While on the one hand the BIM methodology is an essential reference for the construction of new buildings, on the other hand it is receiving particular attention and interest also from owners of large building stocks who want to take advantage of the benefits of Building Information Modelling so as to have a coordinated system for the sharing of information and data. This, especially in a process that concerns the management and maintenance of a large building stocks, involves the processing of uncertain information in BIM, particularly when dealing with existing buildings, due to the lack of and/or incomplete documentation, entailing a significant investment in terms of time and additional costs. Therefore, to represent the reliability of existing building data, we suggest introducing a tool based on Bayesian Network that offers a valid decision support under conditions of uncertainty and is used to evaluate the compliance with the latest standard. This paper presents a process to provide an integrated database defined by a minimum information level that can be used both to extrapolate and query specific information from a digital building model and populate the decision model in order to evaluate the performance parameters of existing buildings which is based on a Multicriteria decision making approach (AHP).

BIM-BASED DECISION SUPPORT SYSTEM FOR THE MANAGEMENT OF LARGE BUILDING STOCKS

Carbonari, Alessandro;Di Giuda, Giuseppe Martino;Villa, Valentina
2018-01-01

Abstract

While on the one hand the BIM methodology is an essential reference for the construction of new buildings, on the other hand it is receiving particular attention and interest also from owners of large building stocks who want to take advantage of the benefits of Building Information Modelling so as to have a coordinated system for the sharing of information and data. This, especially in a process that concerns the management and maintenance of a large building stocks, involves the processing of uncertain information in BIM, particularly when dealing with existing buildings, due to the lack of and/or incomplete documentation, entailing a significant investment in terms of time and additional costs. Therefore, to represent the reliability of existing building data, we suggest introducing a tool based on Bayesian Network that offers a valid decision support under conditions of uncertainty and is used to evaluate the compliance with the latest standard. This paper presents a process to provide an integrated database defined by a minimum information level that can be used both to extrapolate and query specific information from a digital building model and populate the decision model in order to evaluate the performance parameters of existing buildings which is based on a Multicriteria decision making approach (AHP).
35th International Symposium on Automation and Robotics in Construction and Mining (ISARC 2018) and the International AEC/FM Hackathon
978-1-5108-6902-8
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1090688
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