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.

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

2011 ◽  
Vol 71-78 ◽  
pp. 847-850
Author(s):  
Hua Guo Gao
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Back Propagation ◽  
Structure Design ◽  
Propagation Model ◽  
The Neural Network ◽  
Load Carrying ◽  
Predicted Values ◽  
Set Up ◽  
Concrete Filled Steel Tubes

Concrete filled steel tubes of square columns under axial load are in complicated stress, the influence of every factor on mechanics performance is difficult to ascertain accurately. Neural network performs well obtaining the relationship between input and output variables by self-studying, self-organizing, self-adapting and nonlinear mapping. In this paper a three-layer back-propagation model of network is successfully trained and set up according to experimental data of square CFT columns under load. Ten groups of experimental data were verified by the model, the results show the predicted values are in accord with test values, precision in calculation is good enough for structure design. So the neural network model can be used as an auxiliary method to calculate the capacity of square concrete filled tube columns in the project. With the increase of experimental data, the neural network precision of prediction will be improved in the future.

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.

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.

Geological Adaptive Cutterhead Selection for EPB Shield Based on BP Neural Network

2014 ◽  
Vol 607 ◽  
pp. 118-123
Author(s):  
Lai Kuang Lin ◽  
Yi Min Xia ◽  
Fei He ◽  
Qing Song Mao ◽  
Kui Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Earth Pressure ◽  
Training Model ◽  
Structure Design ◽  
Pressure Balance ◽  
Selection For ◽  
Earth Pressure Balance ◽  
Set Up ◽  
Epb Shield

In view of complex and fuzziness of geological adaptive cutterhead selection for earth pressure balance (EPB) shield, a cutterhead selection method based on BP neural network is put forward. Considering the structure characteristics of EPB shield cutterhead, typical cutterhead types are classified and summarized based on cutterhead topology structure and number of spokes. After analyzing the determinants of cutterhead selection, one-to-many mapping relation between cutterhead type and geological parameters is put forward, and then core geologic parameters related to cutterhead selection are concluded. The feasibility of using neural network method to choose the cutterhead type is analyzed, and a BP neural network training model for cutterhead selection is set up and tested in testing sample data. The result shows that the selected cutterhead and the construction cutterhead are basically consistent. The feasibility of this method is proved and it can be theoretical basis for the cutterhead structure design which will improve scientific of cutterhead selection.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

A Prediction Model of Deflection of Fixing Thin Parts

2012 ◽  
Vol 217-219 ◽  
pp. 1526-1529
Author(s):  
Yu Mei Liu ◽  
Wen Ping Liu ◽  
Zhao Liang Jiang ◽  
Zhi Li
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Simulated Data ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Abaqus Software

A prediction model of deflection is presented. The Artificial Neural Network (ANN) is adopted, and ANN establishes the mapping relation between the clamping forces and the position of fixing and the value of deflection. The results of simulation of Abaqus software is used for Training and querying an ANN. The predicted values are in agreement with simulated data and experimental data.

Back Propagation Artificial Neural Network Approach to Predict the Flow Stress in Isothermal Tensile Test of Medium Carbon Steel Material

2020 ◽  
Vol 977 ◽  
pp. 163-168
Author(s):  
Mohanraj Murugesan ◽  
Dong Won Jung
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Flow Stress ◽  
Carbon Steel ◽  
Back Propagation ◽  
Medium Carbon Steel ◽  
Ann Model ◽  
Medium Carbon ◽  
Average Absolute Relative Error ◽  
Steel Material

Isothermal tensile test of medium carbon steel material was conducted on a computer controlled servo-hydraulic testing machine at the deformation temperatures (923 to 1223 K) and the strain rates (0.05 to 1.0 s-1). Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For the model training and testing purpose, deformation temperature, strain rate and strain data were considered as inputs and in addition, the flow stress data were used a targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination (R2) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.997 and 0.913%, respectively. In addition, by evaluating each test conditions, it was found that the average absolute relative error based on an ANN model varied from 0.55% to 1.36% and moreover, the results showed the better predictability compared with the measured data. Overall, the trained BP-ANN model is found to be much more efficient and accurate by means of flow stress prediction with respect to the experimental data for an entire tested conditions.

scholarly journals Determination of Critical Load in a Nonuniform Circular Steel Column under the Eccentric Axial Load

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Stanislav Kotšmíd ◽  
Chang-Hung Kuo ◽  
Pavel Beňo
Keyword(s):  
Critical Load ◽  
Axial Load ◽  
Variable Cross Section ◽  
Steel Tube ◽  
Variable Cross ◽  
Runge Kutta Method ◽  
Circular Steel Tube ◽  
Load Carrying ◽  
Loading Tests ◽  
Different Diameters

The purpose of this paper is to determine a critical load for a nonuniform circular steel tube under eccentrically axial load. The circular tube has variable cross section at flattened ends with existing holes used for connection between members. Three different cases of eccentricities are studied with the drilled holes either on the same side or on the opposite side of column axis. The critical load is calculated from the differential equation of deflection curve which is solved by the power series and Runge-Kutta method. In addition, the loading tests were performed on a total of 180 specimens with different diameters, slenderness, and connection. The calculated results are compared and shown in a good agreement with those obtained from the experimental results. The results also show that the critical load decreases rapidly even at a small value of eccentricity and thus may have a significant effect on the load-carrying capacity.

Optimization of the Hot Pressing Parameters of Nanocomposite Ceramic Tool and Die Materials: I. with BP Neural Network Method

2010 ◽  
Vol 154-155 ◽  
pp. 1114-1118
Author(s):  
Jing Jie Zhang ◽  
Chong Hai Xu ◽  
Ming Dong Yi ◽  
Hui Fa Zhang ◽  
Xing Hai Wang
Keyword(s):  
Neural Network ◽  
Hot Pressing ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ceramic Tool ◽  
Network Method ◽  
Die Materials ◽  
Die Material ◽  
Set Up ◽  
The Relationship

In this paper, back propagation neural network was used in the optimum design of the hot pressing parameters of an advanced ZrO2/TiB2/Al2O3 nanocomposite ceramic tool and die material. The BP algorithm could set up the relationship well between the hot pressing parameters and mechanical property of nanocomposite ceramic tool and die materials. After analyzed the predicted results, the best predicted results were the sintering temperature was 1420°C and the holding time was 60min. Under these hot pressing parameters, the best flexural strength and the best fracture toughness of the material could be obtained.

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.

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