A BIM-Based Decision Support System for Building Maintenance

Available data about asset condition and performances can be conveyed into different Key Performance Indicators (KPIs). Many KPIs measuring technical, functional and economic/financial asset performances can be found in literature. Nevertheless, they are often strictly related to a specific scope, thus they provide an incomplete depiction of the whole assets performances. The objective of this research is to provide facility managers and asset owners with an easy instrument to prioritize maintenance. In order to reduce costs related to its use, the instrument, developed in the form of a Decision Support System (DSS), is based on existing and reliable performances metrics and leverages new technologies like Building Information Modelling (BIM). Accordingly, the Facility Condition Index (FCI) is combined with the D index, a KPI related to the age of building components, developed by the authors. The joint use of the FCI and the D index, allows facility managers to make more conscious decisions. The proposed DSS helps in the definition of the best maintenance plan, providing a ranking of building components which require more urgent maintenance interventions. Although the DSS should be tested measuring its ability to preserve buildings and their performances on a long term, the first results are positive, as confirmed by the application to a case study on an office building in Italy. Moreover, the usability of the instrument has been appreciated by the users in a medium size Italian company.

[1]  David J. Edwards,et al.  The building information modelling trajectory in facilities management: A review , 2017 .

[2]  Tim Baines,et al.  The servitization of manufacturing: A review of literature and reflection on future challenges , 2009 .

[3]  Edmundas Kazimieras Zavadskas,et al.  Multivariant design and multiple criteria analysis of building refurbishments , 2005 .

[4]  Solomon Tesfamariam,et al.  Decision Models To Prioritize Maintenance And Renewal Alternatives , 2006 .

[5]  F. Cecconi,et al.  Dynamic Facility Condition Index calculation for asset management , 2017 .

[6]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[7]  Heng Li,et al.  Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of intelligent building systems , 2008 .

[8]  L. Roberts Measuring school facility conditions: an illustration of the importance of purpose , 2009 .

[9]  Charles M. Eastman,et al.  BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors , 2008 .

[10]  Mohamed Marzouk,et al.  Establishing Multi-level Performance Condition Indices for Public Schools Maintenance Program Using AHP and Fuzzy Logic , 2016 .

[11]  D Caccavelli,et al.  TOBUS — a European diagnosis and decision-making tool for office building upgrading , 2002 .

[12]  R. W. Saaty,et al.  The analytic hierarchy process—what it is and how it is used , 1987 .

[13]  Fulvio Re Cecconi,et al.  Key Performance Indicators for Building Condition Assessment , 2017 .

[14]  Dariusz Walasek,et al.  Analysis of the Adoption Rate of Building Information Modeling [BIM] and its Return on Investment [ROI] , 2017 .

[15]  Ahmad Jrade,et al.  Integrating Decision Support System (DSS) and Building Information Modeling (BIM) to Optimize the Selection of Sustainable Building Components , 2015, J. Inf. Technol. Constr..

[16]  Christoph Merschbrock,et al.  A Review of Building Information Modelling for Construction in Developing Countries , 2016 .

[17]  Sarel Lavy,et al.  KPIs for facility's performance assessment, Part II: identification of variables and deriving expressions for core indicators , 2014 .

[18]  Qian Wang,et al.  BIM-based framework for automatic scheduling of facility maintenance work orders , 2018, Automation in Construction.

[19]  Qinghua Zhu,et al.  Using a Delphi method and the Analytic Hierarchy Process to evaluate the search engines: A case study on Chinese search engines , 2011, Online Inf. Rev..