Classification and regression tree technique in estimating peak particle velocity caused by blasting

Khandelwal, Manoj; Armaghani, Danial Jahed; Faradonbeh, Roohollah Shirani; Yellishetty, Mohan; Majid, Muhd Zaimi Abd; Monjezi, Masoud

doi:10.1007/s00366-016-0455-0

Cited by 74 publications

(18 citation statements)

References 41 publications

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“…As one of the most popular statistical methods, the CART (classification and regression tree) has been widely employed to handle classification and regression problems [64]. Inspired by the growth process of trees, the construction of CART trees generally consists of roots, leaves, branches, and nodes.…”

Section: Classification and Regression Tree (Cart)mentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Classification and Regression Tree (Cart)mentioning

confidence: 99%

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

et al. 2019

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“…According to the no free lunch theorem, if the performance of algorithm A is better than algorithm B in this problem, there must be other problems that algorithm B has better performance at resolving. Hence, although a series of prediction models such as HKM-ANN [39], MLPNN (multilayer perceptron neural network) [42] and CART [43] were utilized in some mines, the proposed algorithms are far from solving all possible problems and being suited for all field sites. As far as the authors know, the combination of the Harris hawks optimization algorithm (HHO) and random forest (RF) has not been tried in predicting blast-induced ground vibration.…”

Section: Introductionmentioning

confidence: 99%

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Shi

Zhou

et al. 2020

Applied Sciences

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Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

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“…Since the TBM PR is a continuous target variable, the supervised ML techniques, such as SVM, nearest neighbor and decision trees (DTs) should be used for classification and regression of TBM data. Supervised ML approaches are powerful due to their ability to solve problems in science and engineering and obtaining a high accuracy level for prediction purposes, especially when dealing with highly complex and nonlinear problems [48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

et al. 2019

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Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.

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Classification and regression tree technique in estimating peak particle velocity caused by blasting

Abstract: obtained results demonstrated that the CART technique is more reliable for predicting the peak particle velocity than the MR and empirical models and it can be introduced as a new technique in this field.

Cited by 74 publications

References 41 publications

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

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

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

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