Automated intelligent hybrid computing schemes to predict blasting induced ground vibration

Author(s):  
Abbas Abbaszadeh Shahri ◽  
Fardin Pashamohammadi ◽  
Reza Asheghi ◽  
Hossein Abbaszadeh Shahri
2016 ◽  
Vol 47 (6) ◽  
pp. 649-663
Author(s):  
Regina Vladimirovna Leonteva ◽  
Vsevolod Igorevich Smyslov

2019 ◽  
Vol 18 (4) ◽  
pp. 817-824
Author(s):  
Edward Gheorghiosu ◽  
Attila Kovacs ◽  
Gabriel Dragos Vasilescu ◽  
Daniela Carmen Rus ◽  
Florin Radoi
Keyword(s):  

Author(s):  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Hoa Anh Nguyen ◽  
Dinh-An Nguyen ◽  
...  

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Aniruddha Sengupta ◽  
Raj Banerjee ◽  
Srijit Bandyopadhyay
Keyword(s):  

Author(s):  
Sangseok Yun ◽  
Jae-Mo Kang ◽  
Jeongseok Ha ◽  
Sangho Lee ◽  
Dong-Woo Ryu ◽  
...  

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 144
Author(s):  
Yan Zhang ◽  
Jijian Lian ◽  
Songhui Li ◽  
Yanbing Zhao ◽  
Guoxin Zhang ◽  
...  

Ground vibrations induced by large flood discharge from a dam can damage surrounding buildings and impact the quality of life of local residents. If ground vibrations could be predicted during flood discharge, the ground vibration intensity could be mitigated by controlling or tuning the discharge conditions by, for example, changing the flow rate, changing the opening method of the orifice, and changing the upstream or downstream water level, thereby effectively preventing damage. This study proposes a prediction method with a modified frequency response function (FRF) and applies it to the in situ measured data of Xiangjiaba Dam. A multiple averaged power spectrum FRF (MP-FRF) is derived by analyzing four major factors when the FRF is used: noise, system nonlinearity, spectral leakages, and signal latency. The effects of the two types of vibration source as input are quantified. The impact of noise on the predicted amplitude is corrected based on the characteristics of the measured signal. The proposed method involves four steps: signal denoising, MP-FRF estimation, vibration prediction, and noise correction. The results show that when the vibration source and ground vibrations are broadband signals and two or more bands with relative high energies, the frequency distribution of ground vibration can be predicted with MP-FRF by filtering both the input and output. The amplitude prediction loss caused by filtering can be corrected by adding a constructed white noise signal to the prediction result. Compared with using the signal at multiple vibration sources after superimposed as input, using the main source as input improves the accuracy of the predicted frequency distribution. The proposed method can predict the dominant frequency and the frequency bands with relative high energies of the ground vibration downstream of Xiangjiaba Dam. The predicted amplitude error is 9.26%.


Author(s):  
Niichi Nishiwaki ◽  
Noboru Fujio ◽  
Takuji Mori

People living in houses near a big factory complained about chattering of glass windows. At one of these houses, the SPL of low frequency noise was about 66 dB at 5.5 Hz and ground acceleration level was about 40 dB at 9 Hz in the horizontal direction. (0 dB acceleration = 10−5 m/s2). We found that the noise and ground vibration were caused by a big grinding mill in the factory, because both SPL and acceleration level at the residential district were considerably decreased when the mill was not in operation. We also confirmed that low frequency noise was not transmitted from the grinding mill directly, but was due to the resonant vibration of walls of the factory building. Two ideas are studied here to suppress the noise, one of which is to isolate the vibration of the grinding mill at its foundation, and the other is to improve the stiffness of the building frames to stop the wall vibration. As a result of the study, the latter method to increase the stiffness of the building was adopted. The SPL of low frequency noise near the wall was decreased.


2007 ◽  
Vol 116 (1) ◽  
pp. 1-6 ◽  
Author(s):  
M. P. Roy ◽  
P. K. Singh ◽  
G. Singh ◽  
M. Monjezi
Keyword(s):  

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