Multivariate statistical analysis approach for prediction of blast-induced ground vibration

Himanshu, Vivek Kumar; Roy, Moumita; Mishra, A. K.; Paswan, Ranjit Kumar; Panda, Deepak Kumar; Singh, Pradeep K.

doi:10.1007/s12517-018-3796-8

Cited by 35 publications

(7 citation statements)

References 10 publications

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“…where 'Y' is the predicted variable, 'a 0 ' is intercept, 'a i ' (i ¼ 1, 2, … , n) are the coefficients up to ith input parameter, 'x i ' (i ¼ 1, 2, … , n) are input parameters up to ith term and 'e' is the error associated with the prediction. To develop the MLR model with highly influencing inputs, the different combinations of eleven inputs were examined in multiple steps (Himanshu et al 2018) considering the results of boxplots and correlation. For testing the significance of models analysis of variance, F-test, Shapiro-Wilk test, R 2 , root-mean-square error (RSME) have been considered (Table 4).…”

Section: Mlrmentioning

confidence: 99%

Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique

Bhagat

Rana

Mishra

et al. 2021

Geomatics, Natural Hazards and Risk

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Boulder blasting is a different process from conventional bench blasting. Fly-rock produced in boulder blasting is a major safety concern due to the presence of 360 free-face which may result into excessive throw of the fragments radially up to 900 m distance causing accidents. Many researchers have attempted to predict the flyrock using empirical and soft computing tools in bench blasting. But, there is paucity of literature to predict the extent of fly-rock in boulder blasting. Machine learning techniques are frequently used in bench blasting to predict ground vibrations, air overpressure, flyrocks, but it has been rarely used in boulder blasting. In this study, an attempt has been made to use Classification and Regression Trees (CART) technique to predict the fly-rock distance in boulder blasting. Multiple linear regression (MLR) technique has been used to compare the results obtained by the CART technique. Sixty-one boulder blasting events were monitored while excavating the accident-prone slope areas of Konkan Railways. The performance of the developed models using both the techniques has been evaluated using the coefficients of determination (R 2 ) and root-mean-square error (RSME) values. The results indicate that CART model (R 2 ¼ 0.9555 and RMSE ¼ 1.141) provides better output than MLR model. This paper suggests the use of CART technique in boulder blasting, which will be useful in execution at sensitive locations to predict and control the fly-rock distance.

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Section: Mlrmentioning

confidence: 99%

Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique

Bhagat

Rana

Mishra

et al. 2021

Geomatics, Natural Hazards and Risk

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“…To gain better results, artificial intelligence (AI) methods/models have been proposed to predict PPV with many advantages, such as high accuracy, rock properties are considered, different blasting parameters are investigated and applied, low-cost, and time-saving. A variety of AI models have been proposed for the aims of PPV prediction and control in open-pit mines, such as artificial neural networks (ANN) models [17][18][19][20], machine learning-based models (e.g., support vector machine, CART, multivariate statistical analysis, multivariate adaptive regression splines, to name a few) [21][22][23][24][25], metaheuristic algorithm-based ANN models [1,5,[26][27][28][29][30], metaheuristic algorithm-based machine learning models [31][32][33][34][35][36][37], and clustering-based models [38][39][40][41]. Therein, the accuracies of the introduced models are in the range of 92.7%-98.6%.…”

Section: Introductionmentioning

confidence: 99%

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

Tran¹,

Nguyen²,

Bui³

2023

Computer Modeling in Engineering &Amp; Sciences

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This study considered and predicted blast-induced ground vibration (PPV) in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms. Accordingly, four machine learning algorithms, including support vector regression (SVR), extra trees (ExTree), K-nearest neighbors (KNN), and decision tree regression (DTR), were used as the base models for the purposes of combination and PPV initial prediction. The bagging regressor (BA) was then applied to combine these base models with the efforts of variance reduction, overfitting elimination, and generating more robust predictive models, abbreviated as BA-ExTree, BA-KNN, BA-SVR, and BA-DTR. It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before, and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model, as well. In addition, two empirical models (i.e., USBM and Ambraseys) were also treated and compared with the bagging models to gain a comprehensive assessment. With this aim, we collected 300 blasting events with different parameters at the Sin Quyen copper mine (Vietnam), and the produced PPV values were also measured. They were then compiled as the dataset to develop the PPV predictive models. The results revealed that the bagging models provided better performance than the empirical models, except for the BA-DTR model. Of those, the BA-ExTree is the best model with the highest accuracy (i.e., 88.8%). Whereas, the empirical models only provided the accuracy from 73.6%-76%. The details of comparisons and assessments were also presented in this study.

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“…e conclusions showed that the modified formulas generated safer prediction results, but their solutions were not good supported by theoretical analysis since no normalization was implemented. Zhu et al [11], Faradonbeh et al [12], and Himanshu et al [13] proposed a modified formula for predicting the blast vibration velocities for sloped terrains based on field data analysis and comparison experiment. ey performed dimensional analyses on the altitude difference and obtained acceptable results when they applied the modified formula to practical situations.…”

Section: Introductionmentioning

confidence: 99%

A Study of Blast Vibration Propagation Law under Negative Altitude Terrains

Gao

Huang

et al. 2022

Mathematical Problems in Engineering

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The propagation characteristics of blast waves and the prediction accuracy of blast vibration velocities along negative slopes are important because those can be used to guide engineering application and theoretical research. In this paper, the wave theory was applied to better understand the propagation mechanism of blast waves along negative slopes. Regression analysis was used for the Sadovsky and the CRSRI blast vibration velocity prediction models during on-site operations. The magnification of peak blast vibration velocity along a negative slope was introduced to determine the threshold altitude difference for the magnification effect to occur. Based on this parameter, the relative errors between the two prediction models were compared. The obtained results indicate that the superposition of incident and reflected blast waves on a negative slope creates the “slope effect” which locally amplifies the blast vibration velocity. The relative error of the CRSRI prediction model was as small as 17.53%, demonstrating a greater accuracy than Sadovsky’s prediction model. The magnification effect of a negative slope was observed at specific altitude differences and was more noticeable in the perpendicular direction. This paper creates a theoretical basis for studying the propagation mechanisms of blast vibration waves along negative slopes as well as predicting the blast vibration velocities.

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Multivariate statistical analysis approach for prediction of blast-induced ground vibration

Cited by 35 publications

References 10 publications

Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique

Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

A Study of Blast Vibration Propagation Law under Negative Altitude Terrains

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