Skip to main content
SpringerLink
Log in

Big Data and Cloud Computing for the Built Environment

  • Chapter
  • First Online:
Industry 4.0 for the Built Environment

Part of the book series: Structural Integrity ((STIN,volume 20))

Abstract

Designing the built environment is by default a multidisciplinary endeavour, producing an abundance of data that needs to be analysed during the process. This data is associated with specific design solutions and driven by multiple, usually competing objectives that need to be taken into consideration during fast review cycles. Quantifiable data ranges from simple area measurements, to more elaborate metrics such as thermal performance, carbon footprint or contextual integration, derived by a plethora of time-consuming analyses. The need to create a built environment which is not only functional and elegant but also energy efficient and sustainable is making performance-oriented design one of the main driving forces in contemporary architecture. A by-product of this practice is the large data sets that it can produce, which in turn raises the question of how the industry can deal with all this data—not only in terms of production, but also classification and reuse. This has been a catalyst to investigating how other industries are dealing with similar issues. The shift, for example, towards big data and the adoption of cloud computing, has enabled IT companies to dramatically increase performance and efficiency of many industries over the past years. This runs contrary to contemporary tools used for architectural computing, traditionally built around a single workstation, and their respective workflows. This problem is firstly challenged by explaining the technology behind both big data and cloud computing while comparing them to state-of-the-art computer-aided design (CAD) software. Additionally, cloud-based software development and continuous delivery strategies are analysed and the potential improvement on design pipelines, based on experience. Then a prototype of a bespoke system called Hydra will be presented, which runs on a high-performance compute cluster combining multiobjective optimization, a popular parametric CAD system and a set of building performance analyses. The data produced by the system is stored in a database and visualized using a modern web-based interface. The system is being demonstrated and assessed on a large-scale master planning project case study, where Hydra’s benefits are more evident due to project’s complexity and size.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bilal, M., Oyedele, L.O., Qadir, J., et al.: Big data in the construction industry: a review of present status, opportunities, and future trends. Adv. Eng. Inform. 30, 500–521 (2016). https://doi.org/10.1016/j.aei.2016.07.001

  2. Loyola, M.: Big data in building design: a review. J. Inf. Technol. Constr. 23, 259–284 (2018)

    Google Scholar 

  3. Tsigkari, M., Chronis, A., Conrad Joyce, S., et al.: Integrated design in the simulation process. Proc. Symp. Simul. Archit. Urban Des. 28, 1–28:8 (2013)

    Google Scholar 

  4. Chronis, A., Tsigkari, M., Giouvanos, E., et al.: Performance driven design and simulation interfaces: a multi-objective parametric optimization process. Proc. SimAUD SCS SpringSim’ 12, 81–88 (2012)

    Google Scholar 

  5. Bragança, L., Vieira, S.M., Andrade, J.B.: Early stage design decisions: the way to achieve sustainable buildings at lower costs. Sci. World J. 2014, 1–8 (2014). https://doi.org/10.1155/2014/365364

    Article  Google Scholar 

  6. NIST Big Data Public Working Group.: NIST Big Data Interoperability Framework: vol. 1, Definitions. Gaithersburg, MD (2015)

    Google Scholar 

  7. Sh. Hajirahimova, M., Aliyeva, A.S.: About big data measurement methodologies and indicators. Int. J. Mod. Educ. Comput. Sci. 9, 1–9 (2017). https://doi.org/10.5815/ijmecs.2017.10.01

  8. Wikipedia. Wikipedia:Size_of_Wikipedia 20/11/20 (2020). Accessed 20 Nov 2020

    Google Scholar 

  9. Google Storage Cloud. https://cloud.google.com/storage. Accessed 1 Feb 2021

  10. Amazon Simple Storage Service. https://aws.amazon.com/s3/. Accessed 1 Jan 2021

  11. Azure Data Lake. https://azure.microsoft.com/en-gb/solutions/data-lake/. Accessed 1 Jan 2021

  12. Oracle Database. https://www.oracle.com/uk/database/. Accessed 1 Jan 2021

  13. Amazon Redshift. https://aws.amazon.com/redshift/. Accessed 1 Jan 2021

  14. Azure Synapse. https://azure.microsoft.com/en-gb/services/synapse-analytics/. Accessed 1 Jan 2021

  15. Zikopoulos, P., Eaton, C., IBM.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, 1st edn. McGraw-Hill Osborne Media (2011)

    Google Scholar 

  16. Lukoianova, T., Rubin, V.L.: Veracity roadmap: is big data objective, truthful and credible? Adv. Classif. Res. Online 24, 4 (2014). https://doi.org/10.7152/acro.v24i1.14671

    Article  Google Scholar 

  17. Ghemawat, S., Gobioff, H., Leung, S.-T.: The Google file system. SIGOPS Oper. Syst. Rev. 37, 29–43 (2003). https://doi.org/10.1145/1165389.945450

    Article  Google Scholar 

  18. Chang, F., Dean, J., Ghemawat, S., et al.: BigTable: a distributed storage system for structured data. In: OSDI 2006—7th USENIX Symposium Operating Systems Design and Implementation, pp. 205–218 (2006)

    Google Scholar 

  19. Witten, I.H., Frank, E., Hall, M.A.: References. In: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn, pp. 587–605. Elsevier, Boston (2011)

    Google Scholar 

  20. Sivarajah, U., Kamal, M.M., Irani, Z., Weerakkody, V.: Critical analysis of big data challenges and analytical methods. J. Bus. Res. 70, 263–286 (2017). https://doi.org/10.1016/j.jbusres.2016.08.001

    Article  Google Scholar 

  21. Wang, L., Von Laszewski, G., Younge, A., et al.: Cloud computing: a perspective study. New Gener. Comput. 28, 137–146 (2010). https://doi.org/10.1007/s00354-008-0081-5

    Article  MATH  Google Scholar 

  22. Bilal, K., Khan, S.U., Kolodziej, J., et al.: A comparative study of data center network architectures. In: Proceedings—26th European Conference on Modelelling and Simulation, ECMS 2012 (2012). https://doi.org/10.7148/2012-0526-0532

  23. Smith, J.E., Nair, R.: The architecture of virtual machines. Computer (Long Beach Calif) 38, 32–38 (2005). https://doi.org/10.1109/MC.2005.173

    Article  Google Scholar 

  24. Fox, G., Gannon, D., Thomas, M.: Editorial: a summary of grid computing environments. Concurr. Comput. Pract. Exp. 14, 1035–1044 (2002). https://doi.org/10.1002/cpe.734

    Article  Google Scholar 

  25. Shahzadi, S., Iqbal, M., Qayyum, Z.U., Dagiuklas, T.: Infrastructure as a service (IaaS): a comparative performance analysis of open-source cloud platforms. In: IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2017-June (2017). https://doi.org/10.1109/CAMAD.2017.8031522

  26. Keller, E., Rexford, J.: The “Platform as a Service”; Model for Networking (2010)

    Google Scholar 

  27. Gribaudo, M., Iacono, M., Manini, D.: Performance evaluation of replication policies in microservice based architectures. Electron Notes Theor. Comput. Sci. 337, 45–65 (2018). https://doi.org/10.1016/j.entcs.2018.03.033

    Article  Google Scholar 

  28. Garriga, M.: Towards a Taxonomy of Microservices Architectures, pp. 203–218 (2018)

    Google Scholar 

  29. Fielding, R.T.: Architectural Styles and the Design of Network-Based Software Architecturese. University of California, Irvine (2000)

    Google Scholar 

  30. Facebook. Rapid release at massive scale. In: Facebook English (2017). https://engineering.fb.com/2017/08/31/web/rapid-release-at-massive-scale/#:~:text=Althoughwepushtoproduction,orsoAndroidbetatesters. Accessed 1 Dec 2020

  31. Rhinoceros3d. McNeel Association. https://www.rhino3d.com (2020). Accessed 20 Nov 2020

  32. Hypar.io. Hypar. https://hypar.io (2020). Accessed 20 Nov 2020

  33. Rhino.Compute. McNeel Association. https://developer.rhino3d.com/guides/#compute (2020). Accessed 20 Nov 2020

  34. Swarm. Thort. Tomasseti Core. http://core.thorntontomasetti.com/announcing-swarm/. Accessed 20 Nov 2020

  35. Roudsari, M.S., Mackey, C.: Pollination. https://pollination.cloud/ (2020). Accessed 20 Nov 2020

  36. Matteo, S.D.C.: Speckle. https://speckle.systems (2015). Accessed 20 Nov 2020

  37. The buildings and habitats object model. In: Buro Happold. https://bhom.xyz/. Accessed 20 Nov 2020

  38. Spacemaker.ai. Spacemaker. https://www.spacemakerai.com. Accessed 20 Nov 2020

  39. Giraffe. https://www.giraffe.build/ (2020). Accessed 20 Nov 2020

  40. Harness, C.J.: TestFit.io. https://testfit.io/ (2020). Accessed 20 Nov 2020

  41. Coorey, B.: Archistar. https://archistar.ai/. Accessed 20 Nov 2020

  42. Sidewalklabs. Delve. https://hello.delve.sidewalklabs.com/. Accessed 20 Nov 2020

  43. Li, S., Liu, L., Peng, C.: A review of performance-oriented architectural design and optimization in the context of sustainability: dividends and challenges. Sustain 12 (2020). https://doi.org/10.3390/su12041427

  44. Müller, P., Wonka, P., Haegler, S., et al.: Procedural modeling of buildings. ACM Trans. Graph. 25, 614–623 (2006). https://doi.org/10.1145/1141911.1141931

    Article  Google Scholar 

  45. Dawkins, R.: The selfish gene. In: 40th Anniversary. Oxford University Press, Oxford, United Kingdom (2016)

    Google Scholar 

  46. Flake, G.W.: The Computational Beauty of Nature. MIT Press, Cambridge, MA, USA (1998)

    MATH  Google Scholar 

  47. Emmerich, M.T.M., Deutz, A.H.: A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat. Comput. 17, 585–609 (2018). https://doi.org/10.1007/s11047-018-9685-y

    Article  MathSciNet  Google Scholar 

  48. Bandaru, S., Ng, A.H.C.C., Deb, K.: Data mining methods for knowledge discovery in multi-objective optimization: part B—new developments and applications Sunith. Expert Syst. Appl. 70, 119–138 (2017). https://doi.org/10.1016/j.eswa.2016.10.016

    Article  Google Scholar 

  49. Coello, C.: A short tutorial on evolutionary multiobjective optimization. Evol. Multi-Criterion Optim. 1993, 21–40 (2001). https://doi.org/10.1007/3-540-44719-9_2

    Article  MathSciNet  Google Scholar 

  50. Bhattacharya, M., Islam, R., Abawajy, J.: Evolutionary optimization: a big data perspective. J. Netw. Comput. Appl. 59, 416–426 (2016). https://doi.org/10.1016/j.jnca.2014.07.032

    Article  Google Scholar 

  51. Kosicki, M., Tsiliakos, M., Tsigkari, M.: Hydra distributed multi-objective optimization for designers. In: Impact: Design With All Senses. Springer International Publishing, Cham, pp. 106–118 (2020)

    Google Scholar 

  52. Branke, J., Schmeck, H., Deb, K., Reddy, S.M.: Parallelizing multi-objective evolutionary algorithms: cone separation. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), pp. 1952–1957. IEEE (2005)

    Google Scholar 

  53. Van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7, 144–173 (2003). https://doi.org/10.1109/TEVC.2003.810751

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Foster + Partners, Applied Research & Development, London, UK

    Marcin Kosicki, Marios Tsiliakos, Khaled ElAshry & Martha Tsigkari

Authors
  1. Marcin Kosicki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marios Tsiliakos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khaled ElAshry
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martha Tsigkari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcin Kosicki .

Editor information

Editors and Affiliations

  1. Mace, London, UK

    Marzia Bolpagni

  2. Europe Foundation, Digital Built Environment Institute (DBEI), Delft, The Netherlands

    Rui Gavina

  3. Department of Civil Engineering, School of Engineering of Polytechnic of Porto, Porto, Portugal

    Diogo Ribeiro

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kosicki, M., Tsiliakos, M., ElAshry, K., Tsigkari, M. (2022). Big Data and Cloud Computing for the Built Environment. In: Bolpagni, M., Gavina, R., Ribeiro, D. (eds) Industry 4.0 for the Built Environment. Structural Integrity, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-82430-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-82430-3_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82429-7

  • Online ISBN: 978-3-030-82430-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation