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.

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.

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.

Application of ANN Back-Propagation for An Alloy Reinforced Ceramics/Metal Composite under Extra-Low Cycle Bending Fatigue Loading

2010 ◽  
Vol 105-106 ◽  
pp. 108-111
Author(s):  
Zhi Yuan Rui ◽  
Hong Yan Duan ◽  
Chun Li Lei ◽  
Xing Chun Wei
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Propagation Model ◽  
Training Data ◽  
Design Parameters ◽  
Metal Composite ◽  
Ann Model ◽  
The Neural Network

Artificial neural network (ANN) back-propagation model was developed to predict the fracture design parameters in reinforced ceramic matrix composites (CMCS).Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used.

Prediction of Spring Back of the Two-Axle Rotary Shaping Based on Neural Network

2006 ◽  
Vol 532-533 ◽  
pp. 1044-1047
Author(s):  
Shi Hong Lu ◽  
Jing Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Fem Simulation ◽  
Back Propagation Neural Network ◽  
Small Error ◽  
Forming Process ◽  
The Neural Network ◽  
Metal Forming Process ◽  
Set Up ◽  
Good Agreement

Two-axle rotary shaping is one of advanced sheet metal forming process that combined stamping ascendant used elastic medium with traditional rotary shaping principle. The prediction model of two-axle rotary shaping is set up to predict the springback for two-axle rotary shaping. It used the back propagation neural network because of the better nonlinear mapping ability. Some of data from the experiment and FEM simulation is applied to train the network; the other data is used to test the prediction result. The result showed that the value of prediction and experiment is in good agreement, and just small error is existed. It demonstrated that the neural network model might predict the springback of two-axle rotary shaping and reduce the number of simulation calculation and experiment operation. It can offer a powerful guidance for rapid choice of process parameters in production.

Possibility of Anaesthetical Activity Prediction of N-(Pyrrolidinyl)ethyl Esters of Alkoxyphenylcarbamic Acids

1995 ◽  
Vol 60 (8) ◽  
pp. 1410-1414
Author(s):  
Štefan Hatrík ◽  
Jozef Lehotay ◽  
Jozef Čižmárik
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Ethyl Esters ◽  
Average Error ◽  
Activity Prediction ◽  
Rp Hplc ◽  
The Neural Network ◽  
Structural Formulas ◽  
Predicted Values ◽  
Good Agreement

A back-propagation-type neural network consisting of three layers was used for predicting of surface and infiltration anaesthetical activity of pyrrolidinylethyl esters of 2- and 3-alkoxyphenylcarbamic acids. The prediction of local anaesthesia was based on the knowledge of this property for piperidinylethyl esters, piperidinylpropyl esters and azepanylethyl esters of 2- and 3-alkoxy- phenylcarbamic acids. The structural formulas of compounds pre-processed to the numerical input and RP-HPLC capacity factors (characterizing the lipophility of tested compounds) were used for generating inputs for the neural network. Anaesthetical activities of tested drugs calculated by neural network were in a good agreement with the experimentally measured data. Average error of predicted values ranges from 2% for the surface anaesthesia of pyrrolidinylethyl esters of 2-alkoxy- phenylcarbamic acids to 15% for the infiltration anaesthesia of pyrrolidinylethyl esters of 3-alkoxyphenylcarbamic acids.

scholarly journals Hysteresis Modeling and Compensation of Fast Steering Mirrors with Hysteresis Operator Based Back Propagation Neural Networks

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 732
Author(s):  
Kairui Cao ◽  
Guanglu Hao ◽  
Qingfeng Liu ◽  
Liying Tan ◽  
Jing Ma
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Hysteresis Model ◽  
Cooperative Movement ◽  
Hysteresis Operator ◽  
The Neural Network ◽  
Hysteresis Nonlinearity ◽  
Hysteresis Modeling ◽  
Hysteresis Characteristics ◽  
Inverse Hysteresis

Fast steering mirrors (FSMs), driven by piezoelectric ceramics, are usually used as actuators for high-precision beam control. A FSM generally contains four ceramics that are distributed in a crisscross pattern. The cooperative movement of the two ceramics along one radial direction generates the deflection of the FSM in the same orientation. Unlike the hysteresis nonlinearity of a single piezoelectric ceramic, which is symmetric or asymmetric, the FSM exhibits complex hysteresis characteristics. In this paper, a systematic way of modeling the hysteresis nonlinearity of FSMs is proposed using a Madelung’s rules based symmetric hysteresis operator with a cascaded neural network. The hysteresis operator provides a basic hysteresis motion for the FSM. The neural network modifies the basic hysteresis motion to accurately describe the hysteresis nonlinearity of FSMs. The wiping-out and congruency properties of the proposed method are also analyzed. Moreover, the inverse hysteresis model is constructed to reduce the hysteresis nonlinearity of FSMs. The effectiveness of the presented model is validated by experimental results.

scholarly journals Rethinking the Random Cropping Data Augmentation Method Used in the Training of CNN-Based SAR Image Ship Detector

2020 ◽  
Vol 13 (1) ◽  
pp. 34
Author(s):  
Rong Yang ◽  
Robert Wang ◽  
Yunkai Deng ◽  
Xiaoxue Jia ◽  
Heng Zhang
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Back Propagation ◽  
Detection Performance ◽  
Training Data ◽  
Sar Image ◽  
Optical Images ◽  
The Neural Network ◽  
Effective Training ◽  
Standard Configuration

The random cropping data augmentation method is widely used to train convolutional neural network (CNN)-based target detectors to detect targets in optical images (e.g., COCO datasets). It can expand the scale of the dataset dozens of times while consuming only a small amount of calculations when training the neural network detector. In addition, random cropping can also greatly enhance the spatial robustness of the model, because it can make the same target appear in different positions of the sample image. Nowadays, random cropping and random flipping have become the standard configuration for those tasks with limited training data, which makes it natural to introduce them into the training of CNN-based synthetic aperture radar (SAR) image ship detectors. However, in this paper, we show that the introduction of traditional random cropping methods directly in the training of the CNN-based SAR image ship detector may generate a lot of noise in the gradient during back propagation, which hurts the detection performance. In order to eliminate the noise in the training gradient, a simple and effective training method based on feature map mask is proposed. Experiments prove that the proposed method can effectively eliminate the gradient noise introduced by random cropping and significantly improve the detection performance under a variety of evaluation indicators without increasing inference cost.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

2009 ◽  
Vol 610-613 ◽  
pp. 450-453
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Jin Zhang ◽  
Gui Ping He
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Bending Fatigue ◽  
The Neural Network ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

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