Predicting the Weight of the Steel Moment-Resisting Frame Structures Using Artificial Neural Networks

2018 ◽  
Vol 19 (1) ◽  
pp. 168-180 ◽  
Author(s):  
Seyed Shaker Hashemi ◽  
Kabir Sadeghi ◽  
Abdorreza Fazeli ◽  
Masoud Zarei
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Frame Structures ◽  
Moment Resisting Frame ◽  
Moment Resisting ◽  
Steel Moment Resisting Frame ◽  
Artificial Neural

scholarly journals Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Panagiotis G. Asteris ◽  
Athanasios K. Tsaris ◽  
Liborio Cavaleri ◽  
Constantinos C. Repapis ◽  
Angeliki Papalou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fundamental Period ◽  
Critical Parameters ◽  
Frame Structures ◽  
Rc Frame ◽  
Infill Walls ◽  
Rc Structures ◽  
Artificial Neural ◽  
Rc Frame Structures

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

Seismic Simulation of U.S. and Japanese Type Steel Moment-Resisting Frame Structures Using Practical FEM Macro Models

2018 ◽  
Vol 763 ◽  
pp. 557-565
Author(s):  
Hiroyuki Tagawa ◽  
Gregory A. MacRae
Keyword(s):  
The United States ◽  
Frame Structure ◽  
Frame Structures ◽  
Moment Connections ◽  
Type Steel ◽  
Moment Resisting Frame ◽  
Seismic Simulation ◽  
Moment Resisting ◽  
Steel Moment Resisting Frame ◽  
Complete Collapse

Building structures around the world have been designed using various framing methods. In Japan, the two-way moment-resisting frame structure, which is designed as a 3D seismic frame with beams connected to the columns, with moment connections in both directions, is traditionally constructed. In contrast, in the United States and many other countries in high seismic regions, the one-way moment-resisting frame structure, which is designed as separate seismic and gravity frame structure with only a few expensive moment connections in seismic frames, is typically constructed. Structures with these different framing systems are likely to exhibit different seismic response and collapse mechanism when subjected to large earthquake excitation. However, the simulation up to complete collapse has almost not been conducted and safety margin to complete collapse of these different framing systems has not been sufficiently understood. In this study, seismic simulation of U.S. and Japanese type three-story steel moment-resisting frame structures is conducted using general-purpose finite element analysis program. Practical macro models used for the simulation are based on beam and shell elements. It is found that composite effects of floor slab accelerate column yielding in both U.S. and Japanese type steel frame structures and drift concentration may occur at relatively small ground motion level and eventually result in complete collapse.

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