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We propose a simulation-based decision strategy for the proactive maintenance of complex structures with a particular application to structural health monitoring (SHM). The strategy is based on a data-driven approach which exploits an offine-online decomposition. A synthetic dataset is constructed offine by solving a parametric time-dependent partial differential equation for multiple input parameters, sampled from their probability distributions of natural variation. The collected time-signals, extracted at sensor locations, are used to train classiffiers at such sensor locations, thus constructing multiple databases of healthy configurations. These datasets are then used to train one class Support Vector Machines (OC-SVMs) to detect anomalies. During the online stage, a new measurement, possibly obtained from a damaged configuration, is evaluated using the classiffiers. Information on damage is provided in a hierarchical manner: first, using a binary feedback, the entire structure response is either classifiied as inlier (healthy) or outlier (damaged). Then, for the outliers, we exploit the outputs of multiple classiffiers to retrieve information both on the severity and the spatial location of the damages. Because of the large number of signals needed to construct the datasets offline, a model order reduction strategy is implemented to reduce the computational burden. We apply this strategy to both 2D and 3D problems to mimic the vibrational behavior of complex structures under the effect of an active source and show the effectiveness of the approach for detecting and localizing cracks.