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Using meta-networks to analyse the impact of adverse random events on the time and cost of completing construction work

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
According to the concept of a system-based approach, a construction project can be treated as a complex system composed of various elements, such as human, equipment and material resources, as well as knowledge and tasks that are mutually interlinked. In the classical approach to construction project risk assessment, the impact of the “system” in the analysis of relationships between risk sources and their consequences has so far been neglected. The concept of construction project vulnerability and its adaptability has appeared in literature in recent years. It is analysed on the basis of a project’s vulnerability to the impact of risk factors and its adaptive capacity is seen an answer to project perturbations caused by adverse random events. As a part of developing the system-based approach to analysing construction project schedule, the author further developed the concept of modelling planned construction projects with relationship meta-networks composed of four types of nodes: agents (human resources), knowledge, equipment and material resources and tasks. The author included possible deviations from the planned project’s budget in the schedule vulnerability and adaptability analysis, instead of only focusing on deviations from its completion deadline. An analysis of the occurrence of additional and replacement work was introduced by the author, which further developed the concept of the simulated evolution of such networks to include the capacity to introduce new nodes and links into their structure. Furthermore, the author used the potential of weighted meta-networks to model certain dependencies within the planned project. A simulation-based approach as a part of DNA (dynamic network analysis) was used to analyse the vulnerability and adaptability of such networks. The proposed approach was presented on the example of a renovation project performed on a historical structure. The conclusions drawn from the author’s analyses can be used to formulate construction project schedules that are less vulnerable to perturbations and are characterised by greater adaptability. In the future, the author plans to expand the analysis presented above to include dependencies in single-mode networks (e.g. in agent, resource or knowledge networks) on the meta-network of a project.
Rocznik
Strony
192--202
Opis fizyczny
Bibliogr. 22 poz., rys., tab., zdj.
Twórcy
  • Politechnika Krakowska, Wydział Inżynierii Lądowej, ul. Warszawska 24, 31-155 Kraków, Poland
Bibliografia
  • Adger, W.N. (1999). Social vulnerability to climate change and extremes in Coastal Vietnam. World Development, 27(2), 249-269.
  • Agarwal, J. & Blockley, D.I. (2007). Structural integrity: hazard, vulnerability and risk. International Journal of Materials and Structural Integrity, 1(1-3), 117-127.
  • Baloi, D. & Price, A.D. (2003). Modelling global risk factors affecting construction cost performance. International Journal of Project Management, 21(4), 261-269.
  • Brooks, N. (2003). Vulnerability, risk and adaptation: A conceptual framework. Tyndall Centre for Climate Change Research Working Paper, 38(38), 1-16.
  • Buckle, P., Marsh, G. & Smale, S. (2001). Assessing resilience and vulnerability: Principles, strategies and actions. Guidelines. Emergency Management Australia.
  • Center for Computational Analysis of Social and Organizational Systems [COSAS] (2018). The Organizational Risk Analyzer (ORA). Retrieved from: http://www.casos.cs.cmu.edu/projects/ora/software.php.
  • Dikmen, I., Birgonul, M.T. & Fidan, G. (2008). Assessment of project vulnerability as a part of risk management in construction. In Proceedings of Joint 2008 CIB W065/W055 Symposium (pp. 15-17).
  • Ezell, B.C. (2007). Infrastructure vulnerability assessment model (I-VAM). Risk Analysis: An International Journal, 27(3), 571-583.
  • Kasprowicz, T. (2001). Interdependent analysis of construction project cost and time. AACE International Transactions, CS21.
  • Kasprowicz, T. (2003). Analiza systemowa przedsięwzięć budowlanych [System analysis of construction projects]. In Proceedings of XLIX Academic Conference of the Civil Engineering Committee of the Polish Academy of Science and the Committee of Science of the PZITB, Krynica 14-19.09.2003 (pp. 179-186).
  • Krackhardt, D. & Carley, K.M. (1998). PCANS model of structure in organizations (pp. 113-119). Pittsburgh, Pa, USA: Carnegie Mellon University, Institute for Complex Engineered Systems.
  • Li, Y.K., Qian, L.L., He, Q.H. & Duan, Y.F. (2014). Meta-network Based Fitness Measurement of Projects Organization and Tasks Assignment. In Proceedings of the 17th International Symposium on Advancement of Construction Management and Real Estate (pp. 643-655). Springer: Berlin, Heidelberg.
  • Prowse, M. (2003). Towards a clearer understanding of ‘vulnerability’ in relation to chronic poverty. CPRC Working Paper, 24.
  • Reminga, J. & Carley, K.M. (2003). Measures for ORA (Organizational Risk Analyzer). Institute for Software Research School of Computer Science. Carnegie Mellon University, 22.
  • Skorupka, D. (2007). Metoda oceny ryzyka realizacji przedsięwzięć inżynieryjno-budowlanych [Risk identification and assessment method in the construction projects]. Zeszyty Naukowe WSOWL, 3(145), 79-88.
  • Śladowski, G. [in print]. Use of meta-networks to evaluate key agents, knowledge and resources in the planning of construction projects. Archives of Civil Engineering, 64(3).
  • Vidal, L.A. & Marle, F. (2012). A systems thinking approach for project vulnerability management. Kybernetes, 41(1/2), 206-228.
  • Zhang, H. (2007). A redefinition of the project risk process: Using vulnerability to open up the event-consequence link. International Journal of Project Management, 25(7), 694-701.
  • Zhu, J., Mostafavi, A. (2015). An integrated framework for assessment of the impacts of uncertainty in construction projects using dynamic network simulation. In 2015 ASCE International Workshop on Computing in Civil Engineering. Austin, TX.
  • Zhu, J. & Mostafavi, A. (2017). Characterization of the underlying mechanisms of vulnerability in complex projects using dynamic network simulation. In Proceedings of Simulation Conference (WSC), 2017 Winter (pp. 2436-2447). IEEE.
  • Zhu, J., Mostafavi, A. & Ahmad, I. (2014). System-of-systems modeling of performance in complex construction projects: A multimethod simulation paradigm. Computing in Civil and Building Engineering, 1877-1884. DOI: 10.1061/9780784413616.23
  • Zou, P.X., Zhang, G. & Wang, J. (2007). Understanding the key risks in construction projects in China. International Journal of Project Management, 25(6), 601-614.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-074eb465-d4c9-4721-b75f-8c436c124764
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