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RISS 인기검색어
- 검색키워드
- 저자 : Giray Ozay (검색결과 3 건)
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Calculating the collapse margin ratio of RC frames using soft computing models
Ali Sadeghpour,Giray Ozay 국제구조공학회 2022 Structural Engineering and Mechanics, An Int'l Jou Vol.83 No.3
The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.
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Minimization of differential column shortening and sequential analysis of RC 3D-frames using ANN
Wilfried W. Njomo,Giray Ozay 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.51 No.6
In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determineoptimal members’ sizes and the investigation of the parameters required to minimize the differential columnshortening are computational effort consuming, especially when considering various types of loads such asdead load, temperature action, time dependent effects, construction and live loads. Because the desiredaccuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neuralnetworks have been proposed in order to approximate this information. Instead of using a commercialsoftware package, many references providing advanced principles have been considered to code a programand generate these neural networks. The first one predicts the typical amount of time between two phases,needed to achieve the minimum maximorum differential column shortening. The other network aims toprognosticate sequential analysis results from those of the simultaneous analysis. After the training stages,testing procedures have been carried out in order to ensure the generalization ability of these respectivesystems. Numerical cases are studied in order to find out how good these ANN match with the sequentialfinite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in thepreliminary design stage.
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Minimization of differential column shortening and sequential analysis of RC 3D-frames using ANN
Njomo, Wilfried W.,Ozay, Giray Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.51 No.6
In the preliminary design stage of an RC 3D-frame, repeated sequential analyses to determine optimal members' sizes and the investigation of the parameters required to minimize the differential column shortening are computational effort consuming, especially when considering various types of loads such as dead load, temperature action, time dependent effects, construction and live loads. Because the desired accuracy at this stage does not justify such luxury, two backpropagation feedforward artificial neural networks have been proposed in order to approximate this information. Instead of using a commercial software package, many references providing advanced principles have been considered to code a program and generate these neural networks. The first one predicts the typical amount of time between two phases, needed to achieve the minimum maximorum differential column shortening. The other network aims to prognosticate sequential analysis results from those of the simultaneous analysis. After the training stages, testing procedures have been carried out in order to ensure the generalization ability of these respective systems. Numerical cases are studied in order to find out how good these ANN match with the sequential finite element analysis. Comparison reveals an acceptable fit, enabling these systems to be safely used in the preliminary design stage.
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