Sensitivity Analysis of Distress Influence Factors on Steel-Plastic Compound Reinforced Retaining Wall

2019 ◽  
Vol 48 (6) ◽  
pp. 20180632
Author(s):  
Bin-Shuang Zheng ◽  
Xiao-Ming Huang ◽  
Run-Min Zhao ◽  
Jia-Ying Chen ◽  
Wei-Guang Zhang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Retaining Wall ◽  
Influence Factors ◽  
Reinforced Retaining Wall

scholarly journals Mechanical Behavior of the Reinforced Retaining Wall Subjected to Static Load

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yong Liu ◽  
Zhanyong Yao ◽  
Hongzhe Liu ◽  
Mingxia Shao ◽  
Yulong Zhao
Keyword(s):  
Mechanical Behavior ◽  
Static Load ◽  
Retaining Wall ◽  
Influence Factors ◽  
Horizontal Displacement ◽  
Maximum Tensile Stress ◽  
Wall Panel ◽  
Element Analysis ◽  
Panel Stiffness ◽  
Reinforced Retaining Wall

To study the mechanical behavior and influence factors of the reinforced retaining wall under the static load, numerical simulation of the reinforced retaining wall is conducted by finite element analysis, and its mechanical behavior and influencing methods are studied in accordance with relevant theories. The results showed that the properties of back fill, reinforced spacing, reinforced stiffness, reinforced length, and panel stiffness all affect the mechanical behavior of retaining walls. According to the example calculations of different wall heights, the distribution of panel horizontal displacement and maximum tensile stress are analyzed. The gravel with good gradation has better durability and can reduce the amount of reinforcing steel; with the decrease of the reinforcement spacing, the deformation of the wall panel will become smaller, and the reinforcement effect will be improved; the length of reinforcement is not the longer the better, and the deformation of wall panel can be minimized at the suitable length; the larger the elastic modulus of the wall panel, the smaller the deformation of the wall panel will be.

Kriging-Based Reliability Analysis and Variance-Based Sensitivity Analysis for an Idler Shaft

2015 ◽  
Author(s):  
Weimin Cui ◽  
Wei Guo ◽  
Zhongchao Sun ◽  
Tianxiang Yu
Keyword(s):  
Sensitivity Analysis ◽  
Influence Factors ◽  
Random Variables ◽  
Kriging Model ◽  
Element Analysis ◽  
Performance Function ◽  
The Finite Element Analysis ◽  
Inner Diameter ◽  
Sensitivity Method ◽  
The Relationship

In order to analyze the reason of failure and improve the reliability of the idler shaft, this paper studies the reliability and sensitivity for the idler shaft based on Kriging model and Variance Methods respectively. The finite element analysis (FEA) of idler shaft is studied in ABAQUS firstly. Then, combining the performance function and various random variables, the Kriging model of idler shaft is established and verified. Based on Kriging model which has been established, the relationship between random variables and the response value is studied, and the function reliability is calculated which explains why the failure of the idler shaft occurred frequently in service. Finally, the variance-based sensitivity method is used for sensitivity analysis of influence factors, the result shows that the reliability of idler shaft is sensitive to the inner diameter of body A and inner diameter of body B, which could contribute for the analysis and further improvement of idler shaft.

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.

Numerical analysis of geocell-reinforced retaining wall failure modes

2018 ◽  
Vol 46 (3) ◽  
pp. 284-296 ◽  
Author(s):  
Fei Song ◽  
Huabei Liu ◽  
Liqiu Ma ◽  
Hongbing Hu
Keyword(s):  
Numerical Analysis ◽  
Failure Modes ◽  
Retaining Wall ◽  
Reinforced Retaining Wall
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