Neural Network Modeling of an Aerosuspended Vehicle
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Artificial neural networks have attracted considerable attention and have shown promise for modeling complex nonlinear relationships. This paper explores the use of artificial neural networks in predicting the confinement efficiency of concentrically loaded reinforced concrete (RC) columns with rectilinear transverse steel. Fifty-five experimental test results were collected from the literature of square columns tested under concentric loading. A multilayer-functional-link neural network was used for training and testing the experimental data. A comparison study between the neural network model and four parametric models is also carried out. It was found that the neural network model could reasonably capture the underlying behavior of confined RC columns. Moreover, compared with parametric models, the neural network approach provides better results. The close correlation between experimental and calculated values shows that neural network-based modeling is a practical method for predicting the confinement efficiency of RC columns with transverse steel because it provided instantaneous result once it is properly trained and tested.
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Abstract A new approach for assessing nonlinear interaction effects and closed‐loop nonlinearity in multivariable processes is presented. It is based on a differential geometric interpretation of the relative gain array that leads naturally to systematic procedures for describing interaction effects of higher order and for assessing closed‐loop nonlinearity effects in nonlinear processes. Two types of nonlinear effects associated with the behavior of a process are introduced. Between‐channel nonlinearity is associated with the nonlinear dependence of an output channel on other input ‐ output pairings. Withinchannel nonlinearity is used to identify the nonlinear effects that result from the inherent nonlinearity of an individual output channel. A root‐mean‐squared measure of nonlinearity is introduced and is used to evaluate the significance of local nonlinear effects. Nonlinear interaction measures are derived that provide tools for assessing input ‐ output pairings in a nonlinear process. This new approach extends the standard techniques and provides an estimate of the effect of nonlinearity on closed‐loop interactions.
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Most of the practical engineering structures exhibit nonlinearity due to nonlinear dynamic characteristics of structural joints, nonlinear boundary conditions and nonlinear material properties. Hence, it is highly desirable to detect and characterize the nonlinearity present in the system in order to assess the true behaviour of the structural system. Further, these identified nonlinear features can be effectively used for damage diagnosis during structural health monitoring. In this paper, we focus on the detection of the nonlinearity present in the system by confining our discussion to only a few selective time-frequency analysis and multivariate analysis based techniques. Both damage induced nonlinearity and inherent structural nonlinearity in healthy systems are considered. The strengths and weakness of various techniques for nonlinear detection are investigated through numerically simulated two different classes of nonlinear problems. These numerical results are complemented with the experimental data to demonstrate its suitability to the practical problems.
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