Slope failure is a significant risk in both civil and mining operations. This failure phenomenon is more likely to occur during the high rainfall season, areas with a high probability of seismic activity and in cold countries due to freezing-thawing. Further, a poor understanding of hydrogeology and geotechnical factors can contribute to erroneous engineering designs. Several Limit Equilibrium Methods (LEMs) and numerical modelling tools have been developed over the years. However, the highlighted success of the Artificial Neural Networks (ANNs) in other disciplines/sectors has motivated researchers to implement ANNs to forecast the Factor Of Safety (FOS). This paper aims to develop ANNs to predict the value of the FOS for slopes formed by (i) uniform one soil/rock material and (ii) formed by two soil/rock materials. Each of these slopes contains three sub-models with 6, 7 and 8 input material parameters. Thousands of FOS values were generated for each sub-model using LEMs by randomly
Slope failure is a significant risk in both civil and mining operations. This failure phenomenon is more likely to occur during the high rainfall season, areas with a high probability of seismic activity and in cold countries due to freezing-thawing. Further, a poor understanding of hydrogeology and geotechnical factors can contribute to erroneous engineering designs. Several Limit Equilibrium Methods (LEMs) and numerical modelling tools have been developed over the years. However, the highlighted success of the Artificial Neural Networks (ANNs) in other disciplines/sectors has motivated researchers to implement ANNs to forecast the Factor Of Safety (FOS). This paper aims to develop ANNs to predict the value of the FOS for slopes formed by (i) uniform one soil/rock material and (ii) formed by two soil/rock materials. Each of these slopes contains three sub-models with 6, 7 and 8 input material parameters. Thousands of FOS values were generated for each sub-model using LEMs by rando
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