Could design clashes become predictable?

by Erika Pärn

Figure 1: Clash detection snapshot (provided by participating contractor)
Figure 1: Clash detection snapshot (provided by participating contractor)

The current paradigm shift in the Architecture, Construction, Engineering and Operations (AECO) sector towards data driven decision making is founded upon an endemic shift towards digitalisation of building data. Data is viewed as the new commodity or ‘oil’ of the information technology and predictive analytics as its new ‘combustion engine’ [1]. Concomitant benefits of data analysis proffered by the more advanced sectors (i.e. finance, manufacturing and aerospace industries) include the inherent potential to uncover patterns, trends and associations related to design data, human behavior, and the interactions between the two, for improved data driven decision making [2, 3]. This is why academics at BCU have sought to investigate whether data driven decision making could help mitigate design clashes with analytics; and specifically whether contractors’ clash detection reports could be used to identify trends and patterns of the most commonly occurring design clashes. To test this we used a recently completed BCU campus project as a case study. This blog post outlines the premise of this novel research and its key findings.

Within this pervasive shift towards a state of continuous digital change for the existing processes of building design and contractual obligations, a more collaborative means of working is eulogised as the new modus operandi in the AECO sector. Key here is the extraction of digitised building data from Building Information Models (BIM). Data within BIM is in a polymorphic (i.e. multi-faceted evolution) state [4] from embryonic stages of design inception to latter stage of pre-construction (herein referred to as ‘to-be BIM’) for final production of a mature digitised 3D model representation of the built assets (commonly referred to as ‘as-built BIM’). In a transition towards the handover of an accurate ‘as-built BIM’ the design data produced requires validation, most commonly done through design clash detection. BIM enabled clash detection is said to offer the potential to mitigate construction errors on-site through collaborative design management and resolution of 3D design clashes in a digital environment [5].

Figure 2: Conceptualisation of clash detection processes (Pärn, et al., 2017)
Figure 2: Conceptualisation of clash detection processes (Pärn, et al., 2017)

The BIM data for a development is produced by individual designers (e.g. the architect, structural engineer and mechanical, electrical and plumbing (MEP) designer) and integrated into a single 3D model and tested to identify design clashes. Design clashes consist of ‘positioning errors’ where building components overlap each other in a 3D BIM environment (i.e. when the original individual designer models are merged into one single model). These mismatches are then used to produce a clash report to help eradicate possible construction errors from occurring later on site. It is the main contractor’s responsibility to produce fortnightly clash detection reports consisting of an aggregate of information pertaining to design clashes. The BCU research study analysed thematic groupings of clashes (specifically MEP model elements vs. structural model elements) within each compartment category and their numerical value in millimetres (mm) to specify the positioning errors. Such clashes are deemed to be the most complex and costly building elements to resolve with construction rework (i.e. completed activities, requiring to be repeated as a result of construction errors). Hitherto this aggregate of rich semantic BIM data on design clashes has largely been overlooked for its potential to yield additional knowledge or insight outside of the confines of current clash detection processes.

Thus, our research highlighted that BIM offers a potential digital solution space for design error management through clash detection as a collaborative and inclusive platform. To date there is little research that investigated the multitudinous semantic data emanating from project clash detection reports [6]; specifically, by probing the clash data produced with analytics to identify descriptive tendencies of the data dispersion and their likelihood of appearance.

The findings offer an exciting new prospect for research in this specific field of scientific enquiry which combines statistical analysis and modelling with BIM. This study found that clashes from the selected sample of MEP and structural clashes can indeed be predicted. This then offers an exciting proposition to the AECO sector as a whole: ‘Could any design clashes become predictable?’ Whilst this study used a narrow and confined sample of design clashes as an exemplar, it does demonstrate that within this small sample of clashes (404 analysed) the trends of data dispersion can be used to predict the likelihood of the most commonly occurring positional overlaps (in mm).

The future implication of this research is to act as a springboard for new investigation and applied examples of development into: automation of the clash detection management; automation of clash resolution process; analysis of the organizational and human resource management influences impacting upon design clash propagation; and new procedural methods to mitigate clash occurrence using a real-life project.

To read a fuller account of this research please access the published journal article:

Pärn, E.A., Edwards, D.J. and Sing, M.C.P. (2017) The Origins and Probabilities of MEP and Structural Design Clashes within a Federated BIM, Automation in Construction, DOI: https://doi.org/10.1016/j.autcon.2017.09.010

Alternatively, you can listen to the video abstract of this research at https://youtu.be/bSu7dMBG0Rk

 

Editor Note: You may also be interested in the blog by Mustafa Cidik “What does the ‘I’ in BIM mean?” http://blogs.bcu.ac.uk/bsbe/what-does-the-i-in-bim-mean/

 

Erika Parn BSc (Hons) PGCert is a Lecturer in Architectural Technology in the School of Engineering and Built Environment. Erika’s extensive teaching and administration duties have included tutorship and module coordination for the Architectural Technology course and PhD supervision in the domain of digital construction and engineering. Erika’s own research interests focus predominantly upon the multi-disciplinary area of ‘digital built environment and smart city developments’ but she remains actively involved in other broader ‘construction and civil engineering management’ topics whilst working with her international colleagues.

 

References/ Further reading

[1] Marr, B. (2015) The most revealing big data quotes Available via: https://www.weforum.org/agenda/2015/01/the-most-revealing-big-data-quotes/

[2] Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011) Big data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.

[3] Russom, P. (2013) Managing big data. TDWI Best Practices Report, TDWI Research, Vol., No., pp. 1-40. Available via: https://www.pentaho.com/sites/default/files/uploads/resources/tdwi_best_practices_report-_managing_big_data.pdf.

[4] Stroustrup, B. (2007). Bjarne Stroustrup’s C++ Glossary. Polymorphism – providing a single interface to entities of different types.

[5] Solihin, W., Eastman, C., and Lee, Y. C. (2016) A Framework for Fully Integrated Building Information Models in a Federated Environment. Advanced Engineering Informatics, Vol. 30, No. 2, pp. 168-189. DOI: http://dx.doi.org/10.1016/j.aei.2016.02.007

[6] Won, J., and Lee, G. (2016) How to tell if a BIM project is successful: A goal-driven approach. Automation in Construction, Vol. 69, No., pp. 34-43. DOI: http://dx.doi.org/10.1016/j.autcon.2016.05.022

 

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