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Analysis And Prediction Of Time Frequency Characteristics Of Bench Blasting Signal

Posted on:2022-01-10Degree:MasterType:Thesis
Country:ChinaCandidate:Z M WangFull Text:PDF
GTID:2492306512474834Subject:Construction project management
Abstract/Summary:PDF Full Text Request
The time-frequency analysis and prediction of bench blasting vibration signal can achieve the purpose of controlling bench blasting vibration.A large number of engineering practices show that the criterion of particle vibration velocity peak and main vibration frequency can not fully meet the safety requirements of engineering blasting vibration.In order to reduce the vibration hazards of bench blasting and avoid the occurrence of safety accidents in blasting engineering,the vibration signal characteristics such as particle vibration velocity peak and corresponding main vibration frequency and vibration energy should be fully considered in blasting safety monitoring.In this paper,the characteristics of bench blasting vibration signals are studied.The time-frequency distribution characteristics of bench blasting vibration signals are analyzed by using numerical simulation and signal processing methods.The effects of blasting interval time and plugging length on vibration signal characteristics are discussed.Based on BP neural network prediction algorithm,the vibration signal feature prediction model is established,and the vibration signal feature prediction system of step blasting is developed.First,distribution characteristics in bench blasting vibration signal characteristics as the research object,through the bench blasting vibration measurement signals found in practical engineering,the blasting vibration signal in three directions(vertical,radial and tangential)in basic low energy band(0~31 hz),in most cases it contains energy ratio more than 90%of total energy,The energy generated by vibration signals is basically concentrated in the low frequency band.Wavelet transform is used to analyze the frequency band distribution of vibration energy and the method of fast Fourier transform is used to measure that the vibration energy produced by step blasting is mostly concentrated in the vicinity of the frequency of the main vibration.However,the phenomenon that the vibration energy is concentrated in several frequency bands can be found by calculating individual vibration signals.Therefore,it is considered that in the safety standard of blasting vibration,it is not perfect to judge the frequency range of concentrated vibration energy only by calculating the frequency of main vibration.At the same time,it is found that when the number of blasting main holes is increased,the main frequency will converge to the lower frequency,and the phenomenon of energy concentration in several frequency bands will be reduced.Secondly,the effect of the interval time of small difference blasting on the vibration signal characteristics of step blasting is analyzed.Based on the principle of linear superposition of vibration signals,it is found that the amplitude of vibration velocity and the period duration containing the change of energy value with time interval of the synthesized signal are approximate to the period duration corresponding to the main frequency of the sub-signal.When the superposition interval time of two sub-signals is approximate to △t=(2n-1)t/2,the effect of vibration and energy reduction of the synthesized signal is significant near the interval time.The influence of particle vibration peak value of the sub-signal and the main frequency on the law of vibration and energy reduction of the synthetic signal is further analyzed.When the amplitude of the sine wave corresponding to the frequency of the sub-signal is significantly different from the amplitude of the sine wave corresponding to other sub-frequencies in the sub-signal,the sine wave corresponding to the main frequency can be approximately regarded as the sub-signal.The optimal damping time interval is calculated according to the equation △t=(2n-1)t/2(t is the vibration period corresponding to the dominant frequency),and the calculation result is similar to the difference time corresponding to the total energy of the linear superposition synthetic signal reaching the minimum value.Thirdly,the influence of plug length on vibration signal characteristics of bench blasting is studied by numerical simulation.Based on the simulation results,it is concluded that the peak value of particle vibration velocity collected in the middle and far region and the energy value contained in it gradually increase with the increase of the blocking length.The blocking length has a very weak effect on the peak value of vibration velocity of the vibration signal,but has a significant effect on the energy value contained in the blasting signal.The simulation results show that the peak value of particle vibration velocity decreases with the increase of the distance between blasting sources in the middle and far region,and the attenuation rate of the peak value of blasting vibration velocity slows down with the increase of the distance between blasting sources.Finally,MATLAB is used to develop the step blasting signal feature prediction system.The system realizes the prediction visualization operation based on the principle of linear superposition of blasting vibration signals and BP neural network,improves the operability of the prediction of bench blasting characteristics,and provides a convenient means for users to predict the characteristics of vibration signals.In this paper,the vibration signal characteristics of step blasting are studied.By using signal processing algorithm,MATLAB and AutoDyn simulation software,the distribution law and influencing factors of step blasting vibration signal are deeply analyzed from theory to practice,and the vibration characteristic prediction system is developed,which provides a theoretical basis for the safety control standard of step blasting vibration.It provides reference for safety criterion of blasting vibration.
Keywords/Search Tags:Bench blasting, Signal characteristics, Millisecond interval, Clogging length, Feature prediction
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