Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models’ features and to avoid the black box issue.

An earth pressure balance (EPB) TBM is used in soft ground conditions, and these conditions lead to the fluctuation and instability of machine parameters. Machine parameters influence cutter wear and tunnel excavation. For this reason, to evaluate and predict the cutter wear of an EPB TBM, a 1D CNN model was used to provide machine-parameter-based cutter wear prediction using an EPB TBM operational dataset. The machine parameters were split into 80% training and 20% test datasets. Compared to traditional machine learning applications and two deep neural network models, the proposed model provided reliable results with a reasonable computational time. The correlation coefficient was 89.6% $R^2$, the mean squared error (MSE) was 57.6, the mean absolute error (MAE) was 1.6, and the computational wall time was 3 min 22 s.

Ground vibration is one of the most hazardous outcomes of blasting. It has a negative impact both on the environment and the human population near to the blasting area. To evaluate the magnitude of blasting vibrations, it is important to consider PPV as a fundamental critical base parameter practice in terms of vibration velocity. This study aims to explore the application of different soft computing techniques, including a Gaussian process regression (GPR), decision tree (DT), and support vector regression (SVR), for the prediction of blast-induced ground vibration (PPV) in quarry mining. The three models were evaluated using classical mathematical evaluation metrics (R², RMSE, MSE, MAE). The result shows that the GPR model achieves an excellent prediction result; with R² = 0.94, RMSE = 0.0384, MSE = 0.0014, and MAE = 0.0265, it shows high accuracy in predicting PPV. The Shapley additive explanation (SHAP) results emphasize the importance of understanding the interactions between the various factors and their effects on the vibration assessment. The findings can inform the development of more sustainable and environmentally friendly models for predicting blasting vibrations. Using a GPR to simulate and predict blasting-induced ground vibrations is the study’s main contribution. The GPR can capture complicated, non-linear correlations in data, making it ideal for blast-induced ground vibrations, which are dynamic and nonlinear. By using a Gaussian process regression, we can help companies and researchers improve the safety and efficiency in blast-induced ground vibration environments.

The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m², a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices.

This work proposes an image-alignment method for multi-modal images (e.g., synthetic aperture radar (SAR) and optical satellite images) using an image-feature-based keypoint-matching algorithm. In applying the matching algorithm to multi-modal images, common features need to be obtained at the corresponding positions. However, the appearances of features among images are different. We solve this issue by translating the appearance of one modal image to the other using generative adversarial networks (GANs). In this work, we attempt to generate optical images from SAR images as a way to extract common features. Through an experiment, we confirm that the proposed method can estimate accurate correspondences between SAR and optical images.

Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m³ of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills.