Identification of delaminations in composite: structural health monitoring software based on spectral estimation and hierarchical genetic algorithm

Abstract

A hierarchical Genetic Algorithm (GA) is implemented in a high peformance spectral finite element software for identification of delaminations in laminated composite beams. In smart structural health monitoring, the number of delaminations (or any other modes of damage) as well as their locations and sizes are no way completely known. Only known are the healthy structural configuration (mass, stiffness and damping matrices updated from previous phases of monitoring), sensor measurements and some information about the load environment. To handle such enormous complexity, a hierarchical GA is used to represent heterogeneous population consisting of damaged structures with different number of delaminations and their evolution process to identify the correct damage configuration in the structures under monitoring. We consider this similarity with the evolution process in heterogeneous population of species in nature to develop an automated procedure to decide on what possible damaged configuration might have produced the deviation in the measured signals. Computational efficiency of the identification task is demonstrated by considering a single delamination. The behavior of fitness function in GA, which is an important factor for fast convergence, is studied for single and multiple delaminations. Several advantages of the approach in terms of computational cost is discussed. Beside tackling different other types of damage configurations, further scope of research for development of hybrid soft-computing modules are highlighted.