A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Structural damage detection using the optimal weights of the approximating artificial neural networks
10.1002/eqe.106.abs
This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.
Structural damage detection using the optimal weights of the approximating artificial neural networks
10.1002/eqe.106.abs
This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.
Structural damage detection using the optimal weights of the approximating artificial neural networks
Hung, Shih‐Lin (author) / Kao, C. Y. (author)
Earthquake Engineering & Structural Dynamics ; 31 ; 217-234
2002-02-01
18 pages
Article (Journal)
Electronic Resource
English
Structural Damage Detection Using Artificial Neural Networks
| Online Contents | 1998
Artificial Neural Networks for Structural Damage Detection using Modal Data
| British Library Online Contents | 2010
Damage detection under varying temperature using artificial neural networks
| Wiley | 2017
Prediction of seismic-induced structural damage using artificial neural networks
| Online Contents | 2009