Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study

Nguyen, Hoang; Bui, Xuan‐Nam; Bui, Hoang‐Bac; Cuong, Dao Trong

doi:10.1007/s11600-019-00268-4

Cited by 120 publications

(46 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main dynamic factor is blasting vibration (Lu et al, 2012b). Lots of experimental results show that vibration has a close relationship with peak particle velocity (PPV) (Nguyen et al, 2019a(Nguyen et al, , 2019b) which can be seen as the most direct reflection of dynamic response. PPV of slope toe is the common monitored data and PPV of the slope top is also necessary to prevent blasting vibration amplification for high steep slope.…”

Section: Dynamic Criterionmentioning

confidence: 99%

RETRACTED ARTICLE: Blasting excavation and stability control technology for ultra-high steep rock slope of hydropower engineering in China: a review

Deng

Chen

2020

European Journal of Remote Sensing

View full text Add to dashboard Cite

China has entered a high-speed construction period on hydropower engineering since the late 20th century. Blasting is the major way for rock excavation on large projects, especially for ultra-high steep rock slope. Due to the constraints of topography and geomorphology, requirements on blasting excavation and stability of the reserved rock after blasting are strict for ultra-high steep rock slope. Thus, blasting and reinforcement technology have caused engineer's attention. This article reviewed the blasting excavation and stability control technology of Chinese hydropower engineering slope and discussed the progress and limitation. Slope on the left bank of Jinping I hydropower station was selected as a case study to reflect the blasting excavation and stability control technology in a rather challenging hydropower engineering slope. The blasting excavation and stability control technology involved in this article can give some reference for other similar engineering projects.

show abstract

Section: Dynamic Criterionmentioning

confidence: 99%

RETRACTED ARTICLE: Blasting excavation and stability control technology for ultra-high steep rock slope of hydropower engineering in China: a review

Deng

Chen

2020

European Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Zhang et al [42] applied the XGboost algorithm to the fault diagnosis of rolling bearings, and the results showed that the XGboost algorithm was superior to other tree algorithms in accuracy and time. Nguyen et al [43] developed an XGBoost model to predict peak particle velocity (PPV). The results indicated that the developed XGBoost model, on both training and testing datasets, exhibited higher performance than the support vector machine (SVM), the Random Forests (RFs), and kNN models.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Lei

Jiang

et al. 2019

Processes

View full text Add to dashboard Cite

A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.

show abstract

“…It can handle complex data at high speed and accuracy. The XGBoost algorithm can be described as follows [54]:…”

Section: Extreme Gradient Boosting Machine (Xgboost)mentioning

confidence: 99%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

Self Cite

View full text Add to dashboard Cite

In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

show abstract

Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study

Cited by 120 publications

References 45 publications

RETRACTED ARTICLE: Blasting excavation and stability control technology for ultra-high steep rock slope of hydropower engineering in China: a review

RETRACTED ARTICLE: Blasting excavation and stability control technology for ultra-high steep rock slope of hydropower engineering in China: a review

Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Contact Info

Product

Resources

About