Journal
JOURNAL OF BUILDING ENGINEERING
Volume 11, Issue -, Pages 230-235Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jobe.2017.04.001
Keywords
Seismic reliability; Artificial neural network; Monte Carlo Simulation; Failure probability
Ask authors/readers for more resources
Localization and quantification of structural damage and estimating the failure probability are key outputs in the reliability assessment of structures. In this study, an Artificial Neural Network (ANN) is used to reduce the computational effort required for reliability analysis and damage detection. Toward this end, one demonstrative structure is modeled and then several damage scenarios are defined. These scenarios are considered as training data sets for establishing an ANN model. In this regard, the relationship between structural response (input) and structural stiffness (output) is established using ANN models. The established ANN is more economical and achieves reasonable accuracy in detection of structural damage under a set of ground motions. Furthermore, in order to assess the reliability of a structure, five random variables are considered. These are columns' area of the first, second, and third floor, elasticity modulus, and gravity loads. The ANN is trained by suing the Monte Carlo Simulation (MCS) technique. Finally, the trained neural network specifies the failure probability of the proposed structure. Although MCS can predict the failure probability for a given structure, the ANN model helps simulation techniques to receive an acceptable accuracy and reduce computational effort.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available