A Method to Identify Damage of Roof Truss under Static Load Using Genetic Algorithm

Abstract

In recent years, computational intelligence methods are widely used to solve problems in engineering structural field by more and more researchers. In this paper, a method based on genetic algorithm (GA) for identifying the damage in a roof truss under static loads has been developed. At first, the forward analysis based on the finite element model method clearly demonstrates that the damage of elements on a roof truss can result in a change of static axial strain. Then GA has been used to identify the location and the degree of structural damage. In this paper, damage in the structure is modeled as a reduction in the cross-sectional of the damaged element. The identification problem is formulated as a constrained optimization problem, which is solved using GA. Unlike the traditional mathematical methods, which guide the direction of hill climbing by the derivatives of objective functions, GA searches the problem domain by the objective functions itself at multiple points. The objective function is defined as the difference between the measured static strains and the analytically predicted strains obtained from a finite element model. By minimizing the objective function, the damage location and damage severity can be successfully identified. The static-based method uses only strain measurements at a few degrees of freedom as input to the identification procedure and no additional excitation is required. These features make the method ideally suited for long-term monitoring of large civil structures. The method proposed in this paper is demonstrated using a plane roof truss model, and the results fit well with the actual value.