From SPO to SHM: Experimental validation of optimized sensor networks using Digital Image Correlation

Structural Health Monitoring (SHM) ensures the safety of aeronautical composite structures, but Sensor Placement Optimization (SPO) remains a major bottleneck. Furthermore, experimental validation of numerical SPO layouts and their effectiveness for subsequent damage diagnosis is largely missing in the literature. This paper addresses this gap by experimentally validating an SPO framework on an AS-350 helicopter main rotor blade. The approach utilizes the Multi-Objective Lichtenberg Algorithm (MOLA) coupled with Ordinary Kriging to simultaneously minimize sensor count and reconstruction error while maximizing six classical metrics. Compromise layouts are extracted via the TOPSIS method and confronted with a full-field stereoscopic Digital Image Correlation (DIC) modal test on the physical blade in both intact and damaged states. To evaluate the diagnosis capability, four vibration-based damage indicators are compared. Numerically, the optimized layouts reduced the required sensors by over 96% (13 to 32 sensors) with Kriging errors below 2.6%. Experimentally, the sparse layouts successfully reconstructed the measured mode shapes with mean relative errors within the 5%–15% band usually accepted for modal validation (IE and ADPR metrics). For damage detection, baseline indicators were masked by global deformation and noise, whereas baseline-free indicators (DMSC and CWT) consistently identified an anomaly, albeit systematically offset towards the region of maximum intrinsic modal curvature. The framework is corroborated by comprehensive supporting studies confirming its noise robustness, invariant ranking, and the optimizer’s superiority over NSGA-II and MOEA/D.