Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model's validity.Keywords Slope stability . Artificial neural network . Nonlinear . NoabadErosion of a slope by a river or other natural agent This erosion can take place at the toe of the slope or along a weaker layer in it. It can happen due to the effect of a tidal current in a river involving sediment transport. The process Arab J Geosci (

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Over the last few years, artificial neural networks (ANNs) have been used successfully for modelling all aspects of geotechnical engineering problems. ANNs are a form of artificial intelligence which attempt to mimic the function of the human brain and nervous system. ANNs are well suited to model the complex behaviour of most geotechnical engineering problems. The purpose of this paper was to assess the layering of subsurface soil using ANNs. Assessing the structure of soil layers on a site, depending on the extent of the study area, requires drilling several boreholes and performing several tests which demand considerable time and money. Increasing the knowledge of soil layer properties between boreholes leads to improved understanding of the near-surface geology. ANNs learn from data examples presented to them in order to capture the subtle functional data relationships, even if the underlying relationships are unknown or the physical meaning is difficult to explain. This paper focuses on the information gathered from the boreholes in a range of 40 square kilometres of Babol City in the north of Iran. The data was collected and classified in order to determine the characteristics of the soil layers. To later classify the different layers at different depths and to determine the thickness of each layer at a specified depth, multi-layer neural networks were trained separately. To quantify the neural network performance in estimating the changes of soil layers, some data from the test boreholes was presented to the network for the first time, and the results of neural networks were compared with actual data obtained from site investigations. The results show a high degree of accuracy in prediction by ANN models.

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