Performance Analysis of Bentonite-PVA Fiber Cement-Based Composites for Building Based on BP Neural Network

2020 ◽  
Vol 852 ◽  
pp. 209-219
Author(s):  
Zhe Shen
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Composite Materials ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Analysis Method ◽  
System Input ◽  
Prediction And Simulation ◽  
Pva Fiber

The paper will use BP neural network analysis method to study the thermal conductivity of bentonite and its influencing factors as a system. The heat conduction of bentonite was used as the output of the system, and its influencing factors were used as the system input to simulate. The corresponding simulation model was established to verify the thermal conductivity data. In addition, the analysis of the mechanical properties of the bentonite-PVA fiber cement-based composite materials for construction has not only laid a theoretical and realistic foundation for the prediction and simulation of the thermal conductivity of bentonite, but also has opened up the mechanical properties of the bentonite-PVA fiber cement-based composite materials a new path.

Study on Sensor with Mechanical Properties in Nuclear Power Plant with Application of BP Neural Network to Fault Tolerant Control

2013 ◽  
Vol 644 ◽  
pp. 56-59
Author(s):  
Jin Yang Li ◽  
Hong Xia ◽  
Shou Yu Cheng
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Level Sensor

All kinds of sensor with mechanical properties often can go wrong in nuclear power plant. In this kind of situation, it puts forward a kind of active fault tolerant control method based on the improved BP neural network. Firstly, the method will train sensor by BP neural network. Secondly, it will be established dynamic model bank in all kinds of running state. The system will be detected by using BP neural network real time. When the sensor goes wrong, it will be controled by reconstruction. Taking pressurizer water-level sensor as the case, a simulation experiment was performed on the nuclear power plant simulator. The results showed that the proposed method is valid for the fault tolerant control of sensor in nuclear power plant.

Research on Homogenization of Composite Materials

2013 ◽  
Vol 663 ◽  
pp. 426-430
Author(s):  
Zhen Yu Zhou ◽  
Qi Wen Xue
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Bp Neural Network ◽  
Pressure Vessels ◽  
Equivalent Material ◽  
Filament Wound ◽  
Filament Wound Composite ◽  
Output Information ◽  
Wound Composite ◽  
Composite Pressure Vessels

A numerical model is given to identify equivalent parameters of composite materials, using BP neural network algorithm. Taking Filament-wound composite pressure vessels as the research object, finite element models are first constructed .Getting node displacements as network training samples, the mechanical parameters as output information of network for effective training, the equivalent material parameters can be obtained. The satisfactory numerical validation is given and results show that the proposed method can identify the equivalent modulus and the equivalent Poisson’s ratio of the Filament-wound composite pressure vessels with precision. The computational efficiency is improved with BP neural network.

Prediction of Heating Processing-Properties of Ti2AlC/TiAl Compound Materials Based on BP Neural Network

2011 ◽  
Vol 306-307 ◽  
pp. 823-826
Author(s):  
Ming Wen ◽  
Yun Long Yue ◽  
Hai Tao Zhang ◽  
Yang Li
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Bp Neural Network ◽  
Bending Strength ◽  
Treatment Temperature ◽  
Preparation Technology ◽  
Bp Artificial Neural Network ◽  
Processing Properties ◽  
Compound Materials

Parameters of processing (heat treatment temperature, holding time) and properties (Bending strength and Microhardness) of Ti2AlC/TiAl compound materials were obtained through mechanical properties examination, the network model was built by BP artificial neural network. The results show that the built model can reflect the relationships between processing and properties very well and has certain accuracy. It can be used for the prediction of the properties of Ti2AlC/TiAl compound materials after heating processing under different experiment conditions. Meanwhile, the model can also serve as a guide for the preparation technology of Ti2AlC/TiAl compound materials.

Back-Analysis of Indoor Test Parameters for Rock-Fill of Shui Bu Ya CFRD

2011 ◽  
Vol 243-249 ◽  
pp. 4581-4586
Author(s):  
Lei Ming He ◽  
Li Hui Du ◽  
Jian Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Analysis ◽  
Soil Parameters ◽  
Model Parameters ◽  
Analysis Method ◽  
Filling Process ◽  
Rock Fill ◽  
Test Parameters ◽  
Displacement Back Analysis

In the numerical calculation of geotechnical project, it’s difficult to confirm the parameters because of the complexity and the uncertainty of them as the time is changing. However, the back-analysis provides us an effective way. Based on the result of the triaxial test on rock-fill of Shui Bu Ya CFRD, the thesis adopts the direct back-analysis method which combines the BP Neural Network and Genetic Algorithm to calculate the Tsinghua non-linear K-G model parameters of the rock-fill. The back-analysis parameters are used to simulate the filling process of Shui Bu Ya CFRD and predict the displacement of the dam. The thesis provides a technical reference for displacement back-analysis of soil parameters for CFRD.

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