Sensitivity Analysis of a Modified Peak Particle Velocity Model to Predict Blast-Induced Damage

2020 ◽  
Author(s):  
◽  
Mphoentle Gadiile
Keyword(s):  
Sensitivity Analysis ◽  
Particle Velocity ◽  
Velocity Model ◽  
Peak Particle Velocity

scholarly journals Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

2021 ◽  
Vol 11 (8) ◽  
pp. 3705
Author(s):  
Jie Zeng ◽  
Panayiotis C. Roussis ◽  
Ahmed Salih Mohammed ◽  
Chrysanthos Maraveas ◽  
Seyed Alireza Fatemi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Particle Velocity ◽  
Basis Function ◽  
Absolute Error ◽  
Peak Particle Velocity ◽  
Support Vector ◽  
Effective Parameters ◽  
Experimental Database ◽  
Interaction Detection ◽  
Radial Basis

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

Classification and regression tree technique in estimating peak particle velocity caused by blasting

2016 ◽  
Vol 33 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Manoj Khandelwal ◽  
Danial Jahed Armaghani ◽  
Roohollah Shirani Faradonbeh ◽  
Mohan Yellishetty ◽  
Muhd Zaimi Abd Majid ◽  
...  
Keyword(s):  
Particle Velocity ◽  
Regression Tree ◽  
Peak Particle Velocity ◽  
Classification And Regression Tree ◽  
Classification And Regression

scholarly journals Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 1403 ◽  
Author(s):  
Zhi Yu ◽  
Xiuzhi Shi ◽  
Jian Zhou ◽  
Xin Chen ◽  
Xianyang Qiu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Rock Mass ◽  
Random Forest ◽  
Particle Velocity ◽  
Ground Vibration ◽  
Accurate Method ◽  
Peak Particle Velocity ◽  
Simulation Approach ◽  
Input Variables

Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

Prediction of peak particle velocity using multi regression analysis: case studies

2016 ◽  
Vol 12 (3) ◽  
pp. 207-214 ◽  
Author(s):  
K. Ram Chandar ◽  
V. R. Sastry ◽  
Chiranth Hegde ◽  
Srisharan Shreedharan
Keyword(s):  
Regression Analysis ◽  
Case Studies ◽  
Particle Velocity ◽  
Peak Particle Velocity

scholarly journals Monitoring of train-induced vibrations on rock slopes

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668755 ◽  
Author(s):  
Jiangbo Xu ◽  
Changgen Yan ◽  
Xu Zhao ◽  
Ke Du ◽  
Heng Li ◽  
...  
Keyword(s):  
Particle Velocity ◽  
High Speed ◽  
High Efficiency ◽  
Low Cost ◽  
Test System ◽  
Peak Particle Velocity ◽  
Vibration Test ◽  
Long Distance ◽  
Stability Of Slopes ◽  
The Stability

Train-induced vibrations will undoubtedly influence the stability of slopes near railway lines. To monitor the effects of such vibrations on slope stability, a wirelessly networked vibration test system was established, which included wirelessly networked vibration meters, high-precision and high-speed three-dimensional sensors and a remote wirelessly networked data server system. This system represents the first attempt to monitor the effects of train-induced vibrations on the stability of slopes in China. It enables real-time and long-distance monitoring by means of remote transmission with a low cost and high efficiency. The duration, frequency, amplitude, peak acceleration and peak particle velocity were adopted as measures for evaluating the influence of train vibrations. Simultaneously, we used additional monitoring technologies to verify the conclusions of the wirelessly networked vibration test system. The monitoring results indicated that the peak particle velocity was much higher near the track and gradually decreased with increasing distance. When the distance between the measurement point and the road axis was 12 m ( H = 0 m), the maximal peak particle velocity was 0.4 mm/s, which remained below the maximum safe value.

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