Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm

Author:

Xie Lianku1,Yu Qinglei2,Liu Jiandong1,Wu Chunping3,Zhang Guang1ORCID

Affiliation:

1. Information Institute of Ministry of Emergency Management, Beijing 100029, China

2. School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China

3. School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China

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.

Funder

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

Publisher

MDPI AG

Reference42 articles.

1. Dynamic tensile behaviour under impact loading for rocks damaged by static precompression;Zheng;Arch. Civ. Mech. Eng.,2023

2. Vibration response and failure modes analysis of the temporary support structure under blasting excavation of tunnels;Guan;Eng. Fail. Anal.,2022

3. Influential factors analysis of blasting vibration attenuation law;Li;Eng. J. Wuhan Univ.,2005

4. Study of energy attenuation law of blast-induced seismic wave;Li;Chinese J. Rock Mech. Eng.,2010

5. Ghosh, A., and Daemen, J.J.K. (, January June). A Simple New Blast Vibration Predictor (Based on Wave Propagation Laws). Proceedings of the ARMA US Rock Mechanics/Geomechanics Symposium, College Station, TX, USA.

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