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Structural damage detection using an iterative neural network
A structural damage detection method based on parameter identification using an iterative neural network (NN) technique is proposed in this study. The NN model is first trained off-line using an initial training data set that consists of assumed structural parameters as outputs and their corresponding dynamic characteristics as inputs. The structural parameters are assumed with different levels of reduction to simulate various degrees of structural damage. The concept of orthogonal array is adopted to generate the representative combinations of parameter changes, which can significantly reduce the number of training data while maintaining the data completeness. A modified back-propagation learning algorithm is proposed which can overcome possible saturation of the sigmoid function and speed up the training process. The trained NN model is used to predict the structural parameters by feeding in measured dynamic characteristics. The predicted structural parameters are then used in the FE model to calculate the dynamic characteristics. The NN model would go through a retraining process if the calculated characteristics deviate from the measured ones. The identified structural parameters are then used to infer the location and the extent of structural damages. The proposed method is verified both numerically and experimentally using a clamped-clamped T beam. The results indicate that the current approach can identify both the location and the extent of damages in the beam.
Structural damage detection using an iterative neural network
A structural damage detection method based on parameter identification using an iterative neural network (NN) technique is proposed in this study. The NN model is first trained off-line using an initial training data set that consists of assumed structural parameters as outputs and their corresponding dynamic characteristics as inputs. The structural parameters are assumed with different levels of reduction to simulate various degrees of structural damage. The concept of orthogonal array is adopted to generate the representative combinations of parameter changes, which can significantly reduce the number of training data while maintaining the data completeness. A modified back-propagation learning algorithm is proposed which can overcome possible saturation of the sigmoid function and speed up the training process. The trained NN model is used to predict the structural parameters by feeding in measured dynamic characteristics. The predicted structural parameters are then used in the FE model to calculate the dynamic characteristics. The NN model would go through a retraining process if the calculated characteristics deviate from the measured ones. The identified structural parameters are then used to infer the location and the extent of structural damages. The proposed method is verified both numerically and experimentally using a clamped-clamped T beam. The results indicate that the current approach can identify both the location and the extent of damages in the beam.
Structural damage detection using an iterative neural network
Chang, C.C. (author) / Chang, T.Y.P. (author) / Xu, Y.G. (author) / Wang, M.L. (author)
2000
11 Seiten, 36 Quellen
Article (Journal)
English
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