Prediction of Seismic Wave Intensity Generated by Bench Blasting Using Intelligence Committee Machines

doi:10.5829/ije.2019.32.04a.21

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The construction of MLP classifiers consists of adequate input variables and specification of the type of network, relevant data pre-processing and partitioning, the configuration of network infrastructure, specification of success parameters, specification of training algorithm (optimization of relation weights), and finally evaluation model [13].…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Analysis of Accuracy Metric of Machine Learning Algorithms in Predicting Heart Disease

Yousefi

Poornajaf

2023

Front Health Inform

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Introduction: Heart disease is, for the most part, alluding to conditions that include limited or blocked veins that can prompt a heart attack, chest torment or stroke. Earlier identification of heart disease may reduce the death rate. The cost of medical diagnosis makes it perverse to cure it for the large amount of people early. Using machine learning models performed on dataset. This article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several supervised machine learning algorithms were utilized to diagnosis and prediction of heart disease such as logistic regression, decision tree, random forest and KNN. The algorithms are applied to a dataset taken from the Kaggle site including 70000 samples. In algorithms, methods such as the importance of features, hold out validation, 10-fold cross-validation, stratified 10-fold cross-validation, leave one out cross-validation are the result of effective performance and increase accuracy. In addition, feature importance scores was estimated for each feature in some algorithms. These features were ranked based on feature importance score. All the work is done in the Anaconda environment based on python programming language and Scikit-learn library.Results: The algorithms performance is compared to each other so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, random forest algorithm with F1 score 92%, accuracy 92% and AUC ROC 95%, has better performance than other algorithms.Conclusion: The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the diagnosis and prediction of heart disease is compared to determine the most appropriate classifier.

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Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Analysis of Accuracy Metric of Machine Learning Algorithms in Predicting Heart Disease

Yousefi

Poornajaf

2023

Front Health Inform

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“…Zhou et al [24] employed feature selection methods to identify primary input variables and developed two prediction models, FS-RF and FS-BN, for predicting ground vibrations resulting from quarry blasting, and the FS-RF model exhibited marginally superior accuracy compared to the FS-BN model. Azimi [25] introduced a second-order polynomial intelligent committee machine (SPICM) for open-pit mine bench blasting vibration prediction, which proved to be more accurate and reliable than previous empirical formulas and neural network models. The SPICM model represents a novel advancement in machine learning technology for blasting vibration prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm

Xie,

Yu,

Liu

et al. 2024

Applied Sciences

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Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small sample data, Support Vector Regression (SVR) machine and Random Forest (RF), were employed. The models were optimized using the particle swarm optimization algorithm (PSO) to predict peak particle velocity with multiple parameters specific to long hole blasting. Mean absolute error (MAE), mean Squared error (MSE), and coefficient of determination (R2) were used to assess the model predictions. Compared with the fitting equation based on the USBM model, both the Support Vector Regression (SVR) and Random Forest (RF) models accurately and effectively predict peak particle velocity, enhancing prediction accuracy and efficiency. The SVR model exhibited slightly superior predictive performance compared to the RF model.

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“…Three-dimensional numerical modelling code (3DEC) has also been utilized by researchers to assess the promulgation of cracks along the walls of underground structures as well as asserting their responses under the dynamic loads [7,8]. A research study utilized Intelligent Committee Machines for forecasting the Peak Particle Velocity (PPV) generated due to bench blasting on rock slopes [9]. The stability aspect due to different extreme loading conditions has been given a lot of importance over the past decades.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Modelling Approach to Assess Deformations of Horseshoe Cavern on Account of Rock Mass Characteristics and Discontinuities

Kuili

Sastry

2023

IJE

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Caverns are massive underground openings excavated for purposes like defense installations and nuclear waste disposal which becomes challenging for weak strata consisting of fractured rock masses and may result in future calamities. This study is dedicated to ascertain stability of a horseshoe cavern in different types of granitic rock masses as well as fractured rocks. Two different types of granitic formations are accounted and displacements obtained along cavern periphery have been illustrated in the preliminary part of this study. An increment in vertical displacement at the crown of 260% and lateral displacement of almost 170% for both walls were observed for moderately weathered rock. Further, implications due to orientation and frequency of joint sets on the displacements incurred has also been explored. This study acquires its novelty by considering combinations of joint sets with varying spacings for investigating their implications on cavern walls. Presence of discontinuities depicted that horizontal joint spaced closely increased the deformation magnitude which reduced with decrement in joint frequency. Subsequently, along with horizontal joints at 4m interval, vertical and oblique joints sets were also incorporated at different frequencies. Consideration of vertical joints at 2m spacing with horizontal joints resulted in 313.2% increase in vertical displacement at cavern crown as well as 340% and 363% increase in lateral deformation at the left and right wall, respectively in comparison to intact rock. In case of oblique joints spaced at 2m with horizontal joints, increment in vertical deformation at the crown proliferated to 329% in comparison to intact rock.

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Prediction of Seismic Wave Intensity Generated by Bench Blasting Using Intelligence Committee Machines

Cited by 6 publications

References 25 publications

Analysis of Accuracy Metric of Machine Learning Algorithms in Predicting Heart Disease

Analysis of Accuracy Metric of Machine Learning Algorithms in Predicting Heart Disease

Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm

A Numerical Modelling Approach to Assess Deformations of Horseshoe Cavern on Account of Rock Mass Characteristics and Discontinuities

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