Determination of the Risk Factors Impact on the Construction Projects Implementation Using Fuzzy Sets Theory

• Authors: N. Ibadov
• Languages of publication: EN

Abstracts

EN

The complexity of construction projects increases the likelihood of hazards affecting their successful implementation. There are many risk factors that lead to the failure of the project. These factors should be identified and ordered both because of their degree of importance (significance) and level (volume) of a given factors. This is very important in order to determine their effect on the construction project. Typically, the threats for the construction project include the extension of the project duration, the increase of the project cost and/or the reduce of the quality of the works. This article aims (1) to identify risk factors that may affect the construction project in Poland in the above-mentioned aspects and (2) to prioritize the significance of those risk factors. Due to the uniqueness of individual construction projects, the potential hazards and their effects are usually characterized by different levels of uncertainty. Unfortunately, it is often a non-statistical uncertainty. For this reason, the degree of significance of risk factors is described and modelled using linguistic variables and elements of the theory of fuzzy sets. This method allows to evaluate the risk of construction projects prior to their implementation.

Keywords

EN

07.05.Mh; 87.52.Px

Discipline

• 07.05.Mh: Neural networks, fuzzy logic, artificial intelligence

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2016
• Volume: 130
• Issue: 1
• Pages: 107-111

Dates

published

2016-07

Contributors

author

N. Ibadov

• Warsaw University of Technology, The Faculty of Civil Engineering, Warsaw, Poland

References

• [1] Global Construction Survey 2015, Climbing the curve, KPMG International http://www.kpmg.com/BR/PT/Estudos_Analises/artigosepublicacoes/Documents/Build-Construction/global-construction-survey-2015.pdf
• [2] D. Baloi, A.D. Price, Int. J. Project Manag. 21, 261 (2003), doi: 10.1016/S0263-7863(02)00017-0
• [3] V. Carr, J.H. Tah, Adv. Eng. Software 32, 847 (2001), doi: 10.1016/S0965-9978(01)00036-9
• [4] J. Tah, V. Carr, Construct. Managem. Econ. 18, 491 (2000), doi: 10.1080/01446190050024905
• [5] P. Zou, G. Zhang, J. Wang, Int. J. Project Manag. 25, 601 (2007), doi: 10.1016/j.ijproman.2007.03.001
• [6] L.A. Zadeh, Inform. Control 8, 338 (1965), doi: 10.1016/S0019-9958(65)90241-X
• [7] T. Bilgiç, I.B. Türkşen, in: Fundamental of Fuzzy Sets, The Handbook of Fuzzy Sets, Eds. D. Dubois, H. Prade, Vol. 7, Kluwer, Dordrecht 2000
• [8] T. Marchant, Fuzzy Sets Syst. 148, 157 (2004), doi: 10.1016/j.fss.2004.03.013
• [9] N. Ibadov, J. Kulejewski, M. Krzemiński, AIP Conf. Proc. 1558, 1298 (2013), doi: 10.1063/1.4825749

Identifiers

DOI

10.12693/APhysPolA.130.107