Damage Identification Using Artificial Neural Network-Aided Aimed Multilevel Sampling Method

Abstract

Structural health monitoring is extremely important for sustaining and preserving the service life of civil structures. Research to identify the dam- age can detect, locate, quantify and, where appropri- ate, predict potential structural damage. This paper is about damage identified by non-destructive vibration- based experiments, which uses the difference between modal frequencies and deflection of an initial and dam- aged structure. The main objective of this paper is to present a hybrid method for structural damage identi- fication combining artificial neural network and aimed multilevel sampling method. The combination of these approaches yields a more efficient damage identifica- tion in terms of time and accuracy of damage localiza- tion and damage extent determination.