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http://dx.doi.org/10.12989/acd.2020.5.4.445

Identification and risk management related to construction projects  

Boughaba, Amina (Department of Civil Engineering, University 20 Aout 1955, LMGHU Laboratory)
Bouabaz, Mohamed (Department of Civil Engineering, University 20 Aout 1955, LMGHU Laboratory)
Publication Information
Advances in Computational Design / v.5, no.4, 2020 , pp. 445-465 More about this Journal
Abstract
This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.
Keywords
risk management; construction projects; recurrent neural network; fuzzy logic; hybrid model;
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1 Mahmoud, M.M.S. and Hassan, T.A. (2015), "Analysis of risk factors for highway construction projects in Egypt", J. Civil Eng. Architect., 9(2015), 526-533. 10.17265/1934-7359/2015.05.004
2 A, Sotoudeh.G. and M, Kahanzadi. and M, Parchami.J. (2011), "A fuzzy MCDM for evaluating risk of construction projects", Australian J. Basic Appl. Sci., 5(12), 162-171.
3 Agnieszka, D. and Mariusz, R. (2015), "Risk analysis in construction project - chosen methods", Procedia Eng., 122(2015), 258-265. https://doi.org/10.1016/j.proeng.2015.10.034.   DOI
4 Azadeh, S. and Mehdi, R. (2015), "Risk determination, prioritization, and classifying in construction project case study: Gharb Tehran commercial-administrative complex", J. Construct. Eng., 2015, 1-10. http://dx.doi.org/10.1155/2015/203468.
5 Cheng, S.G.H.A.R. (2013), "The identification and management of major risks in the malaysian construction industry", J. Construct. Develop. Countries., 18(1), 19-32.
6 Elman, J.L. (1990), "Finding structure in time", Cognitive Sci., 14(1990), 179-211. https://doi.org/10.1207/s15516709cog1402_1.   DOI
7 Gohar, A.S., Khanzadi, M. and Farmani, M. (2012), "Identifying and evaluating risks of construction projects in fuzzy environment: A case study in Iranian construction industry", Indian J. Sci. Technol., 5(11), 3593-3602.
8 Gohar, A.S., Khanzadi, M. and Farmani, M. (2012), "Identifying and evaluating risks of construction projects in fuzzy environment: A case study in Iranian construction industry", Indian J. Sci. Technol., 5(11), 3593-3602.
9 Greeshma, R.K. and Minu, A.J. (2016), "Assessment of risk factors in construction project using PI method", Int. Res. J. Eng. Technol. (IRJET)., 3(9), 767-770.
10 Mehdi, E. and Reza, G. (2014), "Construction project risk assessment by using adaptive-network-based fuzzy inference system: An empirical study", KSCE J. Civil Eng., 18(5), 1213-1227.   DOI
11 Mohsen, A., Kourosh, B., Abdollah, A. and Zoran, K. (2017), "Comprehensive risk management using fuzzy FMEA and MCDA techniques in highway construction projects", J. Civil Eng. Manage., 23(2), 300-310. https://doi.org/10.3846/13923730.2015.1068847.
12 Murat, G. and Ahmad, M.A.Y. (2018), "Analysis of project success factors in construction industry", Technol. Economic Develop. Economy., 24(1), 67-80. https://doi.org/10.3846/20294913.2015.1074129.
13 Qbal, S., Choudhry, R.M., Holschemacher, K., Ali, A. and Tamosaitiene, J. (2014), "Risk management in construction projects", Int. J. Advan. Appl. Sci. Eng. (IJAEAS)., 1(3), 162-166.
14 Radek, D. (2016), "An evaluation of total project risk based on fuzzy logic", Business: Theory Practice, 17(1), 23-31.
15 Rafiq, M.C.H. and Khurram, I. (2013), "Identification of risk management system in construction industry in Pakistan", J. Manage. Eng., 29, 42-49. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000122.   DOI
16 Renuka, S.M., Umarani, C. and Kamal, S. (2014), "A review on critical risk factors in the life cycle of construction projects", J. Civil Eng. Res., 4(2A), 31-36.
17 Sathishkumar, V., Raghunath, P.N. and Suguna, K. (2015), "Critical factors influencing to management risk in construction projects", Int. J. Eng. Sci. (IJES)., 4(1), 37-46.
18 Sotoudeh, G.A., Khanzadi, M., Jalal, M. and Parchami, A. (2011), "A fuzzy MCDM for evaluating risk of construction projects", Austra. J. Basic Appl. Sci., 5(12), 162-171.
19 Sotoudeh, G.A., Khanzadi, M., Jalal, M. and Parchami, A. (2015), "Risk determination, prioritization, and classifying in construction project case study: Gharb Tehran commercial-administrative complex", J. Construct. Eng., 2015, 1-10. http://dx.doi.org/10.1155/2015/203468.
20 Hesham, A., Remon, F.A. and Hamada, M.K. (2016), "Risk and uncertainty assessment model in construction projects using fuzzy logic", Amer. J. Civil Eng., 4(1), 24-39.   DOI
21 Juan, D.B., Cedrick, B. and Daniel, N. (2013), "Maitrise des risques dans le processus de reponse a appel d'offres", Compiegne, France.
22 Hocine, A., Yasmina, K., Mohamed, G. and Bakhta, B. (2018), "Compressive strength prediction of limestone filler concrete using artificial neural networks", Advan. Comput. Design., 3(3), 289-302. https://doi.org/10.12989/acd.2018.3.3.289.   DOI
23 Hopfield. J.J. (1982), "Neural networks and physical systems with emergent collective computational abilities", Proceedings of the National Academy of Science of the United States of America (PNAs)., 79(1982), 2554-2558.   DOI
24 Jordan, M.I. (1997). "Serial order: A parallel distributed processing approach", Advan. Psychology. 121, 471-495.   DOI
25 Jyh, S.R.J. (1993), "ANFIS: adaptive-network-based fuzzy inference system", IEEE Transactions on Syst., Man Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541.   DOI
26 K, Jayasudha. and B, Vidivelli. (2016), "Analysis of major risks in construction projects", ARPN J. Eng. Appl. Sci., 11(11), 6943-6950.
27 K, Jayasudha. and B, Vidivelli. and E, R.G. Surjith. (2014), "Risk assessment and management in construction projects", Int. J. Sci. Eng. Res., 5(8), 387-396.
28 Kansal, R.K. and Sharma, M. (2012), "Risk assessment methods and application in the construction projects", Int. J. Modern Eng. Res. (IJMER)., 2(3), 1081-1085.
29 Mahendra, P.A., Pitroda, J.R. and Bhavsar, J.J. (2013), "A study of risk management techniques for construction projects in developing countries", Int. J. Innovat. Technol. Explor. Eng. (IJITEE)., 3(5), 139-142.
30 Surabhi, M. and Brajesh, M. (2016), "A study on risk factors involved in the construction projects", Int. J. Innovative Res. Sci., Eng. Technol., 5(2), 1190-1196.
31 Ubani, C., Amade, B., Okorocha, K. A., Agwu, F. and Okogbuo, F. (2015), "Project risk management issues in the Nigerian construction industry", Int. J. Eng. Technical Res. (IJETR)., 3(1), 217-232.
32 Vladimir, C., Oleksandar, D. and Liudmyla, D. (2016), "Fuzzy logic approach to SWOT analysis for economics tasks and example of its computer realization", Bull. Transilvania Univ. Brasov., 9(58), 317-326.