Cultural Intelligence-Investigation of Different Systems for Heritage Sustainable Preservation

Abstract

Cultural heritage protection is a multidisciplinary subject. An intelligent decision-making mechanism, combined with multi-criteria assessment, is required to lead to compatible and sustainable decision making concerning conservation works. Decision making is a complex process that takes into account a wide range of parameters, from qualitative (such as the historical or cultural value of the building) to quantified data (such as the properties of its materials) and involves the following tasks: monitoring, inspection, diagnosis, intervention study, interventions, and evaluation of interventions. It should be based on specific specifications, criteria, and methodology to ensure the sustainability of the construction and require the availability of data of a different nature and of high quality. In this work, different artificial intelligent systems are investigated and tested—UTASTAR methodology based on linear regression, unsupervised non-linear classifiers (feed-forward neural networks), and clustering methodologies (fuzzy c-means algorithm)—in order to develop a decision.