PSI - Issue 44

ScienceDirect Structural Integrity Procedia 00 (2022) 000–000 Structural Integrity Procedia 00 (2022) 000–000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia Structural Integrity 44 (2023) 2082–2089

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. Abstract Recent earthquakes have demonstrated that monumental structures located in regions characterized by high seismic hazard are particularly sensitive to damage, stimulating a growing attention to the formulation of cost-effective and long-lasting methods for damage assessment. Generally, the evaluation of a healthy or damaged state is data-driven and it can be subjected to a large amount of uncertainty. In order to associate a damage symptom to an actual structural damage, including all the uncertainties involved in the process, a Bayesian-based data fusion methodology is proposed. To this purpose, different sources of information are combined, such as dynamic structural properties extracted from monitoring data (natural frequencies and mode shapes), static response data (crack amplitudes) and visual inspections. More in depth, the proposed procedure comprises three fundamental steps: i) calibration of a finite element (FE) model, partitioned in well-thought-out macro-elements on the basis of engineering judgments and/or numerical simulations and, subsequently, construction of a tuned surrogate model (SM) considering pre-selected uncertain parameters as inputs, such as the Young's modulus, shear modulus, Poisson's ratio and mass density associated to each macro element; ii) solve the Bayesian-based inverse problem aimed at deriving the posterior statistics of the uncertain parameters over the space of the surrogate model’s classes in a computational effective manner by using dynamic data; iii) adjust the posterior distribution on the basis of the information obtained from static data and visual inspections, i.e., data fusion. The suitability of the proposed approach is demonstrated by using the monitoring data pertaining to a monumental palace, located in Gubbio (Italy) and named Consoli Palace, which has been monitored by the Authors since 2017. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy Keywords: Bayesian Data fusion, Damage detection, Surrogate model, Structural Health Monitoring, Architectural heritage. XIX ANIDIS Conference, Seismic Engineering in Italy A Bayesian-based data fusion methodology and its application for seismic structural health monitoring of the Consoli Palace in Gubbio, Italy Laura Ierimonti a* , Ilaria Venanzi a , Nicola Cavalagli a , Enrique García-Macías a,b , Filippo Ubertini a Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti, 93 06125 Perugia (PG) Italy b Department of Structura Mechanics and Hydraul c Engineering, University of Gran da, Av. Fuentenueva sn, 18002 Granada, Spain Abstract Recent earthquakes have demonstrated that monumental structures located in regions characterized by high seismic hazard are particularly sensitive to damage, imula ing a growing ttention to the formulation f cost-effect v and long-lasting metho s for d mage asse sment. Generally, the eva uation of a healthy or damaged state is data-driven and it an be subjected to a large amount of unc rtainty. In order to ssociate a damage symptom to an ctual structural damage, including all the un ertainties involved in the process, a Bayesian-based dat fusion methodology is proposed. To this purpose, different sources of informat on are combined, such as dyn mic structur l propertie extrac ed from monit ring ata (natural frequencies and mode shapes), s atic resp nse data (crack amplitudes) and visual inspections. More in depth, the proposed procedur comprises three fundamental steps: i) calibration of finite elem nt (FE) model, partitioned in well-thought-out macro-el ments n the basis o e gin ering judgments and/ r numerical simulations and, subsequently, co struction of a tuned surrogat m del (SM) consider g pre-selected uncertain parameters as inputs, such as the You g's m dul s, shear mod lus, Poisson's ratio and mass density associated to ach macro elemen ; ii) solve the Bayesian-based i verse problem aimed at deriving the pos erior statistics of the uncert in parameters over th space of the surrog te model’ classes in a comput tional eff ctive manner by using dynamic data; iii) djust the po terio distribution on the basis of the inform tion obt ined from static data and visual inspections, i.e., data fusion. The suitability of the proposed approach is demonstrated by usi g the monit ring data pert ining to a monume tal palace, located in Gubbio (Italy) and named Consoli Palace, which h s been monitor d by he Authors since 2017. © 2022 The Authors. Publ s ed by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review u der re ponsibility of scientific committe of the XIX ANIDIS C nfere ce, Seismic Engineering in Italy K ywords: Bayesian Data fu ion, Damage detection, Surrogate model, Structural Health Monitoring, Architectural heritag . XIX ANIDIS Conference, Seismic Engineering in Italy A Bayesian-based data fusion methodology and its application for seismic structural health monitoring of the Consoli Palace in Gubbio, Italy Laura Ierimonti a* , Ilaria Venanzi a , Nicola Cavalagli a , Enrique García-Macías a,b , Filippo Ubertini a a Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti, 93 06125 Perugia (PG) Italy b Department of Structural Mechanics and Hydraulic Engineering, University of Granada, Av. Fuentenueva sn, 18002 Granada, Spain

2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy 2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the sci ntific committee of the XIX ANIDIS Conference, Seismic Engin ering in Italy

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. 10.1016/j.prostr.2023.01.266