Historical Sentient – Building Information Model: A Digital Twin for the Management of Museum Collections in Historical Architectures

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.

Can we benefit from game engines to develop digital twins for planning the deployment of photovoltaics?

“…Artificial Intelligence (AI) is commonly used to construct DTs and is used for predicting, detecting, making decisions, planning and diagnosing. The most common AI methods include machine learning (Vrabič et al 2021;D' Amico et al 2019;La Russa and Santagati 2020;Darvishi et al 2021;Demirel et al 2021;Greis et al 2021;Zohdi 2020;Hafez 2020;Min et al 2019), neural networks (Vrabič et al 2021;Mourtzis et al 2021;Wu and Li 2021) and genetic algorithms (Viola and Chen 2020;Negri et al 2019Negri et al , 2021Zohdi 2021a). Software engineering methods are mainly applied to specify functional requirements and document the DT's structure and behaviour.…”

Towards digital twins for heritage buildings: A workflow proposal

“…[4][5][6]38 A third group argues that the role of a DT is to monitor, analyze, and influence how the real building functions, by receiving and processing data in real time to make the construction responsive and sentient. [39][40][41] None of the reviewed papers discuss feedback from the analyses performed in the DT to a BIM software. Thus, in the development of DTs, the HBIM models are used as repositories for geometric and semantic information.…”

“…44 Hence, it is necessary to develop simpler workflows, with greater automation, that allow a model to be incrementally refined, to improve its accuracy, as more geometric and semantic information becomes available. 37,39,44 The more established workflows in the field of HBIM consist of using point cloud data from surveys as scaffolding on which the BIM model is mounted. These often neglect parameterization in favor of greater geometric precision.…”

From Architectural Survey to Continuous Monitoring: Graph-Based Data Management for Cultural Heritage Conservation With Digital Twins

“…The term is currently very popular and has also attracted growing attention in the various disciplinary fields. For example, in the architectural field the concept of DT is innovative and closely linked to the HBIM model (Jouan and Hallo, 2020;La Russa et al, 2020) or to augmented reality (Dezen-Kempter et al, 2020). The purpose of this methodology aims to obtain a virtual copy of the object to develop conservation and maintenance strategies with a constant update of the data but still today there are several difficulties.…”