Benefits and Challenges of BIM in Construction

  • Bruno DaniottiEmail author
  • Alberto Pavan
  • Sonia Lupica Spagnolo
  • Vittorio Caffi
  • Daniela Pasini
  • Claudio Mirarchi
Part of the Springer Tracts in Civil Engineering book series (SPRTRCIENG)


This chapter covers the issues and benefits deriving from the introduction of digital processes and tools in enterprises in the building sector. A comparative analyzes is proposed between the current processes and the relevant information flows and the possibilities offered by the introduction of digital processes and tools. Starting from the different perspective given by the digital paradigm, the chapter analyzes how the increasing request for information and data and the need to produce information models that accompany the physical asset are changing the configuration of roles and relations between enterprises and the supply chain creating the need for new specialist management and collaboration structures (platforms) in the production phase. The second part of the chapter proposes a view on the possibilities offered by the introduction of machine learning systems for the management of information in enterprises. In particular, the potentialities of the current systems in organizing information and documents are analyzed for an improved management of the latter, both during the collection phase and with regard to the possibility to use the organization’s historical documents in order to define the analysis processes.


  1. 1.
    Alavi M, Leidner DE (2001) Knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quart 25(1):107–136. Scholar
  2. 2.
    ANCE (2017) Osservatorio congiunturale sull’industria delle costruzioniGoogle Scholar
  3. 3.
    Bloch T, Sacks R (2018) Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Autom Constr 91:256–272. Scholar
  4. 4.
    BSI (2013) PAS 1192-2:2013, specification for information management for the capital/delivery phase of construction projects using building information modelling. The British Standards Institution, UKGoogle Scholar
  5. 5.
    DTI UK (2006) Construction statistic annual. Department of Trade and Industry, LondonGoogle Scholar
  6. 6.
    Fernie S et al (2001) Learning across business sectors: context, embeddedness and conceptual chasms. In: 17th Annual ARCOM conference. University of Salford, 5–7 Sept 2001, pp 557–565.
  7. 7.
    Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRefGoogle Scholar
  8. 8.
    Lundvall BÅ, Johnson B (1994) The learning economy. J Ind Stud 1:23–42CrossRefGoogle Scholar
  9. 9.
    Mirarchi C (2019) Knowledge network for innovation of construction sector: increasing efficiency through process digitisation of the entire chain. Politecnico di Milano.
  10. 10.
    Mirarchi C, Pavan A (2019) Building information models are dirty. In: 2019 European conference on computing in construction. Chania, GreeceGoogle Scholar
  11. 11.
    Nonaka I, Takeuchi H (1995) The knowledge-creating company: how Japanese companies create the dynamics of innovation. Oxford University Press, New York and OxfordGoogle Scholar
  12. 12.
    République Francaise (2018) Kroqui platform. Accessed on 26 Sep 2018
  13. 13.
    Tversky A, Kahneman D (1973) Availability: a heuristic for judging frequency and probability. Cognit Psychol 5(2):207–232. Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Bruno Daniotti
    • 1
    Email author
  • Alberto Pavan
    • 2
  • Sonia Lupica Spagnolo
    • 3
  • Vittorio Caffi
    • 4
  • Daniela Pasini
    • 5
  • Claudio Mirarchi
    • 6
  1. 1.Dipartimento ABCPolitecnico di MilanoMilanItaly
  2. 2.Dipartimento ABCPolitecnico di MilanoMilanItaly
  3. 3.Dipartimento ABCPolitecnico di MilanoMilanItaly
  4. 4.Dipartimento ABCPolitecnico di MilanoMilanItaly
  5. 5.Dipartimento ABCPolitecnico di MilanoMilanItaly
  6. 6.Dipartimento ABCPolitecnico di MilanoMilanItaly

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