Blasting vibration parameters using comprehensive regression of wavelet denoising and particle swarm optimization algorithm
|更新时间:2026-06-25
|
Blasting vibration parameters using comprehensive regression of wavelet denoising and particle swarm optimization algorithm
Vol. 35, Issue S2, (2014)
作者机构:
山东大学 岩土与结构工程研究中心,济南,250061
作者简介:
基金信息:
DOI:
CLC:TD235.1
Online First:12 November 2014,
Published:2014
稿件说明:
移动端阅览
ZHANG Le-wen, WANG Hong-bo, QUY Dao-hong, et al. Blasting vibration parameters using comprehensive regression of wavelet denoising and particle swarm optimization algorithm[J]. 2014, 35(S2).
DOI:
ZHANG Le-wen, WANG Hong-bo, QUY Dao-hong, et al. Blasting vibration parameters using comprehensive regression of wavelet denoising and particle swarm optimization algorithm[J]. 2014, 35(S2).DOI:
Blasting vibration parameters using comprehensive regression of wavelet denoising and particle swarm optimization algorithm
Collected data from blasting vibration test is quite discrete. The technique of deleting error data and wavelet denoising are used to preprocess blasting vibration test data. In this study
standard residual sum of squares between the collected blasting vibration and the calculated fitted values using Sadovski formula are adopted as deviation basis; while wavelet denoising method is used to deal with the measured data. Particle swarm optimization algorithm (PSOA) was optimized for k and a of Sadovki formula of regression analysis. The results show that: wavelet denoising and particle swarm optimization algorithm reflect the real situation of blasting vibration test and thus improve sequentially simulation accuracy of blasting vibration test
so as to offer a good reference for blasting vibration test theory and engineering practice.