Towards a comprehensive damage identification of structures through populations of competing models Hernández González, Israel Alejandro García Macías, Enrique Damage identification Digital twins Model selection Model-based damage identification for structural health monitoring (SHM) remains an open issue in the literature. Along with the computational challenges related to the modeling of full-scale structures, classical single-model structural identification (St-Id) approaches provide no means to guarantee the physical meaningfulness of the inverse calibration results. In this light, this work introduces a novel methodology for model-driven damage identification based on multi-class digital models formed by a population of competing structural models, each representing a different failure mechanism. The forward models are replaced by computationally efficient meta-models, and continuously calibrated using monitoring data. If an anomaly in the structural performance is detected, a model selection approach based on the Bayesian information criterion (BIC) is used to identify the most plausibly activated failure mechanism. The potential of the proposed approach is illustrated through two case studies, including a numerical planar truss and a real-world historical construction: the Muhammad Tower in the Alhambra fortress. 2024-06-10T10:39:24Z 2024-06-10T10:39:24Z 2024-04-06 journal article Hernández-González, I.A., García-Macías, E. Towards a comprehensive damage identification of structures through populations of competing models. Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-01972-6 https://hdl.handle.net/10481/92459 10.1007/s00366-024-01972-6 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature