LAUSR

Abstract Building Information Modelling, new paradigm of digital design and management, shows great potential for the refurbishment process, as it represents a possible way out of criticalities that occur in documentation and preservation of existing assets, if connected to cognitive automation. The combination of BIM with automation systems improves the quality control during diagnosis, design and work execution, and the labour savings, which is particularly relevant for rapid intervention in case of hazardous conditions. Therefore, the paper is going to address a methodological discussion concerning complete “as-built” parametric models of historical buildings, supporting the design of refurbishment and conservation interventions. Although some reviews of the state of the art exist on the topic of Historic Building Information Modelling, the present research introduces a different perspective on HBIM modelling, with diagnosis and performance assessment as key-aspects, in terms of automating performance assessment. Specifically, from the data collection of contributions regarding HBIM/BIM, diagnostics and monitoring on existing buildings and infrastructures, a critical review by selected criteria is developed. Nevertheless, general methods and tools for information management and exchange tasks in BIM are briefly described as well, since they are considered useful for future developments of HBIM approach. The core of the critical analysis is focused on the scientific and technical relations among HBIM models, diagnosis and performance assessment features. In addition, the review identifies specific activities and relative tools and methods for knowledge acquisition and semantic enrichment. Finally, gaps in knowledge of the current literature are outlined and discussed, with specific focus on performance assessment in HBIM. In this regard, a new methodology toward Diagnosis-Aided Historic Building Information Modelling and Management (DA-HBIMM) is proposed as a framework to be developed in order to address smart knowledge acquisition, collection and notification of assessed performances and eventual risks, by cognitive automation and artificial intelligence, in the near future.