Probabilistic approach in assessing structural damage using multistage artificial neural network using static and dynamic data

• Main Authors: Bakhary, Norhisham, Abdul Rahman, Azlan, Ahmad, Baderul Hisham, Goh, Lyn Dee
• Format: Article
• Published: KU Leuven 2014
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/id/eprint/62335/; http://dx.doi.org/10.1260/1369-4332.13.1.95

This paper addresses a probabilistic approach with consideration of uncertainties using a multistage artificial neural network (ANN) in vibration-based damage detection. Because obtaining complete measurement is a difficult task due to practical limitations, this paper also deals with a limited number of measurements for damage detection by employing a multistage ANN to predict damage severity and location. The multistage ANN consists of a two-stage ANN model. The first-stage ANN is to predict the unmeasured structural responses based on the measured structural responses at the limited point measurements while the second stage is for damage detection. The robustness of the proposed method is demonstrated using the experimental static and dynamic data as the input parameters for the multistage ANN. The results show that the proposed method is capable of considering random errors, thus providing a reliable method for damage detection.