Sensitivity Analysis of Slope Stability Influence Factors Based on BP Neural Network

2014 ◽  
Vol 1010-1012 ◽  
pp. 1544-1547 ◽  
Author(s):  
Chuang Ye Wang ◽  
Fei Zhang ◽  
Wan Dong Han
Keyword(s):  
Neural Network ◽  
Slope Stability ◽  
Bp Neural Network ◽  
Influence Factor ◽  
Pressure Coefficient ◽  
Slope Angle ◽  
Influence Factors ◽  
Friction Angle ◽  
Analysis Model ◽  
Rock Bulk

There are many factors which influence the slope stability. In order to analyze the degree of importance of each influence factor on slope stability, this paper establishes a slope stability analysis model based on BP neural network. The computation results showed that the model was reasonable and reliable. On this basis, the sensitivity of various influence factors to slope stability was analyzed by single-factor test, which were internal friction angle of rock, bulk density, pore pressure coefficient, slope angle, rock cohesion and slope height in a descending order of sensitivity.

scholarly journals Music Emotion Analysis Based on PSO-BP Neural Network and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen Xi
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Emotional Expression ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Big Data Analysis ◽  
Emotion Expression ◽  
Analysis Model ◽  
Network Algorithm ◽  
Neural Network Algorithm

The current music teaching can effectively improve students’ music emotional expression indirectly. How to use the PSO-BP neural network to realize the quantitative research of music emotional expression is the current development trend. Based on this, this paper studies the influence factors of music emotion expression based on PSO-BP neural network and big data analysis. Firstly, a music emotion expression analysis model based on PSO-BP neural network algorithm is proposed. The autocorrelation function is used to simulate the emotion expression information in music. Through the maximum value of the autocorrelation function curve in the detection process, the vocal music signal is restored, and then the emotion expressed is analyzed. Secondly, the influence factors of PSO-BP neural network algorithm in music emotion expression are analyzed. The improved PSO-BP neural network algorithm and multidimensional data model are used for comprehensive analysis to accurately analyze the emotion in music expression, and the fuzzy evaluation method and analytic hierarchy process are used for quality evaluation. Finally, the validity of the music emotion analysis model is verified by many experiments.

Research on Applied-Information Technology with PM2.5 Generation and Evolution Model Based on BP Neural Network

2014 ◽  
Vol 1003 ◽  
pp. 226-229 ◽  
Author(s):  
Ying Hong Xie ◽  
Xiao Wei Han ◽  
Qi Li
Keyword(s):  
Neural Network ◽  
Information Technology ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Evolution Model ◽  
Good Reliability ◽  
Bp Neural Network Model ◽  
Near Future ◽  
Xi’An City

In this paper, BP neural network model is used to establish the occurrence and evolution model of PM2.5 in an area in Xi'an city. In the model, wind, humidity, season, SO2,NO2,PM10, CO,O3 (in one hour ) and O3 (in eight hours ) and other influence factors are all considered. The model has good reliability, it can accurately forecast the value of PM2.5 and its variation in the near future, which can provide the basis for the PM2.5 control.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

scholarly journals Sensitivity Analysis of Influencing Factors of Building Slope Stability Based on Orthogonal Design and Finite Element Calculation

2018 ◽  
Vol 53 ◽  
pp. 03076
Author(s):  
RUAN Jin-kui ◽  
ZHU Wei-wei
Keyword(s):  
Finite Element ◽  
Slope Stability ◽  
Design Method ◽  
Orthogonal Design ◽  
Slope Angle ◽  
Friction Angle ◽  
Range Analysis ◽  
Factors Affecting ◽  
Orthogonal Test Design ◽  
The Stability

In order to study the sensitivity of factors affecting the homogeneous building slope stability, the orthogonal test design method and shear strength reduction finite element method were used. The stability safety factor of the slope was used as the analysis index, and the range analysis of results of 18 cases were carried out. The results show that the order of sensitivity of slope stability factors is: internal friction angle, slope height, cohesion, slope angle, bulk density, elastic modulus, Poisson's ratio. The analysis results have reference significance for the design and construction of building slope projects.

scholarly journals Inversion for Damping Ratio of Flat Blade Based on BP Neural Network

Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 547
Author(s):  
Xiamei Zhang ◽  
Shudan Xia
Keyword(s):  
Neural Network ◽  
Decay Time ◽  
Bp Neural Network ◽  
Damping Ratio ◽  
Drop Test ◽  
Analysis Model ◽  
Bp Network ◽  
The Real ◽  
Neural Network Inversion ◽  
The Impact

Aero engine is impacted by foreign objects frequently during daily usage, including runway gravel, birds, fuselage components and so on, so the fan and compressor may damage, resulting in serious air crash. Thus, simulating the impact of blades and establishing the numerical analysis model of dynamic response demand immediate attention. In the analysis model, damping coefficient is one of the most important physical parameters of the blade structure and cannot be directly measured. Rayleigh damping is widely applied and can be converted to direct modal damping in ABAQUS. BP neural network is a multi-layer feedforward neural network using back propagation algorithm to adjust the network weights. It can be proved that there exists a three-layer BP network to realize the mapping of arbitrary continuous functions with arbitrary precision. In this study, a novel method for obtaining the damping ratio of the flat blade which applies BP neural network inversion is proposed. In order to demonstrate this method, a simplified experiment was conducted. Firstly, fix a section of aluminum plate and then conduct two set of drop tests on different positions with different impact velocities by a steel ball. At the same time, vibration response was recorded by displacement sensor. Secondly, establish a finite element model using ABAQUS to simulate the drop test. Adopt twenty groups of models with different damping ratio and then obtain their amplitudes and decay time, respectively. Thirdly, train a BP neural network using MATLAB program and then establish the mapping relationship between amplitude, decay time and damping ratio. Fourth, a set of experimental amplitude and decay time is substituted into the previously obtained BP neural network mapping model, and then the real damping ratio is obtained by inference. Finally, the real damping ratio is applied to the flat blade impact simulation of the other set of drop test for validation. The numerical results are consistent with the experimental data, which indicates that the damping ratio obtained by BP neural network inversion is reasonable and reliable.

BP Neural Network Model on Choice of Project Manager for Highway Slope Treatment

2014 ◽  
Vol 505-506 ◽  
pp. 274-277
Author(s):  
Bin Wang ◽  
Yong Tao Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Theory ◽  
Influence Factors ◽  
Project Manager ◽  
Slope Treatment ◽  
Neural Network Theory ◽  
Bp Neural Network Model

To get the quantified indexes of comprehensive capacity about project manager, based on the modal on artificial neural network theory, different influence factors about choice of project manager for highway slope treatment were analyzed , identified, quantified and evaluated , then comprehensive capacity of the manager were analyzed. Such procedure provided a new method for choice of project manager for highway slope treatment.

The Research of Ground Deformation Prediction Neural Network Model on City Underground Construction

2014 ◽  
Vol 926-930 ◽  
pp. 708-711
Author(s):  
Wen Xin Zhu
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Influence Factor ◽  
Ground Deformation ◽  
Influence Factors ◽  
Shield Tunnel ◽  
Construction Technology ◽  
Underground Construction ◽  
Deformation Prediction ◽  
Main Influence

This paper sees the underground construction shield tunnel and foundation trench as study object. It has analyzed disturbance mechanism and ground deformation mechanism, which were caused by shield tunnel and building pit construction. Through the system analysis of ground deformation influence factor, it has confirmed main influence factors, like geological environment condition of overall consideration, physical parameter and construction technology. And it has established ground deformation prediction model based on neural network. Then it has made sensitivity analysis of affecting ground deformation factor by neural network hierarchical analytical approach.

Predicting Model of Safe Distance between Tunnel and Karst Cave

2013 ◽  
Vol 470 ◽  
pp. 862-865
Author(s):  
Yong Biao Lai ◽  
Meng Shu Wang ◽  
Cheng Uang Bai
Keyword(s):  
Poisson Ratio ◽  
Pressure Coefficient ◽  
Influence Factors ◽  
Friction Angle ◽  
Support Vector ◽  
Karst Cave ◽  
Safe Distance ◽  
Predicting Model ◽  
Rock Density ◽  
Concealed Karst Cave

A method of predicting safe distance between tunnel and karst cave based on support vector machine was proposed, 10 parameters (rock density,elasticity modulusE, poisson ratio, friction angle, cohesionC, lateral pressure coefficient , the karst cave spanL,rate between height and span of the Karst caveR, tunnel deepH,cave position) were chosen for safe distance influence factors, then an intelligent predicting model of safe distance between tunnel and karst cave was built, this predicting model can predict 7 kinds position of safe distance between tunnel and concealed karst cave and was feasible and high prediction precision, which was verified by engineering example.

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