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.

用于结构健康监测（SHM）的基于模型的损伤识别仍然是文献中的一个悬而未决的问题。除了与全尺寸结构建模相关的计算挑战之外，经典的单模型结构识别（St-Id）方法无法保证逆校准结果的物理意义。有鉴于此，这项工作引入了一种模型驱动损伤识别的新颖方法，该方法基于由一组竞争结构模型形成的多类数字模型，每个模型代表不同的失效机制。前向模型被计算高效的元模型取代，并使用监测数据不断校准。如果检测到结构性能异常，则会使用基于贝叶斯信息准则 (BIC) 的模型选择方法来识别最可能激活的失效机制。通过两个案例研究说明了所提出方法的潜力，包括数值平面桁架和现实世界的历史建筑：阿尔罕布拉宫的穆罕默德塔。