Integrated genetic search for identification of material properties and dynamic excitations

Abstract

In the last decade, genetic algorithms (GAs) have been widely used as search techniques. Their success is attributed to their little mathematical requirements about the problems and high effectiveness at performing global search. Recent research has shown that genetic programming (GP) is powerful in various problems solving, as well as having certain advantages over GAs. To improve the effectiveness of GP, three general search techniques, namely the inGAP, the GP+LBS and the GP+NLP, which integrate the merits of different local search operators (LSOs) (i.e., the GA, the linear bisection search (LBS) and the nonlinear programming (NLP)) and GP, are developed. The GA based method is proposed to identify the rock dynamic properties and aluminum foam dynamic properties, respectively. The GP and the proposed inGAP/GP+LBS/GP+NLP based methods are proposed to solve the force and ground motion identification problems, respectively.One obvious merit of the proposed GP and inGAP/GP+LBS/GP+NLP based methods is that they can obtain the explicit expression of the unknown force/ground motion. Another advantage is that they only need the dynamic response data at one point, i.e., displacement or velocity or acceleration of one degree-of-freedom.