A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

Zhang, Hong; Zhou, Jian; Armaghani, Danial Jahed; Tahir, Mahmood Md.; Pham, Binh Thai; Khang, Huynh Van

doi:10.3390/app10030869

Cited by 84 publications

(32 citation statements)

References 91 publications

(88 reference statements)

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“…Therefore, problems such as slow algorithm training speed will occur when processing large sample data [22]. There are fewer parameters to be adjusted in the RF model, and its final result is the average of the results of each decision tree [23]. Therefore, the prediction errors of the SVR model and the RF model are large, and the training time is long.…”

Section: Comparison Results With Other Typical Prediction Methodsmentioning

confidence: 99%

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

2020

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The objective is to improve the prediction accuracy of foundation pit deformation in geotechnical engineering, thereby provide early warning for engineering practice. The digital closerange photogrammetry is used to obtain monitoring data. The error compensation method is used to optimize the center of the monitoring point. Aiming at the limitations of back propagation neural network (BPNN), a genetic algorithm (GA)-optimized BPNN algorithm is proposed. Then, the optimized algorithm is applied to predict the deformation and displacement of foundation pits from three aspects, i.e., simple horizontal displacement, simple longitudinal displacement, and the combination of horizontal and longitudinal displacements. Meanwhile, the time domain, space domain, and time-space domain are used as input features to compare the prediction results of the BPNN model and the GA-optimized BPNN model. Finally, the GA-improved BPNN is compared with the Support Vector Regression (SVR) model and Random Forest (RF) model. The results show that the prediction result, obtained by simultaneously using horizontal displacement and longitudinal displacement as input features, has smaller errors; also, the actual output is closer to the expected output. Compared with the prediction result with time domain and space domain as input features, the prediction result with time-space domain as input features is closer to the expected output. Taking the combination of time and space domains as input features, compared with the BPNN model, the GA-optimized BPNN model has a lower Root Mean Squared Error (RMSE) value (0.0163), a larger Index of Agreement (IA) value (0.9800), and a shorter training time (7.08 s). Compared with the SVR model and RF model, the GA-improved BPNN model has a lower Root Mean Squared Error (RMSE) value (0.0211), a larger Index of Agreement (IA) value (0.9706), and shorter training time (7.61 s). Therefore, the foundation pit deformation prediction model based on BPNN and GA has strong prediction ability, which can be popularized and applied in similar geotechnical engineering.

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Section: Comparison Results With Other Typical Prediction Methodsmentioning

confidence: 99%

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

2020

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“…In this section, an RF model and a HHO-RF model were developed to establish a stable and accurate relationship between the input variables (f, B, T, D V , D H , Q total , Q max ) and output variable (PPV). To evaluate the predictive performance, three performance indices including RMSE, R 2 and MAE, were introduced and utilized here [64][65][66][67][68].…”

Section: Performance Metricsmentioning

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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“…A statistically significant level of cell merging and splitting is set at 0.05. To eliminate error 1 of the hypothesis testing type, we use the Bonferroni correction method (Zhang et al, 2020).…”

Section: ( )mentioning

confidence: 99%

Rational Consumer in the Context of Environmental Protection

Drábik¹,

Rehák²,

Vernerová³

et al. 2020

ekoncas

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A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

Cited by 84 publications

References 91 publications

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

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

Rational Consumer in the Context of Environmental Protection

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