Prediction of the Delay in the Portfolio Construction Using Naïve Bayesian Classification Algorithms
References
- [1] FRIMPONG, Y. - OLUWOYEB, J. - CRAWFORD, L.: Causes of delay and cost overruns in construction of groundwater projects in a developing country. International Journal of Project Management, Vol. 21, Iss. 5, 2003, pp. 321–326.10.1016/S0263-7863(02)00055-8
- [2] BILAL, M. - OYEDELE, L. - QADIR, J. - MUNIR, K. - AJAYI, S. - AKINADE, O. - OWOLABI, H. -ALAKA, H. - PASHA, M.: Big Data in the construction industry: A review of present status, opportunities, and future trend. Advanced Engineering Informatic, Vol. 30, Iss. 3, 2016, pp. 500–521.10.1016/j.aei.2016.07.001
- [3] CHAN, D. - KUMARASWAMY, M.: Compressing construction durations: lessons learned from Hong Kong building projects. International Journal of Project Management, Vol. 20, Iss.1, 2002, pp. 22–35.10.1016/S0263-7863(00)00032-6
- [4] ELAZOUNI, A. M.: Classifying construction contractors using unsupervised-learning neural networks. Journal of Construction Engineering and Management, Vol. 132, Iss.12, 2006, pp. 1242–1253.10.1061/(ASCE)0733-9364(2006)132:12(1242)
- [5] CHAO, L. C. - CHIEN, C. F.: Estimating project S-curves using polynomial function and neural networks. Journal of Construction Engineering and Management, Vol.135, Iss.3, 2009, pp. 169–177.10.1061/(ASCE)0733-9364(2009)135:3(169)
- [6] HERAVI, G. - ESLAMDOOST, E.: Applying artificial neural networks for measuring and predicting construction-labor productivity. Journal of Construction Engineering and Management, Vol. 141, Iss. 10, 2015, 04015032.10.1061/(ASCE)CO.1943-7862.0001006
- [7] DESAI, V. S. - JOSHI, S.: Application of decision tree technique to analyze construction project data. International Conference on Information Systems, Technology and Management ICISTM 2010: Information Systems, Technology and Management, pp. 304–313.10.1007/978-3-642-12035-0_30
- [8] CHI, S. - SUK, S. - KANG, Y. - MULVA, S.: Development of a data mining-based analysis framework for multi-attribute construction project information. Advanced Engineering Informatics, Vol. 26, Iss. 3, 2012, pp. 574–581.10.1016/j.aei.2012.03.005
- [9] CHEUNG, S. O. - YIU, T. W. - CHAN, H. W.: Exploring the potential for predicting project dispute resolution satisfaction using logistic regression. Journal of Construction Engineering and Management, Vol. 136, Iss. 5, 2010, pp. 508–517.10.1061/(ASCE)CO.1943-7862.0000157
- [10] ALZAHRANI, J. I. - EMSLEY, M. W.: The impact of contractors’ attributes on construction project success: A post construction evaluation. International Journal of Project Management, Vol. 31, Iss. 2, 2013, pp. 313–322.10.1016/j.ijproman.2012.06.006
- [11] JIANG, X. - MAHADEVAN, S.: Bayesian probabilistic inference for nonparametric damage detection of structures. Journal of Engineering Mechanics, Vol. 134, Iss. 10, 2008, pp. 820–831.10.1061/(ASCE)0733-9399(2008)134:10(820)
- [12] GONG, J. - CALDAS, C. H. - GORDON, C.: Learning and classifying actions of construction workers and equipment using Bag-of-VideoFeature-Words and Bayesian network models. Advanced Engineering Informatics, Vol. 25, Iss. 4, 2011, pp. 771–782.10.1016/j.aei.2011.06.002
- [13] CHENG, M. Y. - WU, Y. W.: Evolutionary support vector machine inference system for construction management. Automation in Construction, Vol. 18, Iss. 5, 2009, pp. 597–604.10.1016/j.autcon.2008.12.002
- [14] LAM, K. C. - LAM, M. C. K. - WANG, D.: Efficacy of using support vector machine in a contractor prequalification decision model. Journal of Computing in Civil Engineering, Vol. 24, Iss. 3, 2010, pp. 273–280.10.1061/(ASCE)CP.1943-5487.0000030
- [15] GONDIA, A. - SIAM, A. - EL-DAKHAKHNI, W. - NASSAR, A. H.: Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of Construction Engineering and Management, Vol. 146, Iss. 1, 2019, 04019085.10.1061/(ASCE)CO.1943-7862.0001736
- [16] Mc CALLUM, A.: Graphical Models, Lecture2: Bayesian Network Representation. Retrieved 22 October 2019.
- [17] GHAZVINI, K. – ZANDIEH, M. – VAFAMEHR, M.: Exploring KPIs Utilization Effects on Decision Making for the Architectural Design Process in Industrial Buildings. Civil and Environmental Engineering, Vol. 16, Iss. 1, 2020, pp. 198–209.10.2478/cee-2020-0019
- [18] ŠESTAKOVÁ, J. – IŽVOLT, L. – MEČÁR, M.: Degradation - Prediction Models of the Railway Track Quality. Civil and Environmental Engineering, Vol. 15, Iss. 2, 2019, pp. 115–124.10.2478/cee-2019-0015
- [19] NAJI, H. I. - AL-ZUBAIDI, E. A. - ALI, R. H.: Risk Response Failure in Construction Projects, 2018.
- [20] YASEEN, Z. M., et al.: Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability, 2020, 12.4: 1514.10.3390/su12041514
Language: English
Page range: 673 - 680
Published on: Dec 9, 2021
Published by: University of Žilina
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year
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© 2021 Kadhim Raheim Erzaij, Abbas M. Burhan, Wadhah Amer Hatem, Rouwaida Hussein Ali, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.