Comparison between the performance of four metaheuristic algorithms in training a multilayer perceptron machine for gold grade estimation

Reserve evaluation is a very difficult and complex process. The most important and yet most challenging part of this process is grade estimation. Its difficulty derived from challenges in obtaining required data from the deposit by drilling boreholes, which is a very time consuming and costly act itself. Classic methods which are used to model the deposit are based on some preliminary assumptions about reserve continuity and grade spatial distribution which are not true about all kind of reserves. In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) is applied to solve the problem of ore grade estimation of highly sparse data from zarshouran gold deposit in Iran. The network is trained using four metaheuristic algorithms in separate stages for each algorithm. These algorithms are artificial bee colony (ABC), genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO). The accuracy of predictions obtained from each algorithm in each stage of experiments were compared with real gold grade values. We used unskillful value to check the accuracy and stability of each network. Results showed that the network trained with ABC algorithm outperforms other networks that trained with other algorithms in all stages having least unskillful value of 13.91 for validation data. Therefore, it can be more suitable for solving the problem of predicting ore grade values using highly sparse data

Improved RMR rock mass classification using artificial intelligence algorithms

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures