Abstract
Abstract. This paper investigates the application of the Digital Twin approach to get a Sentient building able to acquire the ability to perceive external inputs and develop strategies to support its management and/or conservation. The experimentation foresees the integration of an H-BIM model with a Decision Support System based on Artificial Intelligence (in this case Machine Learning techniques) for the management of museum collections in historical architectures. The innovative aspect of this methodology resides in the change of paradigm regarding the relations between the historical building under consideration and the professional figures who deal with the management, conservation and architectural restoration. This work tries to contextualize the novel HS-BIM methodology within the theoretical discussion of the disciplines mentioned above and to participate in Digital Twin’s debate. HS-BIM can be seen as a possible path that leads to creating digital twins for cultural heritage. The reflection inspired by this experience aims to revise the concept of Digital Twin as a parallel/external digital model in favour of an artificial evolution of the real system augmented by a “cognitive” apparatus. In this vision, thanks to AI application, future buildings will be able to sense “comfort and pain” and learning from their own life-cycle experience but also from that one of elder sentient-buildings thanks to transfer learning already applied in AI’s fields.
Highlights
The Anthropocene age is strongly characterized by disruptive changes that are impacting the relationship between humans and technology
The study deals with the experimentation of innovative methodologies that allow advanced management of museum collections, hosted in a historical building, through the development of a DSS (Decision Support System) that uses ML (Machine Learning) to implement effective conservation strategies
The HS-BIM (Historical Sentient - Building Information Modeling) definition proposed in this research work draws on the considerations made on the above mentioned DT maturity levels, focuses on historical buildings preservation actions and is projected towards the level 5 ‘Autonomous operations and maintenance’ (Evans, 2019)/level 4 ‘Intelligent Digital Twin’ (Madni et al, 2019)
Summary
The Anthropocene age is strongly characterized by disruptive changes that are impacting the relationship between humans and technology. Among all the buildings that fall under heritage preservation, there is a specific typology to which this research is mainly addressed: museums hosted in historical architectures In this case, the issue of heritage preservation has to consider both the historical values of the building - the container - and the peculiarities of the collections - the content. The study deals with the experimentation of innovative methodologies that allow advanced management of museum collections, hosted in a historical building, through the development of a DSS (Decision Support System) that uses ML (Machine Learning) to implement effective conservation strategies. The formulation of a novel methodology - namely HS - BIM (Historical Sentient - Building Information System) - for historical building documentation, management, and conservation is proposed As case study, it has been chosen the university museum MuRa In the conclusions some reflections on the adoption of the methodology and further developments will be shown
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