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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Advance Researches in Civil Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Active control system,Control forces,Structural active vibration control
- چکیده:
- چکیده انگلیسی: In this paper a numerical investigation of installation of actuator in a toggle configuration for decreasing of active control forces in engineering structures has been carried out. During the past two decades, researchers have been focused to prevent the vibration of tall building from strong earthquakes. For achieving this purpose, they applied either massive conventional bracing or passive energy dissipation dampers. Subsequently, they developed active control systems in structures to resist against the high seismic loads. However, this later method eventuates installing massive actuators in building which are not only very costly and uneconomically but also needs large electricity power. In this research, using by known earthquakes, investigation of the effects of the toggle configuration on actuator forces has been performed numerically. For numerical investigation, active tendon control system was selected as a comparison. The numerical investigation shows significant reduction in actuator forces through using toggle configuration. Finally, comparing results through the numerical processe express high matching that relies on mitigation of control forces in the toggled active model.
- انتشار مقاله: 16-07-1397
- نویسندگان: Seyyed Farhad Mirfakhraei,Gholamreza Andalib,Ricky Chan
- مشاهده
- جایگاه : پژوهشی
- مجله: Advance Researches in Civil Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Stream flow,Denoising,Artificial Neural Network,Least Square Support Vector Machine,Multi-Station,Snoqualmie watershed
- چکیده:
- چکیده انگلیسی: In this study, the ability of threshold based wavelet denoising Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) models were evaluated for forecasting daily Multi-Station (MS) streamflow of the Snoqualmie watershed. For this aim, at first step, outflow of the watershed was forecasted via ad hoc LSSVM and ANN models just by one station individually. Therefore, MS-LSSVM and MS-ANN were employed to use entire information of all sub-basins synchronously. Finally, the streamflow of sub-basins were denoised via wavelet based thresholding method, then the purified signals were imposed into the LSSVM and ANN models in a MS framework. The results showed the superiority of ANN to the LSSVM, MS model to the individual sub-basin model, using denoised data with regard to the noisy data, e.g., DCLSSVM=0.82, DCANN=0.85, DCMS-ANN=0.91, DCdenoised-MS-ANN=0.94.
- انتشار مقاله: 29-05-1397
- نویسندگان: Gholamreza Andalib,Vahid Nourani
- مشاهده
- جایگاه : پژوهشی
- مجله: Advance Researches in Civil Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Stream flow,Denoising,Artificial Neural Network,Least Square Support Vector Machine,Multi-Station,Snoqualmie watershed
- چکیده:
- چکیده انگلیسی: In this study, the ability of threshold based wavelet denoising Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) models were evaluated for forecasting daily Multi-Station (MS) streamflow of the Snoqualmie watershed. For this aim, at first step, outflow of the watershed was forecasted via ad hoc LSSVM and ANN models just by one station individually. Therefore, MS-LSSVM and MS-ANN were employed to use entire information of all sub-basins synchronously. Finally, the streamflow of sub-basins were denoised via wavelet based thresholding method, then the purified signals were imposed into the LSSVM and ANN models in a MS framework. The results showed the superiority of ANN to the LSSVM, MS model to the individual sub-basin model, using denoised data with regard to the noisy data, e.g., DCLSSVM=0.82, DCANN=0.85, DCMS-ANN=0.91, DCdenoised-MS-ANN=0.94.
- انتشار مقاله: 29-05-1397
- نویسندگان: Gholamreza Andalib,Vahid Nourani
- مشاهده