Sparse Sensor Array Based Damage Monitoring and Assessment for Large-scale Structures

In order to solve the problem that the wide range of damage monitoring for large-scale structures, a triangular sparse array-based damage monitoring is researched and validated in this paper. The typical Lamb wave structural responses is analyzed firstly. Multi characteristic parameters is extracted from the responses based on the varies of the response due to the damage. Using these multi characteristic parameters, the damage location in large-scale structure can be monitored by the damage identification model which is designed by using SVM (Support Vector Machine). Experimental validation shown that the multi characteristic parameters from triangular sparse array layout can be used to identify the specific orientation of damage in relative large-scale structure, and the trained SVM based damage identification model can also reveal the damage location. Therefore, the existing large-scale structural damage monitoring method is improved.