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. Structural health monitoring (SHM) is often approached from a statistical pattern recognition or machine learning perspective with the aim of inferring thehealth state of a structure using data derived from a network of sensors placed upon it. In this paper, two SHM sensor placement optimisation (SPO) strategies that offer robustness toenvironmental effects are developed and evaluated. The two strategies both involve constructing an objective function (OF) based upon an established damageclassification technique and an optimisation of sensor locations using a genetic algorithm (GA). The key difference between the two strategies explored hereis in whether any sources of benign variation are deemed to be observable or not. The relative performances of both strategies are demonstrated usingexperimental data gathered from a glider wing tested in an environmental chamber, with the structure tested in different health states across a series ofcontrolled temperatures.

On sensor optimisation for structural health monitoring robust to environmental variations