Hybrid Simulation of a Frame Equipped with MR Damper by Utilizing Least Square Support Vector Machine
Authors
Abstract:
In hybrid simulation, the structure is divided into numerical and physical substructures to achieve more accurate responses in comparison to a full computational analysis. As a consequence of the lack of test facilities and actuators, and the budget limitation, only a few substructures can be modeled experimentally, whereas the others have to be modeled numerically. In this paper, a new hybrid simulation has been introduced utilizing Least Square Support Vector Machine (LS-SVM) instead of physical substructures. With the concept of overcoming the hybrid simulation constraints, the LS-SVM is utilized as an alternative to the rate-dependent physical substructure. A set of reference data is extracted from appropriate test (neumerical test) as the input-output data for training LS-SVM. Subsequently, the trained LS-SVM performs the role of experimental substructures in the proposed hybrid simulation. One-story steel frame equipped with Magneto-Rheological (MR) dampers is analyzed to examine the ability of LS-SVM model. The proposed hybrid simulation verified by some numerical examples and results demonstrate the capability and accuracy of this new hybrid simulation.
similar resources
forecasting municipal solid waste generation by hybrid support vector machine and partial least square model
forecasting of municipal waste generation is a critical challenge for decision making and planning,because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (msw) streams analysis and accurate predictions of solid waste quantities generated. due to dynamic and complexity of solid waste management system, models by artificial intell...
full textforecasting municipal solid waste generation by hybrid support vector machine and partial least square model
forecasting of municipal waste generation is a critical challenge for decision making and planning,because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (msw) streams analysis and accurate predictions of solid waste quantities generated. due to dynamic and complexity of solid waste management system, models by artificial intell...
full texta hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine
in this paper, we present a new brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (ls-svm). the method consists of three steps. in the first two steps, the skull is removed and cerebrospinal fluid (csf) is extracted. these two steps are performed using the fast toolbox (fmrib's a...
full textModeling of Corrosion-Fatigue Crack Growth Rate Based on Least Square Support Vector Machine Technique
Understanding crack growth behavior in engineering components subjected to cyclic fatigue loadings is necessary for design and maintenance purpose. Fatigue crack growth (FCG) rate strongly depends on the applied loading characteristics in a nonlinear manner, and when the mechanical loadings combine with environmental attacks, this dependency will be more complicated. Since, the experimental inv...
full textCredit Risk Evaluation with Least Square Support Vector Machine
Credit risk evaluation has been the major focus of financial and banking industry due to recent financial crises and regulatory concern of Basel II. Recent studies have revealed that emerging artificial intelligent techniques are advantageous to statistical models for credit risk evaluation. In this study, we discuss the use of least square support vector machine (LSSVM) technique to design a c...
full textA Hybrid Least Square Support Vector Machine Model with Parameters Optimization for Stock Forecasting
This paper proposes an EMD-LSSVM (empirical mode decomposition least squares support vector machine) model to analyze the CSI 300 index. A WD-LSSVM (wavelet denoising least squares support machine) is also proposed as a benchmark to compare with the performance of EMD-LSSVM. Since parameters selection is vital to the performance of the model, different optimization methods are used, including s...
full textMy Resources
Journal title
volume 2 issue 3
pages 58- 66
publication date 2018-03
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023