A study of slope stability prediction using neural networks

Abstract

The determination of the non-linear behaviour of multivariate dynamic systems often presents a challenging and demanding problem. Slope stability estimation is an engineering problem that involves several parameters. The impact of these parameters on the stability of slopes is investigated through the use of computational tools called neural networks. A number of networks of threshold logic unit were tested, with adjustable weights. The computational method for the training process was a back-propagation learning algorithm. In this paper, the input data for slope stability estimation consist of values of geotechnical and geometrical input parameters. As an output, the network estimates the factor of safety (FS) that can be modelled as a function approximation problem, or the stability status (S) that can be modelled either as a function approximation problem or as a classification model. The performance of the network is measured and the results are compared to those obtained by means of standard analytical methods. Furthermore, the relative importance of the parameters is studied using the method of the partitioning of weights and compared to the results obtained through the use of Index Information Theory.

Monitoring and Predicting Slope Instability: A Review of Current Practices From a Mining Perspective

“…In machine learning, the neural network is a system inspired by the biological neural network and is used to estimate functions depending on a large number of unknown inputs. Although the artificial neural networks are a simplified version of the biological neural network, they retain enough of a structure to provide information of how biological neural networks might operate [27]. The neural network can learn to accumulate knowledge and experience from the unknown inputs received.…”

“…Neural networks can be categorized as supervised or unsupervised; a supervised neural network is trainedto produce the desired output in response to a set of inputs, whereas an unsupervised neural network is formed by letting the network continually adjusting to new inputs [27].…”

“…Using real-world data sets, Sakellarios and Ferentinou [27] applied the neural network theory to investigate the accuracy and flexibility of the method, for circular, plane and wedge failure mechanisms. Wang, W. Xu, and R Xu [28] used aback propagation neural network to evaluate the slope stability of the Yudonghe landslide.…”

Study for Application of Artificial Neural Networks in Geotechnical Problems

“…The factor of safety based on an appropriate geotechnical model as an index of stability, is required in order to evaluate slope stability. Black-box models, based on the Artificial Neural Networks (ANNs), currently attract many researchers studying slope instability, owing to their successful performance in modeling non-linear multivariate problems (Ni et al, 1995;Neaupane & Achet, 2004;Sakellariou & Ferentinou, 2005;Cho, 2009;Wang et al, 2005). Many variables are involved in slope stability evaluation and the calculation of the factor of safety requires geometrical data, physical data on the geologic materials and their shear-strength parameters (cohesion and angle of internal friction), information on pore-water pressures, etc.…”

Artificial neural network models for predicting soil thermal resistivity

“…The optimum number of neurons in the hidden layer of each model was determined by varying their number by starting with a minimum of 1 and then increasing the network size in steps by adding 1 neuron each time. Different transfer functions (such as log-sigmoid [44] and tan-sigmoid [13]) were investigated to achieve the best performance in training as well as in testing. Two momentum factors, μ (= 0.01 and 0.001), were selected for the training process to search for the most efficient ANN architecture; the maximum number of training epochs to train was chosen as 1000.…”