Neural network based prediction schemes of the non-linear seismic response of 3D buildings

Lagaros, Nikos D.; Papadrakakis, Manolis

doi:10.1016/j.advengsoft.2011.05.033

Cited by 117 publications

(43 citation statements)

References 30 publications

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“…For some of the proposed methods, the selection methods of the seismic loads used for verification of the trained NNs were inappropriate. In some studies, the same EQs as those used for training were taken as new EQs for verification, with only the intensities being changed . Thus, the factors affecting the response amplifications of the building structures subjected to the EQs, such as their duration and dominant period, were unchanged.…”

Section: Introductionmentioning

confidence: 99%

“…In some cases, the validity of the seismic damage prediction method was examined numerically using structural modeling information for a number of building cases as well as damage indexes obtained through FEM‐based analysis of the model, without introduction of the SHM concept, which is a practical and reasonable approach to structural condition evaluation. In other studies, although the methods were implemented in the SHM framework based on recorded structural responses, only numerical validation was presented. Thus, support for practical application of the developed methods using the measured structural responses was not provided.…”

Section: Introductionmentioning

confidence: 99%

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Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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Summary In this study, a method of predicting the seismic responses of building structures based on a convolutional neural network (CNN) is proposed. In the method, the time histories of acceleration responses previously measured in a building during earthquakes are used in the CNN input layer, with the corresponding time histories of the displacement responses being used in the CNN output layer. The correlations between the features automatically extracted from the acceleration responses by the convolution and pooling operations in the various CNN layers and the displacement responses in the CNN output layer are established through CNN training. The trained CNN predicts the displacement responses for a future earthquake event using only the measured acceleration responses. To verify the validity of the proposed method, a numerical study on the ASCE benchmark model and an experimental study on a reinforced concrete frame structure are conducted. In the numerical study, the structural responses of the ASCE model subject to artificial earthquakes are utilized for CNN training, and the performance of the trained CNN is verified through seismic response prediction. In the experimental study, shaking table tests are performed for various earthquakes to measure the seismic responses of the reinforced concrete test structure. The seismic response prediction performance of the proposed method is examined using CNN trained with the measured seismic responses. Furthermore, the performances of CNNs trained with different numbers of datasets generated by the proposed data generation method based on data overlapping with the same data pool are discussed with related to the number of CNN training iteration number.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seismic response prediction method for building structures using convolutional neural network

Park

2020

Struct Control Health Monit

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“…[38] proposes a NN based prediction scheme for the dynamic behavior of structural systems under multiple seismic excitations. The NN prediction includes two different ways: 1) A non-adaptive scheme that uses multiple accelerometers in training NN, and utilizes for the prediction of the structural seismic response ; 2) An adaptive scheme uses multiple accelerometers in the training.…”

Section: Bidirectional Vibration Controlmentioning

confidence: 99%

Advances in bidirectional modeling and structural control

Paul

2015

2015 Resilience Week (RWS)

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This paper provides an overview of building structure modeling and control under bidirectional seismic waves. It focuses on different types of bidirectional control devices, control strategies, and bidirectional sensors used in structural control systems. This paper also highlights the various issues like system identification techniques, the time-delay in the system, estimation of velocity and position from acceleration signals, and optimal placement of the sensors and control devices.978-1-4799-8594-4/15/$31.00 ©2015 IEEE

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“…In order to correlate the parameters it was proposed to use artificial neural networks (ANN). The latter have been increasingly used in construction [12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%