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

Fattahi, Hadi; Hasanipanah, Mahdi

doi:10.1007/s11053-020-09764-7

Cited by 42 publications

(9 citation statements)

References 59 publications

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“…To overcome the limitations associated with ANN in predicting blast-induced ground vibrations, studies have also applied other ML algorithms that are without these shortcomings. Some of the algorithms applied included SVM [104][105][106][107][108][109][110][111], relevance vector regression [112], particle swarm optimization [113,114] Bayesian network and random forest [108], Gaussian process regression [115], classification and regression trees, chi-square automatic interaction detection, random forest [1,116,117], hybrid artificial bee colony algorithm [118], fuzzy Delphi method and hybrid ANN-based systems [119], cuckoo search algorithm [120], extreme learning machine [121], extreme gradient boosting (XGBoost) [122], and the firefly algorithm [123][124][125][126].…”

Section: Ground Vibrationmentioning

confidence: 99%

Advances in Computational Intelligence Applications in the Mining Industry

2022

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Company, a leading vertically integrated fertilizer producer with mines throughout the Americas and a global distribution network. From 2002 to 2018, Dessureault was a professor at the University of Arizona, where he engaged in research related to the integration and effective use of mining information systems and sustainability. In 2004, he founded MISOM Technologies, an IT services company with expertise in data integration, control rooms, and automation. MISOM eventually developed the FARA platform, which brings together Internet of Things (IoT) sensors, a tablet-based mobile app, and modern approaches such as Big Data and gamification to create innovative form digitization, worker engagement, and fleet management. In late 2017, MISOM was acquired by MST Global, where Dr. Dessureault served as Chief Innovation Evangelist until joining Mosaic in 2019.

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Section: Ground Vibrationmentioning

confidence: 99%

Advances in Computational Intelligence Applications in the Mining Industry

2022

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Section: Ground Vibrationmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

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Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

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“…They concluded the hybrid method was more effective and robust than the ELM and empirical models. The mentioned optimization algorithm was also combined with the relevance vector regression (RVR) with the same aim by Fattahi and Hasanipanah ( 33 ). For comparison purposes, a bat-inspired algorithm-RVR was used.…”

Section: Introductionmentioning

confidence: 99%

Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data

Keshtegar

Piri

Abdullah

et al. 2023

Front. Public Health

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Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.

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

Cited by 42 publications

References 59 publications

Advances in Computational Intelligence Applications in the Mining Industry

Advances in Computational Intelligence Applications in the Mining Industry

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data

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