Building condition indicators analysis for BIM-FM integration

Abstract

This paper aims to review and reflect upon the built up scientific knowledge on Building Condition Assessment (BCA) using Key Performance Indicators (KPIs), supported by Building Information Modelling (BIM), to implement appropriate maintenance and rehabilitation activities. For this purpose, a literature review related to KPIs applied to BCA and using BIM to BCA has been performed. KPIs applied to BCA were studied and their calibration and validation methods were identified, as well as their potential to support BIM in BCA. Furthermore, current researches in the field of Artificial Intelligence (AI) and Machine Learning (ML) applied to the optimization of BCA were also presented. This work concludes that despite the studies that have been conducted, only a few focuses on KPIs integration in BIM due to some limitations that still exist. These limitations are related to the application of BIM on building inspections and the limitation of KPIs scope and interoperability with BIM. Considering the identified gaps in the most relevant research trends on the subject, the present study discusses the viability of adjustments on KPIs based on reliable validation and calibration methods and combine them to ML algorithms in order to develop a more robust BIM-BCA strategy, thereby filling in the above-mentioned limitations.