Influence of Meteorological Parameters on Explosive Charge and Stemming Length Predictions in Clay Soil during Blasting Using Artificial Neural Networks

Ground vibration is one of the most unfavourable environmental effects of blasting activities, which can cause serious damage to neighboring homes and structures. As a result, effective forecasting of their severity is critical to controlling and reducing their recurrence. There are several conventional vibration predictor equations available proposed by different researchers but most of them are based on only two parameters, i.e., explosive charge used per delay and distance between blast face to the monitoring point. It is a well-known fact that blasting results are influenced by a number of blast design parameters, such as burden, spacing, powder factor, etc. but these are not being considered in any of the available conventional predictors and due to that they show a high error in predicting blast vibrations. Nowadays, artificial intelligence has been widely used in blast engineering. Thus, three artificial intelligence approaches, namely Gaussian process regression (GPR), extreme learning machine (ELM) and backpropagation neural network (BPNN) were used in this study to estimate ground vibration caused by blasting in Shree Cement Ras Limestone Mine in India. To achieve that aim, 101 blasting datasets with powder factor, average depth, distance, spacing, burden, charge weight, and stemming length as input parameters were collected from the mine site. For comparison purposes, a simple multivariate regression analysis (MVRA) model as well as, a nonparametric regression-based technique known as multivariate adaptive regression splines (MARS) was also constructed using the same datasets. This study serves as a foundational study for the comparison of GPR, BPNN, ELM, MARS and MVRA to ascertain their respective predictive performances. Eighty-one (81) datasets representing 80% of the total blasting datasets were used to construct and train the various predictive models while 20 data samples (20%) were utilized for evaluating the predictive capabilities of the developed predictive models. Using the testing datasets, major indicators of performance, namely mean squared error (MSE), variance accounted for (VAF), correlation coefficient (R) and coefficient of determination (R2) were compared as statistical evaluators of model performance. This study revealed that the GPR model exhibited superior predictive capability in comparison to the MARS, BPNN, ELM and MVRA. The GPR model showed the highest VAF, R and R2 values of 99.1728%, 0.9985 and 0.9971 respectively and the lowest MSE of 0.0903. As a result, the blast engineer can employ GPR as an effective and appropriate method for forecasting blast-induced ground vibration.

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Prediction of Blast-Induced Ground Vibration at a Limestone Quarry: An Artificial Intelligence Approach

Arthur

Bhatawdekar

Mohamad

et al. 2022

Applied Sciences

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Application Software Developed for the Determination of Expansion Volume in Clay Soil Generated by the Detonation of an Explosive Charge

Težak

Dobrilović

et al. 2022

Geosciences

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Blasting in clay soil in the field of anchoring and foundation of objects and structures has its benefits in construction and geotechnical practice. The foundations of the method lay in the fact that a shock wave is generated when the explosive charge is detonated. The shock wave, with high pressure at the wavefront, causes the natural structure of the clay soil to be destroyed, and a spherical expansion in the clay mass is formed. The presented research is focused on determining the shape and volume of the resulting expansion in test blasts performed with several types of explosives. An application named Borehole was developed to determine the resulting spherical expansion formed after the detonation of an explosive charge with the integration of the GNSS method of measurement, depth camera, and laser. The application Borehole calculates expansion volume based on the coordinates obtained with the GNSS and the laser-obtained distance of the formed expansion and provides a graphical interpretation in 2D and 3D views. Additionally, when developing the application Borehole, compatibility with CAD tools was considered, primarily for better verification and a more detailed graphical interpretation of 3D views. The developed method allows for simple determination of the volume and dimension of the spherical expansion in clay soil with acceptable accuracy for the design and building of geotechnical structures constructed above and underground.

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Influence of Meteorological Parameters on Explosive Charge and Stemming Length Predictions in Clay Soil during Blasting Using Artificial Neural Networks

Cited by 2 publications

References 25 publications

Prediction of Blast-Induced Ground Vibration at a Limestone Quarry: An Artificial Intelligence Approach

Prediction of Blast-Induced Ground Vibration at a Limestone Quarry: An Artificial Intelligence Approach

Application Software Developed for the Determination of Expansion Volume in Clay Soil Generated by the Detonation of an Explosive Charge

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