Science and Technology, 8(3), 383–393.
40. Soibelman, L., Kim, H. Data preparation process for construction knowledge generation through knowledge discovery in databases. Journal of Computing in Civil Engineering, 16(1), 39–48. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:1(39)
41. Srinavin, K., Kusonkhum, W., Chonpitakwong, B., et al. (2021). Readiness of Applying Big Data Technology for Construction Management in Thai Public Sector. Journal of Advances in Information Technology, 12(1), 1–5. https://doi.org/10.12720/jait.12.1.1-5
42. Sweis, G., Sweis, R., Abu Hammad, A., et al. (2008). Delays in construction projects: The case of Jordan. International Journal of Project Management, 26(6), 665–674. https://doi.org/10.1016/j.ijproman.2007.09.009
43. Trost SM, Oberlender GD. (2003). Predicting accuracy of early cost estimates using factor analysis and multivariate regression. Journal of Construction Engineering and Management, 129(2), 198–204.
44. Witten, I. H., Frank, E., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Morgan Kaufmann.
45. Xu, H., Chen, X., Li, P., Ding, J., & Eghan, C. (2019). A Novel RFID Data Management Model Based on Quantum Cryptography. In: Proceedings of the Third International Congress on Information and Communication Technology: ICICT 2018.
46. You, Z., & Wu, C. (2019). A framework for data-driven informatization of the construction company. Advanced Engineering Informatics, 39, 269–277. https://doi.org/10.1016/j.aei.2019.02.002
47. Yu, Z., Haghighat, F., Fung, B.C.M. (2016). Advances and challenges in building engineering and data mining applications for energy-efficient communities. Sustainable Cities and Society, 25, 33–38.
48. Zeydalinejad, N. (2022). Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: a review. Modeling Earth Systems and Environment, 8(3), 2911–2932. https://doi.org/10.1007/s40808-022-01365-y