The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 755–762, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-755-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 755–762, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-755-2020

  25 Aug 2020

25 Aug 2020

HISTORICAL SENTIENT – BUILDING INFORMATION MODEL: A DIGITAL TWIN FOR THE MANAGEMENT OF MUSEUM COLLECTIONS IN HISTORICAL ARCHITECTURES

F. M. La Russa and C. Santagati F. M. La Russa and C. Santagati
  • Department of Civil Engineering and Architecture (DICAR), Università degli Studi di Catania, Via Santa Sofia n. 64, 95125 Catania, Italy

Keywords: Digital Twin, Historical Architecture, Artificial Intelligence, Decision Support System, Architectural Survey, Preventive conservation

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.