Share:


A model for evaluating the risk effects on construction project activities

    Usama Hamed Issa Affiliation
    ; Salah Attia Mosaad Affiliation
    ; Mohamed Salah Hassan Affiliation

Abstract

Cost overruns and time delays are considered to be very important challenges for the majority of construction projects. These challenges are typically attributed to their associated risks. Due to the risky and uncertain nature of construction projects, an increasing amount of attention is given to estimating and overcoming cost overruns and time delays. New techniques are being developed to help project managers to contractually complete projects within cost and time constraints. The objective of this study was to develop a new qualitative and quantitative risk analysis model that can be employed for construction projects. The proposed model, which is based on a fuzzy logic tool, consists of two modules for assessing risk factors that affect the main construction activities and computing the expected cost overruns and time delays that are associated with these risks. Using numerous logical rules, the model applies the probability of occurrences and impacts of the risks on the cost and time of the main activities. The Spearman and Kendall correlation coefficient tests are applied to verify and select a suitable membership function. Using four proposed membership functions, the results of these tests confirmed that the triangle membership function is suitable for the model. The model is verified by application to HVAC system activities in two actual construction projects, which serve as case studies. Two different methods are proposed and applied to quantify the cost overruns and time delays. The first method is based on determining the cost overruns and time delay values for each activity according to their weight in the system. Triple premise rules are proposed and applied in the second method, which is established to relate all activities. The results from the second method are more accurate compared with the first method based on actual data from the case study projects. In addition, the results demonstrated that the proposed model can be used to quantify the expected cost overrun and time delays in construction project activities and can be generalized and implemented in different construction activities.

Keyword : construction project activities, evaluating risks, cost overruns, time delays, risk analysis

How to Cite
Issa, U. H., Mosaad, S. A., & Hassan, M. S. (2019). A model for evaluating the risk effects on construction project activities. Journal of Civil Engineering and Management, 25(7), 687-699. https://doi.org/10.3846/jcem.2019.10531
Published in Issue
Jul 15, 2019
Abstract Views
1821
PDF Downloads
1216
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abd El Khalek, H., Aziz, R. F., & Kamel, H. (2017). International construction projects’ risk cost estimation: Fuzzy logic and AHP in application (Real case studies). Journal of Buildings and Sustainability, 2(2).

Abd El Khalek, H., Aziz, R. F., & Kamel, H. M. (2016). Risk and uncertainty assessment model in construction projects using fuzzy logic. American Journal of Civil Engineering, 4(1), 24-39. https://doi.org/10.11648/j.ajce.20160401.13

Ahmed, S. A., Issa, U. H., Farag, M. A., & Abdelhafez, L. M. (2013). Evaluation of risk factors affecting time and cost of construction projects in Yemen. International Journal of Management, 4(5), 168-178.

Asadi, P., Zeidi, J. R., Mojibi, T., Yazdani-Chamzini, A. Y., & Tamosaitiene, J. (2018). Project risk evaluation by using a new fuzzy model based on ELENA guideline. Journal of Civil Engineering and Management, 24(4), 284-300. https://doi.org/10.3846/jcem.2018.3070

Carr, V., & Tah, J. H. M. (2000). A proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics, 18, 491-500. https://doi.org/10.1080/01446190050024905

Carr, V., & Tah, J. H. M. (2001) A fuzzy approach to construction project risk assessment and analysis construction project risk management system. Advances in Engineering Software, 32, 847-857. https://doi.org/10.1016/S0965-9978(01)00036-9

Cheng, J., Xu, S.-M., & Chen, Z.-R. (2018, August). A fuzzy logic-based method for risk assessment of bridges during construction. In The 2018 Structures Congress (Structures 18) (pp. 27-31). Songdo Convensia, Incheon, Korea.

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

Gohar, A. S., Khanzadi, M., & Farmani, M. (2012). Identifying and evaluating risks of construction projects in fuzzy environment: A case study in Iranian construction industry. Indian Journal of Science and Technology, 5, 3593-3602.

Idrus, A., Nuruddin, M. F., & Rohman, M. A. (2011). Development of project cost contingency estimation model using risk analysis and fuzzy expert system. Expert Systems with Applications, 38, 1501-1508. https://doi.org/10.1016/j.eswa.2010.07.061

Issa, U. H. (2012a). A model for time overrun quantification in construction of industrial projects based on risk evaluation. Journal of American Science, 8(8), 523-529.

Issa, U. H. (2012b). Developing an assessment model for factors affecting the quality in the construction industry. Journal of Civil Engineering and Architecture, 6(3), 364-371. https://doi.org/10.17265/1934-7359/2012.03.010

Issa, U. H., & Ahmed, A. (2014). On the quality of driven piles construction based on risk analysis. International Journal of Civil Engineering, Transaction B: Geotechnical Engineering, 12.

Issa, U. H., & Salama, I. M. (2018). Improving productivity in Saudi Arabian construction projects: An analysis based on Lean techniques. International Journal of Applied Engineering Research, 13(10), 8669-8678.

Issa, U. H., Farag, M. A., Abdelhafez, L. M., & Ahmed, S. A. (2015). A risk allocation model for construction projects in Yemen. Civil and Environmental Research, 7(3).

Issa, U. H., Miky, Y. H., & Abdel-Malak, F. F. (2019). A decision support model for civil engineering projects based on multi-criteria and various data. Journal of Civil Engineering and Management, 25(2), 100-113. https://doi.org/10.3846/jcem.2019.7551

Khazaeni, G., Khanzadi, M., & Afshar, A. (2012). Optimum risk allocation model for construction contracts: fuzzy TOPSIS approach. Canadian Journal of Civil Engineering, 39(7), 789800. https://doi.org/10.1139/l2012-038

Kosko, B. (1992). Neural networks and fuzzy systems. PrenticeHall.

Morote, A. N., & Vila, F. R. (2011). A fuzzy approach to construction project risk assessment. International Journal of Project Management, 29, 220-231. https://doi.org/10.1016/j.ijproman.2010.02.002

Mosaad, S. A. A., Issa, U. H., & Hassan, M. S. (2018). Risks affecting the delivery of HVAC systems: Identifying and analysis. Journal of Building Engineering, 16, 20-30. https://doi.org/10.1016/j.jobe.2017.12.004

Nawar, S. E. M., Hosny, K., & Nassar, K. (2017). Owner time and cost contingency estimation for building construction projects in Egypt (Master’s thesis). School of Sciences and Engineering, the American University in Cairo. https://doi.org/10.1061/9780784481271.036

PM Institute. (2004). A guide to the project management body of knowledge (PMBOK Guide) (3rd ed.). USA.

Rezakhani, P. (2011). Fuzzy risk analysis model for construction projects. International Journal of Civil and Structural Engineering, 2(2), 507-522.

Rezakhani, P. (2012). Fuzzy multi criteria decision making model for risk factor selection in construction projects. Buletinul Institutului Polotenic Din IAŞI, 128-142.

Senouci, A., Ismail, A. A., & Eldin, N. (2016). Time and cost overrun in public construction projects in Qatar. In Creative Construction Conference 2016 (pp. 231-236). Budapest, Hungary. https://doi.org/10.1016/j.proeng.2016.11.632

Sharma, S., & Goyal, P. K. (2014). Cost overrun assessment model in fuzzy environment. American Journal of Engineering Research, 3, 44-53.

Singh, S., & Trivedi, M. K. (2012). Application of fuzzy logic in delay analysis in construction. International Journal of Computational Engineering Research, 2(2), 599-605.

Yazdani-Chamzini, Y. A. (2014). Proposing a new methodology based on fuzzy logic for tunnelling risk assessment. Journal of Civil Engineering and Management, 20(1), 82-94. https://doi.org/10.3846/13923730.2013.843583

Zeng, J., An, M., & Smith, N. J. (2007). Application of a fuzzy based decision-making methodology to construction project risk assessment. International Journal of Project Management, 25, 589-600. https://doi.org/10.1016/j.ijproman.2007.02.006

Zhang, G., & Zou, P. X. W. (2007). Fuzzy analytical hierarchy process risk assessment approach for joint venture construction projects in China. Journal of Construction Engineering and Management, 133, 771-779. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:10(771)