Application of Improved Accelerated Random Search Algorithm for Structural Damage Detection

Abstract

Finite element (FE) model updating technique belongs to the class of inverse problems in classical mechanics. According to the continuum damage mechanics, damage is represented by a reduction factor of the element stiffness and mass. The objective of the optimization problem is to minimize the difference between measured and numerical FE vibration data. In this study a new method is presented for structural damage detection called Improved Modified Accelerated Random Search algorithm (IMARS). The algorithm uses model updating procedure to detect damages in a decoupled fashion. First, detecting the location and the number of damaged elements is evaluated by multi-run process. Knowing damage number, the quantification step is then applied using simple computing procedure. The effectiveness of the algorithm is first tested on mathematical benchmark functions. The algorithm is then applied in damage detection of 2D beam structure and 2D truss structure. These two cases have different boundary conditions and different damage scenarios. The simulated experimental modal data have been taken as reference values. A real cantilever beam with experimental modal parameters is used to validate the proposed method for real single and double damages. Results show that the proposed method is accurate and robust in structural damage identification.

Crack Detection through the Change in the Normalized Frequency Shape

“…In this area, there are the bee algorithm [25], the modified Cuckoo optimization algorithm [26], artificial neural networks [27], and genetic algorithms [28]. Recently, Boubakir et al [29] improved the accelerated random search algorithm of (Touat et al [30]) and applied it for the detection of damage in the beams. However, the disadvantage of these methods is in the search for the position and the depth at the same time.…”