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Optimum distribution of seismic energy dissipation devices using neural network and fuzzy inference system
The current study proposes a framework to provide an optimal distribution of energy dissipation devices for low‐ and mid‐rise framed buildings using an artificial neural network (ANN) and fuzzy inference system (FIS). Three illustrative framed buildings to be retrofitted using steel slit dampers are presented to demonstrate the effectiveness of the proposed framework. Two hundred natural earthquake records are used to consider variability in seismic ground motions, and 33,600 nonlinear time history analyses (NLTHAs) are conducted to train the framework. Three engineering demand parameters are used to represent the strength and serviceability requirements concurrently, and the fuzziness of structural limit states is also included within the framework. The framework is fine‐tuned by carrying out sensitivity exercises based on different sample sizes to select the most appropriate training algorithm, activation function, and ANN architecture. After that, the proposed framework is compared with three different supervised machine learning classification algorithms; namely, support vector machine, decision tree, and bagged ensemble. The results show the superiority of the proposed framework compared to the conventional machine learning algorithms in predicting the ranking and obtaining the optimum retrofit scheme for low‐ and mid‐rise framed buildings. Finally, NLTHAs are conducted to validate the results produced by the framework, which are found to be in good agreement with the NLTHAs testing results.
Optimum distribution of seismic energy dissipation devices using neural network and fuzzy inference system
The current study proposes a framework to provide an optimal distribution of energy dissipation devices for low‐ and mid‐rise framed buildings using an artificial neural network (ANN) and fuzzy inference system (FIS). Three illustrative framed buildings to be retrofitted using steel slit dampers are presented to demonstrate the effectiveness of the proposed framework. Two hundred natural earthquake records are used to consider variability in seismic ground motions, and 33,600 nonlinear time history analyses (NLTHAs) are conducted to train the framework. Three engineering demand parameters are used to represent the strength and serviceability requirements concurrently, and the fuzziness of structural limit states is also included within the framework. The framework is fine‐tuned by carrying out sensitivity exercises based on different sample sizes to select the most appropriate training algorithm, activation function, and ANN architecture. After that, the proposed framework is compared with three different supervised machine learning classification algorithms; namely, support vector machine, decision tree, and bagged ensemble. The results show the superiority of the proposed framework compared to the conventional machine learning algorithms in predicting the ranking and obtaining the optimum retrofit scheme for low‐ and mid‐rise framed buildings. Finally, NLTHAs are conducted to validate the results produced by the framework, which are found to be in good agreement with the NLTHAs testing results.
Optimum distribution of seismic energy dissipation devices using neural network and fuzzy inference system
Noureldin, M. (author) / Ali, A. (author) / Nasab, M. S. E. (author) / Kim, J. (author)
Computer‐Aided Civil and Infrastructure Engineering ; 36 ; 1306-1321
2021-10-01
16 pages
Article (Journal)
Electronic Resource
English
| Wiley | 2021
Seismic Upgrades Using Energy Dissipation Devices
| British Library Conference Proceedings | 1994
Seismic design with supplemental energy dissipation devices
| TIBKAT | 2001
Seismic design with supplemental energy dissipation devices
| UB Braunschweig | 2001