Artificial neural networks and support vector machines for more accurate cost estimation in underground mining: A contractor's viewpoint

Accurate cost estimation is crucial in effective decision-making and evaluation in underground mining projects. Machine learning techniques have shown enormous potential in enhancing cost estimation accuracy in various industries. This study harnesses artificial neural networks (ANN) and Support Vector Machines (SVM) to estimate operating costs in underground mining. Special emphasis is placed on cost estimation from a contractor’s perspective. Mining contractors are sensitive to deviations from the estimated costs because slight deviations may result in losing a contract bid or financial loss in an awarded project. The proposed approach can help contractors make more informed decisions and improve project management. Comprehensive data containing various parameters that impact the cost of underground mining projects, such as equipment type utilization, rock type, and cross-sectional area, were collected. This dataset was used to train and evaluate ANN and SVM models that provide more accurate cost estimation for underground mining projects. The best model achieved a mean average percentage error (MAPE) of 5.31 % for the ANN model and 3.05 % for the SVM model, outperforming traditional cost estimation methods. This study demonstrates the potential of machine learning in enhancing the performance of the cost estimation process.