Optimal sensor placement methodology for parametric identification of structural systems

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Co‐evolutionary algorithm for structural damage identification using minimal physical testing (2007, methodology): “Genetic algorithms have been successfully used for error minimization in various damage identification problems [2, 4–8] and have gained wide acceptance due to their inherent advantages such as convergence to global optima, noise tolerance, handling of multimodal problems (non-unique solutions), and”
Optimal sensor placement in structural health monitoring using discrete optimization (2015, methodology): “Papadimitriou [16, 17] applied the information entropy (IE) to measure the performance of a sensor configuration in OSP.”
A modified monkey algorithm for optimal sensor placement in structural health monitoring (2012, unknown): “Case (3): the existing FSSP algorithm [13, 15].”
Optimization of sensor placement for structural health monitoring: a review (2019, methodology): “In case the sensors are sequentially removed, the SP algorithms are termed as BSSP, while in case they are added, they are termed as FSSP.(41) Several initial works in the area of OSP were based on the SP algorithms.”
Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering (2011, methodology): “Optimal sensor location strategies [33] may be advantageously used to obtain the most informative locations that give the best accuracy in the fatigue lifetime predictions with the least number of sensors.”