Seismic Behavior of Concrete Filled Circular Steel Tubular Columns Based on Artificial Neural Network

2012 ◽  
Vol 502 ◽  
pp. 189-192
Author(s):  
Hua Wei ◽  
Yu Du ◽  
Hai Jun Wang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
High Strength ◽  
Steel Tube ◽  
Structure Design ◽  
Seismic Properties ◽  
Artificial Neural ◽  
Propagation Network

Artificial neural network (ANN) is self-adaptability, fault toleration and fuzziness. It is suitable to solve the seismic properties of high strength reinforced concrete columns with concrete filled steel tube core (HRCCFT). A three-layer back-propagation network model is build up to study the seismic properties of HRCCFT. The model is trained according to 30 sets of experimental data. The network convergence is fast. The model is verified by 8 groups of experimental data, the results show the predicted values of displacement ductility are in good agreement with test values. The precision of model is better than that of formula from other reference. This method is good enough to be used as an auxiliary method for structure design.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

Designing and Analysis of Antenna Using Back Propagation Network

2019 ◽  
pp. 453-490
Author(s):  
Rajeev Kumar ◽  
Ritu Vijay ◽  
Surjit Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Computational Intelligence ◽  
Back Propagation ◽  
Adequate Number ◽  
Ansoft Hfss ◽  
Intelligence Algorithm ◽  
Artificial Neural ◽  
Propagation Network

This chapter deals with the designing of an antenna using computational intelligence algorithm (i.e., ANN). An artificial neural network (ANNs) based on back propagation network (BPN) is demonstrated for the designing of an antenna. Initially, the ANN is trained with an adequate number of samples. Once the ANN is trained, it can be used to get antenna parameters corresponding to specific antenna dimensions. The antenna parameters obtained using ANN is compared with the results taken by Ansoft HFSS.

scholarly journals Prediction of the sintering shrinkage of glass-alumina functionally graded materials by a BP artificial neural network

2009 ◽  
Vol 41 (3) ◽  
pp. 257-266 ◽  
Author(s):  
C.L. Yu ◽  
H. Yang ◽  
D.C. Zhao ◽  
C.C. Liu ◽  
T. Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Functionally Graded Materials ◽  
Back Propagation ◽  
Functionally Graded ◽  
Alumina Content ◽  
Graded Materials ◽  
Artificial Neural ◽  
Hidden Layer

The shrinkage of the glass-alumina functionally graded materials (G-A FGMs) as a function of sintering temperature, layers, and the alumina content was predicted by a back propagation artificial neural network (BP-ANN). The BP-ANN was composed of an input layer, a hidden layer, and an output layer. 21 sets of experimental data were trained, in which the temperature, layers, and the alumina content as input parameters whereas the shrinkage as the output parameter. 5 sets of experimental data were used to identify the accuracy of the BP-ANN. From the prediction, selection of the hidden layer neurons is essential for the convergence of the BP-ANN. The minimum predicted errors less than 6.6% are obtained with 8 neurons. Comparison of the predicted shrinkage shows that the increase of layers or alumina content is beneficial to the increase of the shrinkage and expansion resistance for the G-A FGMs.

Calculation of Load-Carrying Capacity of Square Concrete Filled Tube Columns Based on Neural Network

2013 ◽  
Vol 351-352 ◽  
pp. 713-716
Author(s):  
Hua Guo Gao ◽  
Hang Cheng ◽  
Xiao Feng Cui
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Axial Load ◽  
Back Propagation ◽  
Steel Tube ◽  
Structure Design ◽  
Propagation Model ◽  
Load Carrying ◽  
Predicted Values ◽  
Set Up

Steel tube and filled concrete of square CFT columns under axial load are in complicated stress condition, the influence of every kind of factors on mechanics performance is difficult to ascertain accurately. On the other hand, neural network is good at obtaining the relationship between input and output variables by self-studying, self-organizing, self-adapting and nonlinear mapping. Therefore, it is suitable that use neural network to calculating the bearing capacity of square CFT columns. In this paper a three-layer back-propagation model of network is trained according to experimental data of square CFT columns under axial load, a neural network model for axial loaded square CFT columns is set up. The model is verified by six groups of experimental data, the results show the predicted values are in good agreement with test values, precision in calculation is good enough to be used as an auxiliary method for structure design.

Bearing Capacity of Concrete Filled Square Steel Tubular Columns Based on Neural Network

2012 ◽  
Vol 502 ◽  
pp. 193-197 ◽  
Author(s):  
Hai Jun Wang ◽  
Hua Bei Zhu ◽  
Hua Wei
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Bearing Capacity ◽  
Back Propagation ◽  
Steel Tube ◽  
Structure Design ◽  
Propagation Model ◽  
Eccentric Load ◽  
Tubular Columns ◽  
Set Up

Steel tube and filled concrete of square CFT (concrete filled steel tubular structures) columns under eccentric load are in complicated stress condition, the influence of every kind of factors on mechanics performance is difficult to ascertain accurately. On the other hand, neural network is good at obtaining the relationship between input and output variables by self-studying, self-organizing, self-adapting and nonlinear mapping. Therefore, it is suitable that use neural network to calculating the bearing capacity of square CFT columns. In this paper a four-layer back-propagation model of network is trained according to experimental data of square CFT columns under eccentric load, a neural network model for eccentrically loaded square CFT columns is set up. The model is verified by six groups of experimental data, the results show the predicted values are in good agreement with test values, precision in calculation is good enough to be used as an auxiliary method for structure design.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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