Prediction Of Blast-induced Ground Vibration Research Articles

Prediction of blast-induced ground vibration using multivariate statistical analysis in the opencast chromite mines of the Indian State of Odisha

Prediction of blast-induced ground vibration using multivariate statistical analysis in the opencast chromite mines of the Indian State of Odisha

Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach

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.

Prediction of Blast-Induced Ground Vibration Using Gene Expression Programming (GEP), Artificial Neural Networks (ANNs), and Linear Multivariate Regression (LMR)

Prediction of Blast-Induced Ground Vibration Using Gene Expression Programming (GEP), Artificial Neural Networks (ANNs), and Linear Multivariate Regression (LMR)

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.

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

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.

Prediction of Blast-Induced Ground Vibration Using Principal Component Analysis–Based Classification and Logarithmic Regression Technique

Prediction of Blast-Induced Ground Vibration Using Principal Component Analysis–Based Classification and Logarithmic Regression Technique

Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance

In this study, ground vibrations caused by blasting applications in a quarry were recorded and these values were evaluated and estimated by using an artificial neural network (ANN) model. Of the 28 vibration data measured, 20 were used for ANN training, 4 for validation and the remaining 4 for testing. In the model, peak particle velocity (PPV) was used as the output parameter, and the maximum explosive amount per delay and scaled distance were used as input parameters. In addition, MAPE, RMSE and R2 performance criteria were calculated from the realized, predicted by ANN and PPV values obtained from the field equation. The maximum amount of explosives used per delay and the sensitivity analysis of the scaled distance on the highest particle velocity were also determined. As a result, when the vibration data calculated from the field equation and estimated from the ANN model were compared with the realized vibration data, it was seen that the values obtained by the ANN model had a higher correlation.

Prediction of BlastInduced Ground Vibration (BIGV) of Metro Construction Using Difference Evolution AlgorithmOptimized Gaussian Process (DE-GP)

Rock blasting often has an irreversible impact on the surrounding environment and threatens the safety of life and property. Therefore, accurate prediction of blast-induced ground vibration (BIGV) is a prerequisite for safe construction. In view of the fact that traditional blasting peak particle velocity (PPV) empirical formulas cannot be accurately predicted, this study selected 88 sets of blasting monitoring data, based on distance from the blast-face, maximum charge per delay, total charge, hole depth, spacing, burden, stemming length, and powder factor being used as input variables and PPV being used as output variable to characterize BIGV. First, a nonlinear mapping relationship between input variables and output variable is established through the Gaussian process (GP). The differential evolution algorithm (DE) is used to optimize the hyperparameters σf, σn, and l of the GP, and a blasting PPV model based on the DE-GP is constructed. The proposed model is compared with the empirical formulas, least square support vector machine (LSSVM), artificial neural network (ANN), and GP model, and its prediction performance is evaluated by statistical indicators such as root mean square error (RMSE). Finally, the cosine amplitude method (CAM) is used to analyze the sensitivity of blasting parameters. The results show that the DE-GP algorithm for blasting vibration velocity prediction has higher precision and accuracy, which is significantly better than other models, and is the closest to the measured PPV. Distance from the blast-face, total charge, and maximum charge per delay have a greater impact on the prediction of PPV, while stemming length and powder factor have a smaller impact on the prediction of PPV. The DE-GP model proposed by this research has certain reference value for the prediction and control of PPV in blasting construction.

A Comparative Study of Machine Learning Methods for Prediction of Blast-Induced Ground Vibration

Blast-induced ground vibration (PPV) evaluation for a safe blasting is a long-established criterion used mainly by the empirical equations. However, the empirical equations are again considering a limited information. Therefore, using Machine Learning (ML) tools [Support Vector Machine (SVM) and Random Forest (RF)] can help in this context, and the same is applied in this work. A total of 73 blasts are monitored and recorded in this work. For the ML tools, the dataset is divided into the 80-20 ratio for the training and testing purposes in order to evaluate the performance capacity of the models. The prediction accuracies by the SVM and RF models in predicting the PPV values are satisfactory (up to 9% accuracy). The results obtained show that the coefficient of determination (R2) for RF and SVM is 0.81 and 0.75, respectively. Compared to the existing linear regressions, this work recommends using a machine learning regression model for the PPV prediction.

A comparative application of the Buckingham π (pi) theorem, white-box ANN, gene expression programming, and multilinear regression approaches for blast-induced ground vibration prediction

A comparative application of the Buckingham π (pi) theorem, white-box ANN, gene expression programming, and multilinear regression approaches for blast-induced ground vibration prediction

Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Prediction of blast-induced ground vibrations with the use of artificial neural networks. A case study in Greece.

Creating models, capable of making accurate predictions of the Peak Particle Velocity (PPV) after a blast, is always one of the researchers’ most important goals. The use of such prediction can assist in assessing the intensity of the blast vibrations and more importantly in designing the whole blasting phase so as to mitigate potential problems to nearby structures. Main aim of this paper is to demonstrate the capabilities of artificial intelligence applications in geotechnology and more specifically to assess PPV and the characteristics of the blast wave attenuation in an underground construction case study in Greece, using multilayer feed forward artificial neural networks (ANNs). The results showed that the forecasting ability of the developed ANNs was, in almost every case, more accurate than the ones given by the use of traditional empirical formulas, as benchmarked using Root Mean Squared Error (RMSE) and coefficient correlation (R). In this manner, the ANNs proved to be a reliable and accurate method to assess PPV from underground blasting and once trained they become an efficient off-the-shelf tool to assist engineers both in the blast design and in the mitigation of blast wave induced problems.

Suitability assessment of different vector machine regression techniques for blast-induced ground vibration prediction in Ghana

There is a collective demand for the mining industry to accurately predetermine or predict blast-induced ground vibration to support effective blast control management. It is, therefore, desirable to explore and develop accurate forecasting tools that can meet environmental and safety standards. In this study, the prediction capabilities of two proposed models, namely least squares support vector machine (LSSVM) and relevance vector machine (RVM) were explored and compared with support vector machine (SVM) which has found a wide application in blast-induced ground vibration prediction. The prediction results of these techniques were ranked to identify the best using mean square error (MSE), root-mean square error (RMSE), correlation coefficient (R) and model efficiency of Loague and Green (ELG). The ranking results revealed that LSSVM was superior to the SVM and RVM. The results produced by LSSVM model were further compared to three benchmark ANN methods (backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalised regression neural network (GRNN)) and five empirical predictor models (United State Bureau of Mines (USBM), Ambraseys-Hendron, Langefor-Kihlstrom, Indian standard and Central Mining Research Institute (CMRI)). The comparative analysis revealed that the LSSVM was the best prediction approach because it achieved the lowest MSE and RMSE results and recorded the highest R and ELG values of 0.0215, 0.1467, 0.8542 and 0.7273, respectively. The application of Bayesian Information Criterion (BIC) for model selection confirmed LSSVM as the best among all the methods applied because it produced the least BIC value of − 285.2312.

Blast-induced ground vibration prediction in granite quarries: An application of gene expression programming, ANFIS, and sine cosine algorithm optimized ANN

Blasting of rocks has intrinsic environmental impacts such as ground vibration, which can interfere with the safety of lives and property. Hence, accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the literature. Therefore, there is need to consider some robust predictive models for accurate prediction results. Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neural network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries. The input parameters into the models are the distance from the point of blasting to the point of measurement (D), the weight of charge per delay (W), rock density (ρ), and the Schmidt rebound hardness (SRH) value while peak particle velocity (PPV) is the targeted output. 100 datasets were used in developing the proposed models. The performance of the proposed models was examined using the coefficient of determination (R2) and error analysis. The R2 values obtained for the GEP, ANFIS, and SCA-ANN models are 0.989, 0.997, and 0.999, respectively, while their errors are close to zero. The proposed models are compared with an empirical model and are found to outperform the empirical model.

A Re-Look Into Modified Scaled Distance Regression for Prediction of Blast-Induced Ground Vibration

Underground blasts are conducted for deep excavations, tunneling, or mining activities. Scaled distance regression analysis is performed in industry to estimate peak particle velocity from charge weight and distance. For addressing the uncertainties in estimating safe charge weight for controlled blasting, 95% confidence expression is generally used. For addressing inaccuracies arising from superimposition of blast waves in multi-hole blasting when using attenuation equation developed from single-hole blast data, a modified approach was proposed in literature. This article presents comparisons to establish that industrial practice of scaled distance regression would be as satisfactory as the proposed modified approach, when various performance measures (including parsimony) are considered together. Furthermore, industrial practice of using 95% confidence expression generated from sufficient data (say, 40 numbers) would result in safe charge weight estimation, whereas modified scaled distance approach (mean expression) could still result in few non-conservative values.

Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System

Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The efficiency criteria chosen for the comparison between the predicted and actual data were the sum of squares due to error (SSE), the root mean squared error (RMSE), and the goodness of fit (R-squared and adjusted R-squared). The results show that the ANFIS prediction model performs better than the commonly used predictors.

A Self-adaptive differential evolutionary extreme learning machine (SaDE-ELM): a novel approach to blast-induced ground vibration prediction

Blast-induced ground vibration is still an adverse impact of blasting in civil and mining engineering projects that need much consideration and attention. This study proposes the use of Self-Adaptive Differential Evolutionary Extreme Learning Machine (SaDE-ELM) for the prediction of ground vibration due to blasting using 210 blasting data points from an open pit mine in Ghana. To ascertain the predictive performance of the proposed SaDE-ELM approach, several artificial intelligence and empirical approaches were developed for comparative purposes. The performances of various developed models were assessed using model performance indicators of mean squared error (MSE), Nash–Sutcliffe Efficiency Index (NSEI) and correlation coefficient (R). Furthermore, the Bayesian Information Criterion (BIC) was applied to select the best performing approach. The obtained prediction results based on the performance indicators showed that the SaDE-ELM outperformed all the competing models as it had the lowest MSE value of 0.01942, respectively. The SaDE-ELM also achieved the highest R and NSEI values of 0.8711 and 0.7537, respectively. The other artificial intelligent approaches had MSE, R and NSEI in the ranges of (0.02166–0.03006), (0.8012–0.8537) and (0.6188–0.7254), respectively. The empirical approaches performed poorly relative to the artificial intelligence approaches by having had MSE, R and NSEI in the ranges of (0.03419–0.06587), (0.7466–0.7833) and (0.1649–0.5665), respectively. The prediction superiority of SaDE-ELM was confirmed when it is achieved the lowest BIC value of − 293.40. Therefore, the proposed SaDE-ELM has demonstrated great potential to be used for on-site prediction, control and management of blast-induced ground vibration to prevent unwanted effects on the environment.

Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by Metaheuristic Algorithms

Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of two advanced machine learning models in predicting ground vibrations in a granite quarry located in Malaysia. To this end, two intelligent models were proposed by hybridizing the relevance vector regression (RVR) with the grey wolf optimization (GWO) (which formed the RVR-GWO model) and with the bat-inspired algorithm (BA) (which formed the RVR-BA model). To the best of our knowledge, this is the first attempt to predict ground vibration using the RVR-GWO and RVR-BA models. The afore-mentioned models were developed and tested using 95 datasets. Then, the performance of the developed models was statistically checked through four comparative experiments using, among others, mean square error (MSE) and correlation coefficient (R). The results indicated the superiority of the RVR-GWO model over the RVR-BA model in terms of prediction precision. The RVR-GWO model with R of 0.915 and MSE = 7.920 predicted the ground vibration better than the RVR-BA model with R of 0.867 and MSE = 8.551. Accordingly, it was concluded that applying the GWO algorithm to RVR can result in high accuracy in the prediction of blast-induced ground vibration.

A new multikernel relevance vector machine based on the HPSOGWO algorithm for predicting and controlling blast-induced ground vibration

The relevance vector machine (RVM) is considered a robust machine learning method and its superior performance has been confirmed through many successful engineering applications. To improve the performance of the RVM model, three single kernel functions, and three multikernel functions, including two newly proposed multikernel functions, tenfold cross-validation, and the hybrid particle swarm optimization with grey wolf optimizer (HPSOGWO) algorithm were combined to develop an artificial intelligence (AI) model framework. Afterwards, a new application of the RVM method was used and introduced for two different datasets of the blast-induced ground vibration. In addition, an artificial neural network (ANN) model and seven empirical equations were also developed for comparison purposes, and their prediction performances were evaluated considering three performance metrics, i.e., root mean square error (RMSE), correlation coefficient (R2), and mean absolute error (MAE). The obtained results showed that the multikernel RVM model can provide better performance capacity than the single-kernel RVM model. As a result, the AI models were found to be more applicable than the empirical equations in estimating blast-induced ground vibration. The prediction performance results of these models confirmed that the selected database has a great impact on the prediction capacity. Therefore, it is a common act to compare the performance of various models based on the selected database before selecting an optimal predictive model. The proposed model in this study provides new theoretical and practical support for the prediction of blast-induced ground vibration and can be utilized by other researchers in similar fields.

Performance Evaluation of Training Algorithms in Backpropagation Neural Network Approach to Blast-Induced Ground Vibration Prediction

Abstract
 Backpropagation Neural Network (BPNN) is an artificial intelligence technique that has seen several applications in many fields of science and engineering. It is well-known that, the critical task in developing an effective and accurate BPNN model depends on an appropriate training algorithm, transfer function, number of hidden layers and number of hidden neurons. Despite the numerous contributing factors for the development of a BPNN model, training algorithm is key in achieving optimum BPNN model performance. This study is focused on evaluating and comparing the performance of 13 training algorithms in BPNN for the prediction of blast-induced ground vibration. The training algorithms considered include: Levenberg-Marquardt, Bayesian Regularisation, Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi-Newton, Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Fletcher-Powell Conjugate Gradient, Polak-Ribiére Conjugate Gradient, One Step Secant, Gradient Descent with Adaptive Learning Rate, Gradient Descent with Momentum, Gradient Descent, and Gradient Descent with Momentum and Adaptive Learning Rate. Using ranking values for the performance indicators of Mean Squared Error (MSE), correlation coefficient (R), number of training epoch (iteration) and the duration for convergence, the performance of the various training algorithms used to build the BPNN models were evaluated. The obtained overall ranking results showed that the BFGS Quasi-Newton algorithm outperformed the other training algorithms even though the Levenberg Marquardt algorithm was found to have the best computational speed and utilised the smallest number of epochs.
 
 Keywords: Artificial Intelligence, Blast-induced Ground Vibration, Backpropagation Training Algorithms