Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm
Source abstract
Abstract
Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small sample data, Support Vector Regression (SVR) machine and Random Forest (RF), were employed. The models were optimized using the particle swarm optimization algorithm (PSO) to predict peak particle velocity with multiple parameters specific to long hole blasting. Mean absolute error (MAE), mean Squared error (MSE), and coefficient of determination (R2) were used to assess the model predictions. Compared with the fitting equation based on the USBM model, both the Support Vector Regression (SVR) and Random Forest (RF) models accurately and effectively predict peak particle velocity, enhancing prediction accuracy and efficiency. The SVR model exhibited slightly superior predictive performance compared to the RF model.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Civil and Geotechnical Engineering Research · Remote Sensing and Land Use · Technology and Security Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Information Institute of Ministry of Emergency Management, Beijing 100029, China
School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
Funding
Joint Fund of the National Natural Science Foundation of China · NSFC-Xinjiang Joint Fund · S&T Innovation and Development Project of Information Institution of Ministry of Emergency Management
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