Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability

2015 ◽  
Vol 22 (5) ◽  
pp. 1878-1891 ◽  
Author(s):  
Hamed Farshbaf Aghajani ◽  
Hossein Salehzadeh ◽  
Habib Shahnazari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Slope Stability ◽  
Friction Angle ◽  
Anisotropic Friction ◽  
Artificial Neural

Freeway Slope Stability Evaluation Based on Multi-Level Fuzzy Neural Network

2012 ◽  
Vol 430-432 ◽  
pp. 1700-1703
Author(s):  
Yan Kai Wu ◽  
Xian Song Sang ◽  
Bin Niu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Slope Stability ◽  
Basic Principle ◽  
Fuzzy Neural Network ◽  
Stability Assessment ◽  
High Slope ◽  
Fuzzy Neural ◽  
Multi Level ◽  
Artificial Neural

On the basis of introduced basic principle of fuzzy-artificial neural network, this article constructed a slope stability assessment index system with multi-level fuzzy neural network, and made detailed evaluation criterion according to the assessment characteristics of slope stability. Through introducing the basic principle of multi-level comprehensive assessment from fuzzy mathematics and artificial neural network theory, it overcomes the defect of difficult to be quantified in evaluation process of slope stability. Therefore, it can be better to deal with some uncertain problems occurred in the slope stability assessment process, and as much as possible to express all factors influencing slope stability really and objectively. We selected 20 single factor evaluation indexes to assess slope stability based on surveying the high slope stability in Mian county-Ningqiang county freeway section. It took "normal distribution model function" as a membership function to develop a program with the model of fuzzy neural network. Furthermore, we took 30 typical slope examples as training sample to conduct effectiveness test and feedback test for the program. After the precision requirement was met, we used the program to evaluate 21 high slope examples and compared the results with the ones solved by traditional mechanical methods. The coincidence degree by using two kinds of methods to assess the same slope stability is 76.2%.

scholarly journals Assessment of shallow landslide susceptibility using an artificial neural network in Enshi region, China

2017 ◽  
Author(s):  
Bin Zeng ◽  
Wei Xiang ◽  
Joachim Rohn ◽  
Dominik Ehret ◽  
Xiaoxi Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spatial Distribution ◽  
Slope Stability ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Evaluation Index System ◽  
Mechanism Analysis ◽  
Unstable Slope ◽  
Artificial Neural

Abstract. Landslides are one of the most common and damaging natural hazards in mountainous areas. However, due to the complex mechanisms that influence the activation of landslides, it can often be very difficult to predict exactly when a landslide will occur. Therefore, research on landslide prevention and mitigation mainly focuses on the distribution forecasting of unstable slopes that are prone to landslides in specific regions and under multiple external forces. The prediction of the spatial distribution of these unstable slopes, termed Landslide Susceptibility Zonation, is important in helping with government land-use planning and in reducing unnecessary loss of life and property. Researching unstable slopes in the Silurian stratum in Enshi region, China, this investigation established a GIS and artificial neural network (ANN)-based method to predict the distribution of potential landslides in this area. Based on the failure mechanism analysis of typical landslides in Silurian stratum, development of evaluation index system which represents the most relevant factors that influence the slope stability, and establishment of intelligent slope stability susceptibility prediction model by artificial neural network, the spatial distribution of unstable slope zones that are prone to landslides were predicted in the study area. The results were further well supported from remote sensing data and field investigations. This research proves that the spatial unstable slope prediction method based on intelligence theory and GIS technology is accurate and reliable.

scholarly journals Implementation of artificial neural network technique in the simulation of dam breach hydrograph

2011 ◽  
Vol 14 (2) ◽  
pp. 478-496 ◽  
Author(s):  
Vahid Nourani ◽  
Habib Hakimzadeh ◽  
Alireza Babaeyan Amini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Friction Angle ◽  
Earthen Dam ◽  
Dam Breach ◽  
Physically Based ◽  
Laboratory Modelling ◽  
Artificial Neural ◽  
Peak Outflow ◽  
Artificial Neural Network Ann

In the present study, two artificial neural networks were developed to simulate outflow hydrograph from earthen dam breach. The required data for the modelling were collected from literature, laboratory experiments and a physically based model (i.e. BREACH). For the laboratory modelling, five different materials were used for the construction of different dams of various sizes, and the process of the breach was recorded by two video cameras to record the breach growth as well as the output hydrograph. The genetic algorithm was also applied to divide the data into three statistically similar sub-sets for training, validation and test purposes. The obtained results demonstrate that the results of the artificial neural network (ANN) method are in good agreement with the observed values, and this method produces better results than existing classical methods. Also, the experiments show when cohesive strength is larger, the breach process becomes slower, and the peak outflow and the final width and depth of breach become smaller. Moreover, when the friction angle is larger, the breach process becomes slower, and the peak outflow and the final width and depth of breach become smaller. However, the rate of breach formation is particularly dependent upon the soil properties.

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