scholarly journals RESEARCH ON THE PREDICTION OF DEFORMATION PROPERTIES BASED ON NEURAL NETWORK

2021 ◽  
Vol 2078 (1) ◽  
pp. 012057
Author(s):  
Lu Jin ◽  
Chunli Zhang ◽  
Xiangyuan Meng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Direct Analysis ◽  
Steel Frame ◽  
Second Order ◽  
Absolute Difference ◽  
Lateral Deformation ◽  
Analysis Method ◽  
Support Structure ◽  
Deformation Properties

Abstract The direct analysis method based on deformation properties needs to predict the lateral deformation of the steel frame-support structure in advance, but it takes time and labor to use both finite element numerical simulation and experimental research. In this paper, the BP neural network receives the parameters of the steel frame-support structure, such as the aspect ratio, the second-order effect parameters, the slenderness ratio of frame columns, the rigidity ratio of beam-column bus and the deformation limit, with the lateral displacement of the column as the output layer, the second-order lateral deformation properties of the steel frame-support structure can be quickly obtained. Comparing the prediction results of BP neural network with those of the existing fitting formulas, the absolute difference and the relative error ratio between them are very small, which shows that the accuracy of the second-order column roof migration using the BP neural network prediction is high. The BP neural network can be used to judge the applicability of the direct analysis method based on the deformation properties of the steel frame-support structure under various parameters.

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.

scholarly journals Error analysis method of intelligent electricity meter based on improved BP neural network

2021 ◽  
Vol 1920 (1) ◽  
pp. 012002
Author(s):  
Xiangyu Yuan ◽  
Penghe Zhang ◽  
Ning Li ◽  
Jiasheng Xu ◽  
Suqin Xiong
Keyword(s):  
Neural Network ◽  
Error Analysis ◽  
Bp Neural Network ◽  
Analysis Method

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.

scholarly journals CONSIDERATIONS ON THE DIFFUSE SEISMICITY ASSUMPTION

2015 ◽  
Vol 12 (2) ◽  
Author(s):  
Jorge Daniel Riera ◽  
Ignacio Iturrioz
Keyword(s):  
Steel Structures ◽  
Direct Analysis ◽  
Second Order ◽  
Effective Length ◽  
Order Effects ◽  
Analysis Method ◽  
Order Analysis ◽  
3D Space ◽  
Effective Length Method ◽  
Diffuse Seismicity

Second-order effects were historically included by the effective length method (K concept). All the studies about that methodology have been developed in frame plane, with regular rectangular frames. The new way to include those effects is the use of second-order analysis, direct analysis method or alternative simplified options. This methodology was included in ANSI AISC360 in the 2005 version and in the 2010 version. As before, the studies already developed for DAM analysis are in plane. In this paper, the K concept is revisited by numerical analysis, and extended to the 3D space. Using models of symmetric and non-symmetric industrial steel structures in plane, 3D stability analyses were developed, and the results were compared with plane behavior. Several conclusions and recommendations were exposed, resulting from the analyzed models. Keywords: Second-order analyses, steel structures, irregular 3D frames.

An Implementation of First and Second Order Neural Network Classification on Potential Drug Addict Repetition

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Nazri Mohd Nawi ◽  
◽  
Eneng Tita Tosida ◽  
Hamiza Hasbi ◽  
Norhamreeza Abdul Hamid ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gradient Descent ◽  
Back Propagation ◽  
Drug Addict ◽  
Second Order ◽  
Learning Performance ◽  
Neural Network Classification ◽  
Minimization Methods ◽  
Bp Networks

Back propagation (BP) neural network is known for its popularity and its capability in prediction and classification. BP used gradient descent (GD) method as one of the most widely used error minimization methods used to train back propagation (BP) networks. Besides its popularity BP still faces some limitation such as very slow in learning as well as easily get stuck at local minima. Many techniques have been introduced to improve BP performance. This research implements second order method together with gradient descent in order to improve its performance. The efficiency of both methods are verified and compared by means of simulations on classifying drug addict repetition. The results show that the second order methods are more reliable and significantly improves the learning performance of BP.

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