Safe City

Safe City

Prioritize Mechanical, Electrical and Plumbing Elements in Clash Resolution Process from the Cost and Time Aspect by Fuzzy-AHP METHOD

Document Type : Original Article

Authors
1 Department of Civil Engineering, Faculty of Civil Engineering and Land Resources, Central Tehran Branch, Islamic Azad University
2 Faculty of Civil Engineering and Land Resources, Central Tehran Branch, Islamic Azad University
3 Faculty of Civil Engineering, University of Science and Technology
Abstract
In recent years, with the advent of building information modeling (BIM), significant progress has been
made in the quality of the designs and execution of projects. BIM-based models are widely applied to
project safety planning and control of time and cost in construction projects. The use of Building
Information Modeling has also shown a positive influence on process of clash detection and clash
resolution. During the design stages, BIM drawings and plans produced by individual designers are
integrated into a federated model and tested to detect design clashes, which if they are not carefully
detected and resolved in the design phase, they risk the safety, time, and cost of the project management
in addition to increasing the workload. Because of the confined spaces left for MEP systems, between the
various elements, mechanical, electrical and plumbing (MEP) design clashes have traditionally dogged
the design process. The purpose of this study is to grouping MEP elements to determine their priority in
terms of time and cost in the clash resolution process. This research uses the Delphi method to group
MEP elements and then applied the Fuzzy-AHP method to determine the weight of MEP elements. 
Keywords

  1. Hsu, H.C. et al. (2020). Knowledge-based system for resolving design clashes in building information models, Automation in Construction, 110 (September2019). doi:10.1016/j.autcon. 2019.10300.
  2. Hu, Y., Castro-lacouture, D. and Eastman, C.M. (2019). ‘Holistic clash detection improvement using a component dependent network in BIM projects’, Automation in Construction, 105(April), p. 102832. doi: 10.1016/j.autcon.2019.102832.
  3. Bernstein, H.M., & Jones, S.A. (2012). Smart Market Report: The Business Value of BIM in North America. Bedford, MA: McGraw-Hill Construction.
  4. Eastman, C., Eastman, C.M., Teicholz, P., & Sacks, R. (2011). BIM handbook: A guide to building information modeling for owners, managers, designers, engineers and contractors (2nd Edition). Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/97804’.
  5. Bagwat, R. Shinde. (2016). Clash Detection: A New Tool in Project Management, International Journal of Scientific Research in Science, Engineering and Technology, Vol. 2, No. 4, pp. 193–197. Available via: http://ijsrset.com/ paper/1637.
  6. Khanzode, A. (2010). An integrated, virtual design and construction and lean (IVL) method for coordination of MEP. CIFE Center for Integrated Facility Engineering Technical Report (Vol. 187). Stanford, CA. Retrieved from https://www.dpr.com/assets/doc.
  7. Hartmann, T. (2010). Detecting design conflicts using building information models: a comparative lab experiment, Proceedings of the CIB W78 2010: 27th International Conference, Cairo, Egypt, 16-18 November pp. 16-18.
  8. Mehrbod, S. et al. (2019). Beyond the clash: Investigating BIM-based building design coordination issue representation and resolution’, Journal of Information Technology in Construction, 24(October 2017), pp. 33–57.
  9. Lin, W.Y. (2019). Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning. Applied Sciences.
  10. Love, P.E.D. & Smith, J. (2016). Toward error Management in Construction: moving beyond a zero vision. J. Constr. Eng. Manag., 142(11), 04016058. https:// doi.org/10.1061/(ASCE)CO. 1943-7862.0001170.
  11. Wang, L. and Leite, F. (2016). Formalized knowledge representation for spatial conflict coordination of mechanical, electrical and plumbing (MEP) systems in new building projects, Automation in Construction. Elsevier B.V., 64, pp. 20-26. doi: 10.1016/j.autcon.2015.12.020.
  12. L.C. Ciribini, S.M. Ventura, M. Paneroni. (2016) implementation of an interoperable process to optimise design and construction phases of a residential building: a BIM pilot project,). Automation in construction.
  13. Gijezen, S. (2016). Organizing 3D Building Information Models with the Help of Work Breakdown Structures to Improve the Clash Detection Process. VISICO Center, Univ. of Twente: Enschede, the Netherlands, 2010.
  14. Hu, Y. et al. (2019). Clash Relevance Prediction Based on Machine Learning. Comput. Civ. Eng. 33(2), doi: 10.1061/(ASCE)CP.1943-5487.0000810.
  15. Van den Helm, P.; Böhms, M.; van Berlo, L. (2010). IFC-based clash detection for the open-source BIM server. In Proceedings of the International Conference on Computing in Civil and Building Engineering, Nottingham, UK, 30 June–2 July 2010; Nottingham University Press: Nottingham, UK, 2010; p. 30. Available online: http://www.engineering.nottingham.ac.uk/ icccbe/proceedings/pdf/pf91.pdf (accessed on 1 May 2019).
  16. Palmer, I.J., Grimsdale, R.L. (1995). Collision Detection for Animation Using Sphere-trees. Comput. Graph. Forum1995, 14, 105-116.
  17. Hubbard, P.M. (1996). Approximating polyhedra with spheres for time critical collision detection. ACM Trans. Graph. 1996, 15, 179-210.
  18. Klosowski, J.T. Held, M. Mitchell, J.S.B. Sowizral, H. Zikan, K. (1998) Efficient collision detection using boundingvolume hierarchies of k-DOPs. IEEE Trans. Vis. Comput. Graph., 4, 21-36.
  19. Gottschalk, S. Lin, M.C. Manocha, D. Hill, C. (2019). Obbtree: A Hierarchical Structure for Rapid Interference Detection. Available online: http://gamma.cs.unc.edu/SSV/obb.pdf.
  20. Akponeware, A.O. Adamu, Z.A. (2017). Clash detection or clash avoidance? An investigation into coordination problems in 3D BIM. Buildings 2017, 7, 75. School of Civil and Building Engineering, Loughborough University, Loughborough LE11 3TU, UK.
  21. Ziolkowski, P. Demczynski, S. Niedostatkiewicz, M. (2017). Assessment of failure occurrence rate for concrete machine foundations used in gas and oil industry by machine learning. Appl. Sci. 2019, 9, 3267.
  22. Hoshyar, A.N. Rashidi, M. Liyanapathirana, R. Samali, B. (2019). Algorithm development for the non-destructive testing of structural damage. Appl. Sci. 2019, 9, 2810.
  23. Korman, T. M., Fischer, M.A., & Tatum, C.B. (2003). Knowledge and reasoning for MEP coordination. J. Constr. Eng. Manag., 129(6), 627–634. https://doi.org/10.1061/ (ASCE)0733 9364(2003)129:6(627).
  24. Ucal Sari, I. (2018). Development of an integrated discounting strategy based on vendors’ expectations using FAHP and FUZZY goal programming. Technol. Econ. Dev. Econ. 2018, 24, 635-652.
  25. Pamucar, D. Petrovic, I. C. irovic, G. (2017). Modification of the Best-Worst andMABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert. Syst. Appl. 2018, 91, 89-106.
  26. Ghorui, N. Ghosh, A. Algehyne, E.A.; Mondal, S.P. Saha, A.K. (2020). AHP-TOPSIS Inspired Shopping Mall Site Selection Problem with Fuzzy Data. Mathematics 2020, 8, 1380.
  27. Scholl, W., M.B. Konig, and P. Heisig. (2004). The future of knowledge management: An international Delphi study. J. Knowl. Manage. 8 (2): 19. https://doi.org/10.1108/ 13673270410529082.
  28. Sun, C. (2010). Expert Systems with Applications A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods, Expert Systems With Applications. Elsevier Ltd, 37(12), pp. 7745-7754. doi: 10.1016/j.eswa.2010.04.066.