Developing GPR model for forecasting the rock fragmentation in surface mines

Gao, Wei; Karbasi, Masoud; Hasanipanah, Mahdi; Zhang, Xun; Guo, Jia

doi:10.1007/s00366-017-0544-8

Cited by 80 publications

(24 citation statements)

References 17 publications

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“…Recent applications of deep learning in blasting include the prediction of flyrock [157], rock fragment distribution [176], and classification of mine seismic events, among others. Further, we observed that most common ML algorithms for blastinduced fragment size predictions include the ANN [159][160][161][162], SVM [104,177], PCA [177], fuzzy inference system [178][179][180], adaptive neuro-fuzzy inference system [177,181,182], bee colony algorithm [162], PSO [183,184], ant colony optimization [185], and gaussian process regression [186]. The ML-based fragment size prediction models performed significantly better than the empirical models [187].…”

Section: Discussion and Future Trendsmentioning

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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Section: Discussion and Future Trendsmentioning

confidence: 99%

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

2021

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“…is approach can simply solve complicated problems. Nonlinear GPR techniques may be employed using small training datasets and integrate new evidence as the data points rise in number [48]. Overfitting is avoided to a great extent as optimization includes fewer hyperparameters in the training phase.…”

Section: Gpr Modelmentioning

confidence: 99%

On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach

Wang

Jafari

2022

International Journal of Chemical Engineering

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Materials discovery is usually done using high-throughput computational screening. The use of costly and complex direct density functional theory (DFT) simulation methods has been commonly used to determine subtle trends in spin-state ordering and inorganic bonding of inorganic materials and, in general, to predict the electronic structure properties of transition metal complexes. A Gaussian process regression (GPR) framework consisting of four kernel functions is introduced for spin-state splitting estimation through inorganic chemistry-appropriate empirical inputs. To this end, the present study reviewed an extensive range of data values from earlier works. According to statistical analysis, the GPR model showed very good performance. The coefficients of determination were calculated to be 0.986 for the exponential and Matern kernel functions, suggesting the highest predictive power of these methods. Moreover, the sensitivity of output to inputs was measured. Artificial intelligence (AI) helped accurately predict the target values through various input ranges.

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“…The symbol represents the gamma function and x, y are positive parameters, and the adjusted K v is the Bessel function [46].…”

Section: Train Outputmentioning

confidence: 99%

Comprehensive Modeling in Predicting Biodiesel Density Using Gaussian Process Regression Approach

Wang

Alruyemi

2021

BioMed Research International

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In this study, four Gaussian process regression (GPR) approaches by various kernel functions have been proposed for the estimation of biodiesel density as the functions of pressure, temperature, molecular weight, and the normal melting point of fatty acid esters. Comparing the actual values with GPR outputs shows that these approaches have good accuracy, but the performance of the rational quadratic GPR model is better than others. In this GPR model, RMSE = 0.47 , MSE = 0.22 , MRE = 0.04 , R 2 = 1 , and STD is equal to 0.3. In addition, for the first time, this study shows that the effective parameters affect the biodiesel density. According to this analysis, it was shown that among the input parameters, pressure has the greatest effect on the target values with a relevancy factor of 0.59. This study can be used as a suitable and valuable work/tool for chemical and petroleum engineers who attempt environment protection and recovery improvement.

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Developing GPR model for forecasting the rock fragmentation in surface mines

Cited by 80 publications

References 17 publications

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

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

On the Investigation of Effective Factors on Electronic Structure Properties of Transition Metal Complexes: Robust Modeling Using GPR Approach

Comprehensive Modeling in Predicting Biodiesel Density Using Gaussian Process Regression Approach

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