Artificial intelligence applied to the preventive conservation of heritage buildings

Architectural heritage is an important economic and cultural capital of European countries. A monument is more than just the construction itself, being part of the local identity and a source of memory of historical events. The concept of conservation of cultural heritage has evolved over the recent decades at the global level, in order to define multidisciplinary approaches to intervention in buildings, thus leading to their maximum preservation. Assessing the degradation condition of buildings over time is an essential issue to establish the necessary repairs and rehabilitation actions. In this sense, the maintenance of architectural heritage buildings requires methods, strategies and efficient plans. Within this context, and considering the investment required for the maintenance and repair of the built heritage, it is essential to define efficient tools that may be useful to planning an appropriate maintenance strategy for this kind of historical constructions. The aim of this paper is to present an innovative first approach of a new computerised tool (Art-Risk 3.0) for preventive conservation of heritage in urban centres based on models of artificial intelligence. This methodology is able to manage vulnerability, risks and functional service life of buildings in order to contribute to the preservation of built cultural heritage, helping owners, local, regional and national administrations to make decisions about conservation implemented by scientific criteria. The novelty of this challenge lies in its approach and results, a free software to evaluate decisions in regional policies, planning and management of heritage buildings, with a transversal development that includes urban, architectural, cultural heritage value, and the analysis of environmental, natural and socio-demographic situation around the monuments. This new model (Art-Risk 3.0) uses the fuzzy logic theory and the geographic information systems (GIS) for implements the environmental input variables and geological location of buildings located in the peninsular territory of Spain. The information obtained in this study is exceptionally relevant for the researchers and stakeholders responsible for the definition and implementation of maintenance programmes in building stocks. This analysis is extremely important in the implementation of maintenance programs in large building stocks. Art-Risk 3.0 model is one of the main results that will be obtained in the National Research Project (Art-Risk) funded by Ministerio de Economia y Competitividad (Spain) and Fondo Europeo de Desarrollo Regional (FEDER) [code: BIA2015-64878-R (MINECO/FEDER, UE)].