An AI-based DSS for preventive conservation of museum collections in historic buildings

Abstract The study deals with the experimentation of innovative and advanced preservation methodologies for museum collections in historical buildings. In particular, this work shows the development of an algorithm, based on Machine Learning techniques, that suggests which actions to undertake in relation to thermo-hygrometric conditions. The aim is to guarantee microclimate conditions that are favorable both for collections and architecture. In order to boost the time required to gather the needed dataset a possibility is to train the decision-making model based on machine learning through the use of a synthetic dataset created via plugins connected to VPL (Visual Programming Languages) that allow the simulation of different scenarios starting from open source data. The training of the machine learning mechanism will be supervised and each solution will be “an experience” for the building from which to learn. Along the time thanks to experience and background data the decision-making system will improve the quality of his work.

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