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.

scholarly journals Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting

2020 ◽  
Vol 161 ◽  
pp. 878-892
Author(s):  
V.N. Sewdien ◽  
R. Preece ◽  
J.L. Rueda Torres ◽  
E. Rakhshani ◽  
M. van der Meijden
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Critical Parameters ◽  
Wind Generation ◽  
Short Term ◽  
Artificial Neural

Nonlinear Seismic Analysis of Masonry Infilled RC Frame Structures

2011 ◽  
Vol 117-119 ◽  
pp. 288-294
Author(s):  
Xiao Ying Gong ◽  
Jun Wu Dai
Keyword(s):  
Seismic Design ◽  
Frame Structures ◽  
Lateral Stiffness ◽  
Rc Frame ◽  
Stiffness Ratio ◽  
Infill Wall ◽  
Infill Walls ◽  
Floor Slab ◽  
Rc Frame Structures ◽  
Lateral Stiffness Ratio

Many RC frame structures were severely damaged or collapsed in some layer. The phenomenon was significantly different from the expected failure mode in seismic design code. This paper comprehensively sums up the earthquake characteristics of masonry infilled RC frame structures. Based on an investigation of a masonry infilled RC frame structure damaged in the earthquake area, conduct the research on frail-layer caused by infill walls uneven decorated. On the hypothesis of keeping the main load-bearing component invariant, two models were considered, i. e. frame with floor slab, and frame with both floor slab and infill wall. Furthermore, divide them into groups of the bottom, the middle and the top frail-layer to discuss by changing the arrange of infill wall. Time history analyses using three-dimensional sophisticated finite element method were conducted. The major findings are: 1)infill walls may significantly alter the failure mechanism of the RC frames. 2)controlling the initial interlayers lateral stiffness ratio in a reasonable range is an effective method to avoid frail-layer damage. These findings suggest that the effects of infill wall should be considered in seismic design, keep the initial interlayers lateral stiffness ratio less than the paper suggested, and the structural elasto-plastic analysis model should take slabs and infill walls into account.

Fundamental period of infilled RC frame structures with vertical irregularity

2017 ◽  
Vol 61 (5) ◽  
pp. 663-674 ◽  
Author(s):  
Panagiotis G. Asteris ◽  
Constantinos C. Repapis ◽  
Filippos Foskolos ◽  
Alkis Fotos ◽  
Athanasios K. Tsaris
Keyword(s):  
Fundamental Period ◽  
Frame Structures ◽  
Rc Frame ◽  
Rc Frame Structures

scholarly journals Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 138 ◽  
Author(s):  
Syed Naqvi ◽  
Tallha Akram ◽  
Sajjad Haider ◽  
Muhammad Kamran ◽  
Aamir Shahzad ◽  
...  
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Critical Parameters ◽  
Superconducting Thin Film ◽  
Current Voltage ◽  
Wide Range ◽  
Artificial Neural

Contemplating the importance of studying current–voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks’ coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks’ initialization and coefficients’ generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current–voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film.

Effect of infill walls on cumulative plastic deformation energy of RC frame structures

2019 ◽  
Vol 16 (2) ◽  
pp. 293-305
Author(s):  
Bing Bing Tu
Keyword(s):  
Plastic Deformation ◽  
Time History ◽  
Deformation Energy ◽  
Frame Structures ◽  
Response Analysis ◽  
Seismic Action ◽  
Rc Frame ◽  
Infill Walls ◽  
Content Type ◽  
Rc Frame Structures

Purpose A large number of earthquake damages showed that infill walls have obvious influence on the seismic damage performance of RC frame structures. The purpose of this paper is to study the effect of infill walls on the cumulative plastic deformation energy of RC frame structures, for which four RC frame structures are build and the time-history response analysis under unidirectional seismic action is presented. Design/methodology/approach The time-history response analysis under unidirectional seismic action is presented. Then the effect of periodic reduction coefficient on the cumulative plastic deformation energy of the structures, the beams and the columns is investigated. Findings Finally, the quantitative calculation formulas are provided. The results show that the periodic reduction coefficient has an obvious effect on the distribution of the accumulated plastic deformation energy, and the influence rules are presented here. Originality/value The effect of infill walls on the cumulative plastic deformation energy of RC frame structures is quantitatively analyzed here.

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