The long-term preservation of heritage structures relies on effective Structural Health Monitoring (SHM) systems, where sensor placement is key to ensuring early damage detection and guiding conservation efforts. Optimal Sensor Placement (OSP) methods offer a systematic framework to identify efficient sensor configurations, yet their application in historical buildings remains limited. Typically, OSP is driven by numerical models; however, in the context of heritage structures, these models are often affected by substantial uncertainties due to irregular geometries, heterogeneous materials, and unknown boundary conditions. In this scenario, data-driven approaches become particularly attractive as they eliminate the need for potentially unreliable models by relying directly on experimentally identified dynamic properties. This study investigates how the choice of input data influences OSP outcomes, using the Church of Santa Ana in Seville, Spain, as a representative case. Three data sources are considered: an uncalibrated numerical model, a calibrated model, and a data-driven set of modal parameters. Several OSP methods are implemented and systematically compared. The results underscore the decisive impact of the input data on the optimisation process. Although calibrated models may improve certain modal parameters, they do not necessarily translate into better sensor configurations. This highlights the potential of data-driven strategies to enhance the robustness and applicability of SHM systems in the complex and uncertain context of heritage buildings.
Keywords: Optimal Sensor Placement (OSP); Structural Health Monitoring (SHM); data-driven methods; dynamic identification; experimental modal analysis (OMA); heritage buildings; heuristic methods; historical structures; model uncertainty; sensor optimisation.