Dynamic Response Prediction of RC Structural Components Subjected to Combined Blast and Fragment Impact

2022 ◽  
Vol 148 (2) ◽  
Author(s):  
Abhiroop Goswami ◽  
Thiagarajan Ganesh ◽  
Satadru Das Adhikary
Keyword(s):  
Dynamic Response ◽  
Response Prediction ◽  
Structural Components

scholarly journals On Predicting the Seismic Response of Acceleration-Sensitive Non-Structural Components in Buildings

2018 ◽  
Author(s):  
Carmine Lima ◽  
Enzo Martinelli
Keyword(s):  
Dynamic Response ◽  
Seismic Response ◽  
Parametric Analysis ◽  
Short Review ◽  
Structural Components ◽  
Main Structure ◽  
Current State ◽  
Critical Issues ◽  
Key Features ◽  
Two Degree Of Freedom

This paper is intended at highlighting the main mechanical parameters controlling the behavior of the so-called "acceleration-sensitive" Non-Structural Components (NSCs). In the first part a short review of the current state of knowledge and the critical issues related to the prediction of the seismic response of NSCs is reported. Then, the paper presents the results of a numerical parametric analysis intended to capture the key features of the dynamic response of a two-degree-of-freedom (2DOF) system which is supposed to be representative of both the main structure and the "non-structural" component (NSC). Particularly, it allows to simulate the coupled behaviour of both main structure and NSC and evaluating their response. The main parameters controlling the dynamic response of NSCs emerge from this study, which could pave the way towards formulating more mechanically consistent relationships for evaluating the maximum accelerations induced by seismic shakings on NSCs.

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 Dynamic Response Prediction of Underwater Explosion Vessels

2020 ◽  
Vol 453 ◽  
pp. 012040
Author(s):  
Linna Li ◽  
Yanfei Hu ◽  
Chenchen Fang ◽  
Yue You ◽  
Kai Liu ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Underwater Explosion ◽  
Response Prediction

Structural Surrogate Model and Dynamic Response Prediction with Consideration of Temporal and Spatial Evolution: An Encoder–Decoder ConvLSTM Network

2021 ◽  
pp. 2150140
Author(s):  
Hong Peng ◽  
Jingwen Yan ◽  
Ying Yu ◽  
Yaozhi Luo
Keyword(s):  
Dynamic Response ◽  
Surrogate Model ◽  
Structural Model ◽  
Short Term Memory ◽  
Time History ◽  
Response Prediction ◽  
Surrogate Models ◽  
Hybrid Network ◽  
Spatiotemporal Evolution ◽  
Structural Dynamic

In this paper, a new deep learning framework named encoding convolution long short-term memory (encoding ConvLSTM) is proposed to build a surrogate structural model with spatiotemporal evolution of structure, estimate the structural spatiotemporal state and predict the dynamic response under similar future dynamic load conditions. The main work of this study includes: (a) The spatiotemporal response tensor database is developed using discrete-time history data of structural dynamic response. (b) As an extension of LSTM, convolution operation is combined with LSTM network to construct structural surrogate model from the spatiotemporal evolution structural performance. (c) To enhance the anti-interference ability of structural surrogate models, a new three-layer encoding layer is added for denoising autoencoders of the hybrid network. The influence of building types and input noise on the accuracy and antinoise performance of the surrogate models are analyzed through the dynamic response prediction of a frame-shear wall, a cylindrical, and a spherical reticulated shell structure. As a testbed for the proposed network, a case study is performed on a laboratory stadium structure. The results demonstrate that the developed surrogate model can predict the structural dynamic response precisely with more under 30% noise interference.

Improvement of dynamic response prediction of helicopters

2007 ◽  
Vol 79 (6) ◽  
pp. 579-592 ◽  
Author(s):  
Farid Shahmiri ◽  
Fariborz Saghafi
Keyword(s):  
Dynamic Response ◽  
Response Prediction
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