Structural Damage Detection through PSO and Evolutionary
Algorithms
Minjoong Jeong*, Bong-Hwan Koh**
*Advanced Research Team, Supercomputing Center, KISTI, Daejeon, KOREA
**Dept. of Mechanical Engineering, Dongguk Univ., Seoul, KOREA
This study investigates several intelligence algorithms to tackle a traditional damage detection
problem having stiffness degradation or damage in mechanical structures. Particle swarm optimization
(PSO) and evolutionary algorithms (EAs) have been exploited for localizing and estimating the location
and extent damages in a structure. PSO and EAs are population-based, stochastic algorithms that have
been developed from the underlying concept of swarm intelligence and search heuristic. A finite element
(FE) model updating is implemented to minimize the difference in a set of natural frequencies between
measured and baseline vibration data. Stiffness loss of certain elements is considered to simulate
structural damages in the FE model. It is numerically shown that PSO and evolutionary algorithms
successfully completed the optimization process of model updating in locating unknown damages in a
mechanical structure.

Corresponding Author: Minjoong Jeong
Address: 335 Gwahakro, Yuseong-Gu, Daejeon, 305-806, Republic of Korea
Tel: +82-42-869-0632 / Fax: +82-42-869-0599 / E-mail: jeong@kisti.re.kr