An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings

• Autor(a) principal: Barontini, Alberto
• Data de Publicação: 2017
• Outros Autores: Masciotta, Maria-Giovanna, Ramos, Luís F., Amado-Mendes, Paulo, Lourenço, Paulo B.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/108327; https://doi.org/10.1016/j.proeng.2017.09.439
• topic: Historical building conservation; structural health monitoring; damage identification; optimal sensor placement; nature-inspired algorithm

Structural Health Monitoring (SHM) of historical building is an emerging field of research aimed at the development of strategies for on-line assessment of structural condition and identification of damage in the earliest stage. Built heritage is weak against operational and environmental condition and preservation must guarantee minimum repair and non-intrusiveness. SHM provides a cost-effective management and maintenance allowing prevention and prioritization of the interventions. Recently, in computer science, mimicking nature to address complex problems is becoming more frequent. Nature-inspired approaches turn out to be extremely efficient in facing optimization, commonly used to analyze engineering processes in SHM, providing interesting advantages when compared with classic methods. This paper begins with an introduction to Natural Computing. Then, focusing on its applications to SHM, possible improvements in built heritage conservation are shown and discussed suggesting a general framework for safety assessment and damage identification of existing structures.