Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration

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

Analytical Damage Model for Predicting Coal Failure Stresses by Utilizing Acoustic Emission

Overburden collapse and water inrush in mines are primarily caused by rock fractures. Mining safety can be enhanced by monitoring and identifying early signs of coal failure in the mines. This article collected acoustic emission data synchronously throughout a series of uniaxial compression (UC) experiments on natural and water-saturated coal. The influence mechanisms of water, mechanical properties, and acoustic emission signals on the stress–strain curve and the SEM results of water-saturated and dry samples are investigated. As a result, the mechanical properties of coal are not only weakened by water saturation, such as elastic modulus, strain, stress, and compressive strength but also reduced acoustic emissions. In comparison with saturated coal, natural coal has a uniaxial stress of 13.55 MPa and an elastic modulus of 1.245 GPa, while saturated coal has a stress of 8.21 MPa and an elastic modulus of 0.813 GPa. Intergranular fractures are more likely to occur in coal with a high water content, whereas transgranular fractures are less likely to occur in coal with a high water content. An innovative and unique statistical model of coal damage under uniaxial loading has been developed by analyzing the acoustic emission data. Since this technique takes into account the compaction stage, models based on this technique were found to be superior to those based on lognormal or Weibull distributions. A correlation coefficient of greater than 0.956 exists between the piecewise constitutive model and the experimental curve. Statistical damage constitutive models for coal are compatible with this model. Additionally, the model can precisely forecast the stress associated with both natural and saturated coal and can be useful in the prevention of rock-coal disasters in water conditions

Assessment of the ground vibration during blasting in mining projects using different computational approaches

Abstract The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model to assess the ground vibrations during blasting in mining projects. The long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision tree (DT), Gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR) models are employed using 162 data points. For the first time, the blackhole-optimized LSTM model has been used to predict the ground vibrations during blasting. Fifteen performance metrics have been implemented to measure the prediction capabilities of computational models. The study concludes that the blackhole optimized-LSTM model PPV11 is highly capable of predicting ground vibration. Model PPV11 has assessed ground vibrations with RMSE = 0.0181 mm/s, MAE = 0.0067 mm/s, R = 0.9951, a20 = 96.88, IOA = 0.9719, IOS = 0.0356 in testing. Furthermore, this study reveals that the prediction accuracy of hybrid models is less affected by multicollinearity because of the optimization algorithm. The external cross-validation and literature validation confirm the prediction capabilities of model PPV11. The ANOVA and Z tests reject the null hypothesis for actual ground vibration, and the Anderson–Darling test rejects the null hypothesis for predicted ground vibration. This study also concludes that the GPR and LSSVM models overfit because of moderate to problematic multicollinearity in assessing ground vibration during blasting

Effect of rock properties on wear and cutting performance of multi blade circular saw with iron based multi-layer diamond segments

This study is an attempt for comprehensive, combining experimental data with advanced analytical techniques and machine learning for a thorough understanding of the factors influencing the wear and cutting performance of multi-blade diamond disc cutters on granite blocks. A series of sawing experiments were performed to evaluate the wear and cutting performance of multi blade diamond disc cutters with varying diameters in the processing of large-sized granite blocks. The multi-layer diamond segments comprising the Iron (Fe) based metal matrix were brazed on the sawing blades. The segment’s wear was studied through micrographs and data obtained from the Field Emission Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDS). Granite rock samples of nine varieties were tested in the laboratory to determine the quantitative rock parameters. The contribution of individual rock parameters and their combined effects on wear and cutting performance of multi blade saw were correlated using statistical machine learning methods. Moreover, predictive models were developed to estimate the wear and cutting rate based on the most significant rock properties. The point load strength index, uniaxial compressive strength, and deformability, Cerchar abrasivity index, and Cerchar hardness index were found to be the significant variables affecting the sawing performance.Validerad;2024;Nivå 2;2024-04-04 (joosat);Funder: Deanship of Scientific Research at King Khalid University (RGP2/402/44);Full text license: CC BY</p

Assessment of Charge Initiation Techniques Effect on Blast Fragmentation and Environmental Safety: An Application of WipFrag Software

Blast charge initiation procedures have a significant impact on both mining safety and production rates. In this study, the inventory benefit of an electric initiation system was investigated to assess its influence on both fragmentation and blast-induced damages. The WipFrag software was used to examine the size distribution and productivity of 12 small-scale blasts initiated by both nonelectric and electric detonators. All blast rounds were initiated with plain-type electric and NONEL detonators. The average burden, spacing, stemming length, and charge weight were, respectively, 0.85 m, 1.10 m, 0.66 m, and 1.1 kg. The results showed that the mesh through which 80% of the blast fragments passed for the electric blast was smaller than the mesh through which the material products from the NONEL blast passed. The results also demonstrated that the generated blast-induced ground vibration (PPV) from all blast rounds for electric blast varied from 0.4–1.2 mm/s and 80–105 dB, while that for nonelectric blast ranged from 0.05–0.2 mm/s and 72–95 dB. As a result, the electric blast initiation technique was found to produce good fragmentation, with a higher percentage of optimum fragment sizes on spec than nonelectrically initiated blasts