›› 2014, Vol. 35 ›› Issue (S2): 338-342.

• Geotechnical Engineering • Previous Articles     Next Articles

Blasting vibration parameters using comprehensive regression of wavelet denoising and particle swarm optimization algorithm

ZHANG Le-wen, WANG Hong-bo, QUY Dao-hong, SUN Huai-feng, SUN Zi-zheng, DING Wan-tao   

  1. Research Center of Geotechnical and Structural Engineering, Shandong University, Jinan 250061, China
  • Received:2014-03-31 Online:2014-10-31 Published:2014-11-12

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Abstract: 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.

Key words: particle swarm optimization algorithm, wavelet, denoising, blasting vibration, regression

CLC Number: 

  • TD 235.1
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