Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration

Qiu, Yingui; Zhou, Jian; Khandelwal, Manoj; Yang, Haitao; Yang, Peixi; Li, Chuanqi

doi:10.1007/s00366-021-01393-9

Cited by 187 publications

(83 citation statements)

References 70 publications

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“…This usually occurs when the ANN model begins "to memorize the training set instead of learning them and consequently loses the ability to generalize" [48]. The methods proposed for resolving it include early stopping, noise injection, cross-validation, Bayesian regularization, and the optimization approximation algorithm [48,100,101]. Paneiro et al [102] employed bilevel optimization to avoid overfitting and reduce the complexity of an ANNbased ground vibration model.…”

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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Section: Ground Vibrationmentioning

confidence: 99%

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

2021

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“…Adding the new sample output values predicted by each tree is the final predicted value of the sample. Unlike the commonly used gradient lifting decision tree [53], which only uses the initial derivative information during optimization, XGB carries out a second-order Taylor expansion of the cost function, and uses the first and second derivatives at the same time so that XGB has good results [54].…”

Section: Extreme Gradient Boosting (Xgb)mentioning

confidence: 99%

“…It has many unique advantages for solving small-sample, nonlinear, and high-dimensional pattern recognition problems, and overcomes the problems of "dimension disaster" and "over-learning" to a great extent. It has been widely used in pattern recognition, function estimation, regression analysis, time series prediction, and other fields [53,54].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Identifying Dynamic Changes in Water Surface Using Sentinel-1 Data Based on Genetic Algorithm and Machine Learning Techniques

Huang

Liu

et al. 2021

Remote Sensing

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The knowledge of water surface changes provides invaluable information for water resources management and flood monitoring. However, the accurate identification of water bodies is a long-term challenge due to human activities and climate change. Sentinel-1 synthetic aperture radar (SAR) data have been drawn, increasing attention to water extraction due to the availability of weather conditions, water sensitivity and high spatial and temporal resolutions. This study investigated the abilities of random forest (RF), Extreme Gradient Boosting (XGB) and support vector machine (SVM) methods to identify water bodies using Sentinel-1 imageries in the upper stream of the Yangtze River, China. Three sets of hyper-parameters including default values, optimized by grid searches and genetic algorithms, were examined for each model. Model performances were evaluated using a Sentinel-1 image of the developed site and the transfer site. The results showed that SVM outperformed RF and XGB under the three scenarios on both the validated and transfer sites. Among them, SVM optimized by genetic algorithm obtained the best accuracy with precisions of 0.9917 and 0.985, kappa statistics of 0.9833 and 0.97, F1-scores of 0.9919 and 0.9848 on validated and transfer sites, respectively. The best model was then used to identify the dynamic changes in water surfaces during the 2020 flood season in the study area. Overall, the study further demonstrated that SVM optimized using a genetic algorithm was a suitable method for monitoring water surface changes with a Sentinel-1 dataset.

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“…The coefficient of determination (R 2 ) value is an index of the relationship between the actual and predicted pillar strength, which has been used as another index to evaluate SVM model. A zero value of MSE and a value of 1.0 for R 2 are required in order to have a perfect model based on performance prediction (Li et al 2020c;Qiu et al 2021;Zhou et al 2021c). As results of SVM model, the strength of pillars is estimated with R 2 of 0.96 for using training and testing dataset.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Stochastic assessment of hard rock pillar stability based on the geological strength index system

Zhou

Armaghani

et al. 2021

Geomech. Geophys. Geo-energ. Geo-resour.

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Hard rock pillar is a crucial rock mass structure to maintain the stability of underground mine. It needs of special attention to analyze its stability from the point of rock mass quality. In this paper, the geological strength index (GSI) representing the rock mass quality of the hard rock pillar is examined as a new influence factor of stability, and combined with the conventional parameters (uniaxial compressive strength (UCS) of intact rock mass, width of pillar (w), height of pillar (h), the ratio of pillar width to its height (w/h)) to complete the stochastic assessment of the stability of hard rock pillar. The 47 actual cases of hard rock pillar improved by numerical simulation software Flac 3D . An empirical formula fitted by the Least Square method and artificial intelligence prediction models are used to estimate the pillar strength combining with the pillar stress to conduct the probability and reliability analysis in the Monte Carlo simulation. The result of stochastic assessment showed that UCS and w still play a vital role in maintaining pillar stability, but the influence of GSI cannot be ignored. It found that the GSI has a greater influence on the sloughing pillars in comparison with stable and failed hard rock pillars. Concluding remarks is that GSI has crucial effects on the stability of hard rock pillars as well as UCS of the rock mass and shape of pillars (w and h). Thus, the GSI should be considered as one of input parameter for pillar design and stability assessment in underground mines.

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Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration

Cited by 187 publications

References 70 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

Identifying Dynamic Changes in Water Surface Using Sentinel-1 Data Based on Genetic Algorithm and Machine Learning Techniques

Stochastic assessment of hard rock pillar stability based on the geological strength index system

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