Seismic response prediction method for building structures using convolutional neural network

2020 ◽  
Vol 27 (5) ◽  
Author(s):  
Byung Kwan Oh ◽  
Youngjun Park ◽  
Hyo Seon Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Seismic Response ◽  
Prediction Method ◽  
Response Prediction ◽  
Building Structures

Neural network-based seismic response prediction model for building structures using artificial earthquakes

2020 ◽  
Vol 468 ◽  
pp. 115109 ◽  
Author(s):  
Byung Kwan Oh ◽  
Branko Glisic ◽  
Sang Wook Park ◽  
Hyo Seon Park
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Seismic Response ◽  
Response Prediction ◽  
Building Structures

scholarly journals A phase-error prediction method for coherent beam combining via convolutional neural network

Optik ◽  
2021 ◽  
pp. 167827
Author(s):  
Haolong Jia ◽  
Jing Zuo ◽  
Qiliang Bao ◽  
Chao Geng ◽  
Xinyang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phase Error ◽  
Prediction Method ◽  
Coherent Beam ◽  
Error Prediction ◽  
Coherent Beam Combining ◽  
Beam Combining

Dynamic response prediction of water medium explosion container based on convolutional neural network

2020 ◽  
pp. 1-10
Author(s):  
Linna Li ◽  
Chenchen Fang ◽  
Dongwang Zhong ◽  
Li He ◽  
Jianfeng Si
Keyword(s):  
Neural Network ◽  
Dynamic Response ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Response Prediction ◽  
Feature Analysis ◽  
Water Medium ◽  
Strain Response ◽  
Bp Neural Network Model ◽  
Different Doses

The water medium explosion container is an experimental device that simulates explosion in different water depth environments by loading different hydrostatic pressures and different doses of explosive. To ensure its safety during service, it is necessary to study the dynamic response of water medium explosion container. Because the dynamic response is complicated and the correlation between the response and the load of the container is nonlinear, it is difficult to calculate the dynamic response by analytical and numerical methods. In this paper, a model is built based on convolutional neural network (CNN) to predict the dynamic response of water medium explosion container. The accuracy and usability of the CNN prediction model are verified by comparison with the prediction results of the BP neural network model. The results show that CNN can be effectively used to predict the strain response of the dynamic response of water medium explosion container. and this method will play an important role in the later study of the overall feature analysis of the dynamic response of the water medium explosion vessel.

scholarly journals Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling

2020 ◽  
Vol 215 ◽  
pp. 110704 ◽  
Author(s):  
Ruiyang Zhang ◽  
Yang Liu ◽  
Hao Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Seismic Response ◽  
Data Driven ◽  
Response Modeling

scholarly journals SEISMIC RESPONSE PREDICTION METHOD OF SINGLE-STORY NC BRACED FRAMES BASED ON ENERGY BALANCE

2017 ◽  
Vol 82 (738) ◽  
pp. 1265-1274
Author(s):  
Shingo KOMATSU ◽  
Takao TAKAMATSU ◽  
Hiroyuki TAMAI ◽  
Teruaki YAMANISHI
Keyword(s):  
Energy Balance ◽  
Seismic Response ◽  
Prediction Method ◽  
Response Prediction ◽  
Braced Frames

scholarly journals Multiple Aerodynamic Coefficient Prediction of Airfoils Using a Convolutional Neural Network

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 544
Author(s):  
Hai Chen ◽  
Lei He ◽  
Weiqi Qian ◽  
Song Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Model Simulation ◽  
Prediction Method ◽  
Aircraft Design ◽  
Aerodynamic Characteristics ◽  
Aerodynamic Coefficients ◽  
Pitch Moment ◽  
Drag And Lift

Both symmetric and asymmetric airfoils are widely used in aircraft design and manufacture, and they have different aerodynamic characteristics. In order to improve flight performance and ensure flight safety, the aerodynamic coefficients of these airfoils must be obtained. Various methods are used to generate aerodynamic coefficients. The prediction model is a promising method that can effectively reduce cost and time. In this paper, a graphical prediction method for multiple aerodynamic coefficients of airfoils based on a convolutional neural network (CNN) is proposed. First, a transformed airfoil image (TAI) was constructed by using the flow-condition convolution with the airfoil image. Next, TAI was combined with the original airfoil image to form a composite airfoil image (CAI) that is used as the input of the CNN prediction model. Then, the structure and parameters of the prediction model were designed according to CAI features. Finally, a sample set that was generated on the basis of the deformation of symmetrical airfoil NACA 0012 was used to train and test the prediction model. Simulation results showed that the proposed method based on CNN could simultaneously predict the pitch-moment, drag, and lift coefficients, and prediction accuracy was high.

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