Abstract
We consider the problem of supporting decision makers in construction projects for making the rational-best choice between the introduction of advanced technological solutions and the use of conventional approaches. The complexity, uniqueness, and uncertainty, typical of construction projects, are the main obstacles in this kind of decision problems. We propose a method for the development of decision support systems for such problems based on Bayesian decision theory. Bayesian decision theory is applied to capture and rationally solve such decision problems. We account for uncertainty and construction process specific criteria, for structuring the knowledge-base and easing the elicitation of the decision network. The applicability potential of the proposed approach is presented by means of an example application scenario.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Rauch, E., Matt, D.T., Brown, C.A., et al.: Transfer of industry 4.0 to small and medium sized enterprises. In: Transdisciplinary Engineering Methods for Social Innovation of Industry 4.0, vol. 7, pp. 63–71 (2018). https://doi.org/10.3233/978-1-61499-898-3-63
Oesterreich, T.D., Teuteberg, F.: Understanding the implications of digitisation and automation in the context of Industry 4.0: a triangulation approach and elements of a research agenda for the construction industry. Comput. Ind. 83, 121–139 (2016). https://doi.org/10.1016/j.compind.2016.09.006
European Commission Internal Market, Industry, Entrepreneurship and SMEs. In: Construction. https://ec.europa.eu/growth/sectors/construction_en. Accessed 4 May 2019
Mohan, S.: Expert systems applications in construction management and engineering. J. Constr. Eng. Manag. 116, 87–99 (1990). https://doi.org/10.1061/(ASCE)0733-9364(1990)116:1(87)
Hastak, M.: Advanced automation or conventional construction process? Autom. Constr. 7, 299–314 (1998). https://doi.org/10.1016/S0926-5805(98)00047-8
Jahr, K., Borrmann, A.: Semi-automated site equipment selection and configuration through formal knowledge representation and inference. Adv. Eng. Inform. 38, 488–500 (2018). https://doi.org/10.1016/j.aei.2018.08.015
Nowak, P., Książek, M., Draps, M., Zawistowski, J.: Decision making with use of building information modeling. Procedia Eng. 153, 519–526 (2016). https://doi.org/10.1016/j.proeng.2016.08.177
Zhu, J.Y., Deshmukh, A.: Application of Bayesian decision networks to life cycle engineering in Green design and manufacturing. Eng. Appl. Artif. Intell. 16, 91–103 (2003). https://doi.org/10.1016/S0952-1976(03)00057-5
Hammad, A., Akbarnezhad, A.: Modular vs conventional construction: a multi-criteria framework approach. In: ISARC. Proceedings of the 34th International Symposium on Automation and Robotics in Construction. Vilnius Gediminas Technical University, Department of Construction Economics & Property, pp. 214–220 (2017)
Carbonari, A., Corneli, A., Di Giuda, G., et al.: BIM-based decision support system for the management of large building stocks. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction. IAARC Publications, pp. 348–355 (2018)
Russell, S.J., Norvig, P., Davis, E.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson Education Inc., Upper Saddle River (2016)
Kjærulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, 2nd edn. Springer, New York (2013). https://doi.org/10.1007/978-0-387-74101-7
Gonzales, C., Torti, L., Wuillemin, P.-H.: aGrUM: a graphical universal model framework. In: Benferhat, S., Tabia, K., Ali, M. (eds.) IEA/AIE 2017. LNCS (LNAI), vol. 10351, pp. 171–177. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60045-1_20
Acknowledgements
The authors gratefully acknowledge the domain experts Robert Ploner, project manager of Metall Ritten S.r.l., and Alexander Alber, project manager of the building development department of NOI S.p.a..
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Marcher, C., Giusti, A., Schimanski, C.P., Matt, D.T. (2019). Application of Decision Support Systems for Advanced Equipment Selection in Construction. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2019. Lecture Notes in Computer Science(), vol 11792. Springer, Cham. https://doi.org/10.1007/978-3-030-30949-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-30949-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30948-0
Online ISBN: 978-3-030-30949-7
eBook Packages: Computer ScienceComputer Science (R0)