Comparison and Validation of Landslide Susceptibility Maps Using an Artificial Neural Network Model for Three Test Areas in Malaysia

2010 ◽  
Vol 16 (2) ◽  
pp. 107-126 ◽  
Author(s):  
B. PRADHAN ◽  
M. F. BUCHROITHNER
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Landslide Susceptibility ◽  
Artificial Neural Network Model ◽  
Susceptibility Maps ◽  
Artificial Neural ◽  
Landslide Susceptibility Maps

Landslide susceptibility analysis using an artificial neural network model

2007 ◽  
Author(s):  
Shattri Mansor ◽  
Biswajeet Pradhan ◽  
Mohamed Daud ◽  
Normalina Jamaludin ◽  
Zailani Khuzaimah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Landslide Susceptibility ◽  
Artificial Neural Network Model ◽  
Susceptibility Analysis ◽  
Artificial Neural

scholarly journals Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3042
Author(s):  
Sheng Jiang ◽  
Mansour Sharafisafa ◽  
Luming Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rock Strength ◽  
Strain Rates ◽  
Filling Material ◽  
Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

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