Optimum Sensor Placement Considering Modeling Uncertainties: Application on a Laboratory Benchmark Structure

Abstract

Optimal Sensor Placement (OSP) is a critical component of vibration-based Structural Health Monitoring (SHM), yet its effectiveness is often compromised by the deterministic nature of standard Finite Element (FE) models. This study evaluates two advanced OSP frameworks that explicitly account for epistemic and aleatory uncertainties: a variance-based method that utilizes hierarchical clustering to manage modal sensitivity variance, and a likelihood-maximization method designed to maximize the probability of achieving specific SHM objectives under measurement noise. Using a laboratory-tested glulam timber beam as a benchmark, the research investigates the impact of uncertain material properties and support stiffness on sensor performance. Experimental results from ambient and impact vibration tests serve as the ground truth for validation. The findings reveal that boundary condition uncertainties can lead to significant modal discrepancies, especially for higher modes. Both probabilistic methods identified robust sensor configurations that significantly outperformed random layouts, with the likelihood-maximization method achieving a 65.9% success rate for high-fidelity mode shape reconstruction (MAC ≥ 0.95). By bridging the gap between theoretical optimization and practical structural variability, these frameworks provide a reliable methodology for designing SHM systems in complex, real-world infrastructure.