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Modelling of time, cost and risk of construction with using fuzzy logic

Abstract

The paper presents an overview of the literature from recent years devoted to planning the time, costs and risk of a construction investment using fuzzy logic. It also presents three own original models concerning the issue. The first model is used to build a fuzzy construction schedule taking into account fuzzy norms and the number of workers. The costing model uses fuzzy inference from CBR cases. The aim was to increase the accuracy and correctness of the cost calculation performed for the investor in the construction and investment process with a certain degree of vagueness of the available information about materials. In the last of the presented models, fuzzy sets were used to assess the effects of technological and construction (implementation) risk factors. The presented examples prove the usefulness of fuzzy logic in solving problems in construction, where we have incomplete and imprecise information.

Keyword : cost, time, risk, construction, fuzzy set, case based reasoning

How to Cite
Plebankiewicz, E., Zima, K., & Wieczorek, D. (2021). Modelling of time, cost and risk of construction with using fuzzy logic. Journal of Civil Engineering and Management, 27(6), 412-426. https://doi.org/10.3846/jcem.2021.15255
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Jul 15, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abdelgawad, M., & Fayek, A. R. (2011). Comprehensive hybrid framework for risk analysis in the construction industry using combined failure mode and effect analysis, fault trees, event trees, and fuzzy logic. Journal of Construction Engineering and Management, 138(5), 642–651. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000471

Amadi, A. I., & Higham, A. (2017). Latent geotechnical pathogens inducing cost overruns in highway projects. Journal of Financial Management of Property and Construction, 22(3), 269–285. https://doi.org/10.1108/JFMPC-03-2017-0008

Antucheviciene, J., Kala, Z., Marzouk, M., & Vaidogas, E. R. (2015). Solving civil engineering problems by means of fuzzy and stochastic MCDM methods: Current state and future research. Mathematical Problems in Engineering, Article ID 362579. https://doi.org/10.1155/2015/362579

Bhaskar, T., Manabendra, N. P., & Asim, K. P. (2011). A heuristic method for RCPSP with fuzzy activity times. European Journal of Operational Research, 208, 57–66. https://doi.org/10.1016/j.ejor.2010.07.021

Castro-Lacouture, D., Süer, G. A., Gonzalez-Joaqui, J., & Yates, J. K. (2009). Construction project scheduling with time, cost, and material restrictions using fuzzy mathematical models and critical path method. Journal of Construction Engineering and Management, 135(10), 1096–1104. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:10(1096)

Chanas, S., & Zielinski, P. (2001). Critical path analysis in the network with fuzzy activity times. Fuzzy Sets & Systems, 122, 195–204. https://doi.org/10.1016/S0165-0114(00)00076-2

Chen, S., & Hsueh, Y. (2008). A simple approach to fuzzy critical path analysis in project networks. Applied Mathematical Modelling, 32, 1289–1297. https://doi.org/10.1016/j.apm.2007.04.009

Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2010). Conceptual cost estimates using evolutionary fuzzy hybrid neural network. Expert Systems with Applications, 37, 4224–4231. https://doi.org/10.1016/j.eswa.2009.11.080

Dikmen, I., Birgonul, M. T., & Han, S. (2007). Using fuzzy risk assessment to rate cost overrun risk in international construction projects. International Journal Project Management, 25, 494–505. https://doi.org/10.1016/j.ijproman.2006.12.002

Ebrahimnejad, S., Mousavi, S., Tavakkoli-Moghaddam, M. R., & Heydar, M. (2014). Risk ranking in mega projects by fuzzy compromise approach: A comparative analysis. Journal of Intelligent and Fuzzy Systems, 26(2), 949–959. https://doi.org/10.3233/IFS-130785

El-Maaty, A. E. A., El-Kholy, A. M., & Akal, A. Y. (2017). Modeling schedule overrun and cost escalation percentages of highway projects using fuzzy approach. Engineering, Construction and Architectural Management, 24(5), 809–827. https://doi.org/10.1108/ECAM-03-2016-0084

Elizabeth, S., & Sujatha, L. (2013). Fuzzy critical path problem for project network. International Journal of Pure and Applied Mathematics, 85, 223–240. https://doi.org/10.12732/ijpam.v85i2.4

Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2018). A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Archives of Civil and Mechanical Engineering, 18(1), 32–49. https://doi.org/10.1016/j.acme.2017.04.011

Han, T., Chung, C., & Liang, G. (2006). Application of fuzzy critical path method to airports cargo ground operation systems. Journal of Marine Science and Technology, 14, 139–146.

Hashemi, S. S., Razavi, H., Hajiagha, S. H. R., Zavadskas, E. K., & Mahdiraji, H. A. (2016). Multicriteria group decision making with ELECTRE III method based on interval-valued intuitionistic fuzzy information. Applied Mathematical Modelling, 40(2), 1554–1564. https://doi.org/10.1016/j.apm.2015.08.011

Ibadov, N. (2019). Construction project planning under fuzzy time constraint. International Journal of Environmental Science and Technology, 16, 4999–5006. https://doi.org/10.1007/s13762-018-1695-x

Islam, M. S., Nepal, M., Skitmore, M., & Attarzadeh, M. (2017). Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects. Advanced Engineering Informatics, 33, 112–131. https://doi.org/10.1016/j.aei.2017.06.001

Islam, M. S., Nepal, M., & Skitmore, M. (2019). Modified fuzzy group decision-making approach to cost overrun risk assessment of power plant projects. Journal of Construction Engineering and Management, 145(2), 04018126. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001593

Khalilzadeh, M., Shakeri, H., Gholami, H., & Amini, L. (2017). A heuristic algorithm for project scheduling with fuzzy parameters. Procedia Computer Science, 121, 63–71. https://doi.org/10.1016/j.procs.2017.11.010

Kim, S. Y., Pham, H., & Luu, T.V. (2018). Construction cost overruns in transmission grid projects. International Journal of Engineering Research and Technology, 11(12), 1923–1948.

Knight, K., & Fayek, A. R. (2002). Use of fuzzy logic for predicting design cost overruns on building projects. Journal of Construction Engineering and Management, 128(6), 503–512. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:6(503)

Kumar, A., & Kaur, P. (2010). A new method for fuzzy critical path analysis in project networks with a new presentation of triangular fuzzy numbers. Applications and Applied Mathematics, 5, 1442–1466.

Latief, Y., Wibowo, A. & Isvara, W. (2013). Preliminary cost estimation using regression analysis incorporated with adaptive neuro fuzzy inference system. International Journal of Technology, 1, 63–72.

Leśniak, A., & Zima, K. (2018). Cost calculation of construction projects including sustainability factors using the Case Based Reasoning (CBR) method. Sustainability, 10, 1608. https://doi.org/10.3390/su10051608

Meharie, M. G., Abiero, G. Z. C., Mutuku, R. N. N., & Mengesha, W. J. (2019). An effective approach to input variable selection for preliminary cost estimation of construction projects. Advances in Civil Engineering, Article ID 4092549. https://doi.org/10.1155/2019/4092549

Morovatdar, R., Aghaie, A., Roghanian, A., & AslHaddad, A. (2013). An algorithm to obtain possibly critical paths in imprecise project networks. Iranian Journal of Operations Research, 4, 39–54.

Pawan, P., & Lorterapong, P. (2016). A fuzzy-based integrated framework for assessing time contingency in construction projects. Journal of Construction Engineering and Management, 142(3), 04015083. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001073

Phama, H., Luub, T. V., Kimc, S. Y. & Viend, D. T. (2020). Assessing the impact of cost overrun causes in transmission lines construction projects. KSCE Journal of Civil Engineering, 24(4), 1029–1036. https://doi.org/10.1007/s12205-020-1391-5

Plebankiewicz, E. (2018). Model of predicting cost overrun in construction projects. Sustainability, 10(12), 4387. https://doi.org/10.3390/su10124387

Plebankiewicz, E., & Karcińska, P. (2016). Creating a construction schedule specifying fuzzy norms and the number of workers. Archives of Civil Engineering, 62(3), 149–166. https://doi.org/10.1515/ace-2015-0089

Plebankiewicz, E. & Wieczorek, D. (2016). Rozmyta ocena ryzyka w cyklu życia obiektów budowlanych. Materiały Budowlane, 6, 59–61 (in Polish). https://doi.org/10.15199/33.2016.06.25

Plebankiewicz, E., & Wieczorek, D. (2018). Multidimensional sensitivity study of the fuzzy risk assessment module in the life cycle of building objects. Open Engineering, 8(1), 490–499. https://doi.org/10.1515/eng-2018-0059

Plebankiewicz, E. & Wieczorek, D. (2020). Adaptation of a cost overrun risk prediction model to the type of construction facility. Symmetry, 12, 1739. https://doi.org/10.3390/sym12101739

Plebankiewicz, E., Zima, K. & Wieczorek, D. (2015). Identification of risk factors in the life cycle cost of a building. In D. Skorupka (Ed.), Scientific problems in project management (pp. 153–165). Wydawnictwo Wyższej Szkoły Oficerskiej Wojsk Lądowych imienia generała Tadeusza Kościuszki.

Plebankiewicz, E., Zima, K., & Wieczorek. D. (2019). Original model for estimating the whole life costs of buildings and its verification. Archives of Civil Engineering, 65(2), 163–179. https://doi.org/10.2478/ace-2019-0026

Plebankiewicz, E., Meszek, W., Zima, K., & Wieczorek, D. (2020). Probabilistic and fuzzy approaches for estimating the life cycle costs of buildings under conditions of exposure to risk. Sustainability, 12(1), 226. https://doi.org/10.3390/su12010226

Princy, S., & Dhenakaran, S. (2016). Comparison of triangular and trapezoidal fuzzy membership function. Journal of Computer Science and Engineering, 2(8), 46–51.

Ramli, N., & Mohamad, D. (2010). Fuzzy Jaccard with degree of optimism ranking index based on function principle approach. Majlesi Journal of Electrical Engineering, 4(4), 9–15.

Salah, A., & Moselhi, O. (2015). Contingency modelling for construction projects using fuzzy-set theory. Engineering, Construction and Architectural Management, 22(2), 214–241. https://doi.org/10.1108/ECAM-03-2014-0039

San Cristobal, J. (2013). Critical path definition using multi criteria decision making: the PROMETHEE method. Journal of Management in Engineering, 29(2), 158–163. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000135

Seker, S., & Zavadskas, E. K. (2017). Application of fuzzy DEMATEL method for analyzing occupational risks on construction sites. Sustainability, 9(11), 2083. https://doi.org/10.3390/su9112083

Sekocenbud. (2020a). Biuletyn cen regionalnych (BCR) [Regional prices bulletin, quarters I–III] (in Polish).

Sekocenbud. (2020b). Zagregowane wskaźniki waloryzacyjnoprognostyczne I–III kw. [Aggregate indexing and forecasting indicators, quarters I–III] (in Polish).

Shaheen, A. A., Fayek, A. R., & AbouRizk, S. M. (2007). Fuzzy numbers in cost range estimating. Journal of Construction Engineering and Management, 133(4), 325–334. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:4(325)

Shakeela, S., & Gansean, K. (2011). A simple approach to fuzzy critical path analysis in project networks. International Journal of Scientific and Engineering Research, 2(12), 1–11.

Sharma, S., & Goyal, P. K. (2019). Fuzzy assessment of the risk factors causing cost overrun in construction industry. Evolutionary Intelligence. https://doi.org/10.1007/s12065-019-00214-9

Soltani, A, & Haji, R. (2007). A project scheduling method based on fuzzy theory. Journal of Industrial and Systems Engineering, 1, 70–80.

Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105–116. https://doi.org/10.1016/j.asoc.2014.01.003

Wieczorek, D. (2018). Fuzzy risk assessment in the life cycle of building object – selection of the right defuzzification method. In AIP Conference Proceedings. AIP Publishing. https://doi.org/10.1063/1.5043866

Wang, R. C., & Liang, T. F. (2004). Project management decisions with multiple fuzzy goals. Construction Management and Economics, 22(10), 1047–1056. https://doi.org/10.1080/0144619042000241453

Yu, W.-D., & Skibniewski, M. J. (2010). Integrating neurofuzzy system with conceptual cost estimation to discover cost related knowledge from residential construction projects. Journal of Computing in Civil Engineering, 24(1), 35–44. https://doi.org/10.1061/(ASCE)0887-3801(2010)24:1(35)

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338– 353. https://doi.org/10.1016/S0019-9958(65)90241-X

Zavadskas, E. K., Antucheviciene, J., Turskis, Z., & Adeli, H. (2016). Hybrid multiple-criteria decision-making methods: A review of applications in engineering. Scientia Iranica, 23, 1–20. https://doi.org/10.24200/sci.2016.2093

Zavadskas, E. K., Antucheviciene, J., Vilutiene, T., & Adeli, H. (2018). Sustainable decision-making in civil engineering, construction and building technology. Sustainability, 10(1), 14. https://doi.org/10.3390/su10010014

Zhang, N., & Wei, G. (2013). Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 37(7), 4938–4947. https://doi.org/10.1016/j.apm.2012.10.002

Zheng, D. X. M., & Ng. T. (2005). Stochastic time-cost optimization model incorporating fuzzy sets theory and non-replacement. Journal of Construction Engineering and Management, 131(2), 176–186. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:2(176)

Zima, K. (2015). The use of fuzzy case-based reasoning in estimating costs in the early phase of the construction project. In AIP Conference Proceedings. https://doi.org/10.1063/1.4912842

Zolfaghari, S., & Mousavi, S. M. (2018). Construction-project risk assessment by a new decision model based on De-Novo multi-approaches analysis and hesitant fuzzy sets under uncertainty. Journal of Intelligent & Fuzzy Systems, 35, 639–649. https://doi.org/10.3233/JIFS-162013