Virtual Testing of Buckling-Restrained Braces via Nonlinear Autoregressive Exogenous Neural Networks
01 Nov 2013-Journal of Computing in Civil Engineering (American Society of Civil Engineers)-Vol. 27, Iss: 6, pp 755-768
TL;DR: In this article, an artificial intelligence model utilizing feedforward back-propagation (FFBP) and nonlinear autoregressive exogenous (NARX) artificial neural networks (ANNs) is presented to model the nonlinear behavior of buckling-restrained braces (BRBs).
Abstract: An artificial intelligence model utilizing feedforward back-propagation (FFBP) and nonlinear autoregressive exogenous (NARX) artificial neural networks (ANNs) is presented to model the nonlinear behavior of buckling-restrained braces (BRBs). The NARX ANN is developed using normalized time-delayed inputs and outputs to predict normalized brace forces during load reversals. The values of brace forces are denormalized via an auxiliary FFBP ANN. The training and testing of the proposed model (i.e., the NARX and FFBP ANNs) are performed using experimental data from BRB specimens tested at the Pacific Earthquake Engineering Research (PEER) Center. Experimental data from one specimen is used in the model developing (training) stage. In addition, three sets of data are used to test the model’s learning and generalizing abilities. Brace deformations are used as the network input to estimate the resulting brace forces. The network performance with different parameters is evaluated to arrive at an optimized ...
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TL;DR: In this paper, a simple model of buckling restrained braces (BRBs) is presented and a method is presented to find the spring stiffness, and a comparison is made between the experimental and analytical hysteresis curves (the complete model and the simplified model).
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TL;DR: In this paper, the most influential BRB design parameters as well as the common failure modes observed are explored through detailed nonlinear finite element analysis (FEA) using the commercial software ABAQUS taking into consideration both material and geometric nonlinearities.
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TL;DR: In this article, four data-driven models, namely Non-Linear Multivariate Regression (NLMR), KNN, non-linear Autoregressive with External Input based Artificial Neural Networks (NARX-ANN), and symbolic regression (SR), were employed for forecasting water yield after bushfire in a forested catchment in southeast Australia.
Abstract: Forested catchments in southeast Australia play an important role in supplying water to major cities. Over the past decades, vegetation cover in this area has been affected by major bushfires that in return influence water yield. This study tests methods for forecasting water yield after bushfire, in a forested catchment in southeast Australia. Precipitation and remotely sensed Normalized Difference Vegetation Index (NDVI) were selected as the main predictor variables. Cross-correlation results show that water yield with time lag equal to 1 can be used as an additional predictor variable. Input variables and water yield observations were set based on 16-day time series, from 20 January 2003 to 20 January 2012. Four data-driven models namely Non-Linear Multivariate Regression (NLMR), K-Nearest Neighbor (KNN), non-linear Autoregressive with External Input based Artificial Neural Networks (NARX-ANN), and Symbolic Regression (SR) were employed for this study. Results showed that NARX-ANN outperforms other models across all goodness-of-fit criteria. The Nash-Sutcliffe efficiency (NSE) of 0.90 and correlation coefficient of 0.96 at the training-validation stage, as well as NSE of 0.89 and correlation coefficient of 0.95 at the testing stage, are indicative of potentials of this model for capturing ecological dynamics in predicting catchment hydrology, at an operational level.
11 citations
Cites methods from "Virtual Testing of Buckling-Restrai..."
...4 1 + × − + × − + × = − − − − × − − i i E i i WY NDVI ec WY ( 11) Based on the recommendations of previous studies [44,52], here in the three-layer NARX-ANNs model, “Levenberg–Marquardt” algorithm function was used as back-propagation calibrationvalidation algorithm, and “Tangent sigmoid” function was applied for the hidden layer neurons, and finally “Linear Transfer” function was employed in the output layer neuron....
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...NARX-ANN is able to model nonlinear autoregressive time series and it is quite appropriate to identify nonlinear dependencies among dependent and estimator variables [43,44]....
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TL;DR: It is concluded that Kriging-based Monte Carlo Simulation (MCS) gives the best performance to estimate the limit state function (LSF) of BRB and SC-BRB in the reliability analysis procedures.
Abstract: This paper aims to carry out sensitivity analyses to study how the effect of each design variable on the performance of self-centering buckling restrained brace (SC-BRB) and the corresponding buckl...
7 citations
Cites background from "Virtual Testing of Buckling-Restrai..."
...They designed CBFs based on traditional and innovative methodologies and compared their seismic reliability....
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...Giugliano et al. (2011) studied the seismic reliability of CBFs....
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...As shown in Figure 1, a hysteresis energy dissipation of buckling restrained brace is given in comparison with the concentric braced frame (CBF) (AlHamaydeh et al., 2013)....
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