地下和边坡工程开挖常涉及岩体卸荷问题,采用ABAQUS软件中的扩展有限单元法(extended finite element method,XFEM)对开挖卸荷过程岩体内部裂纹的起裂扩展进行了模拟,通过计算裂纹尖端应力强度因子研究了其起裂特征,并探讨了起裂影响因素,通过记录裂纹扩展形态研究了其动态演化模式.结果表明,卸荷过程中卸荷速率越快,裂纹长度越长,倾角越大,其起裂越容易;并且裂纹面受到的正应力不断减小,剪应力不断增大,裂纹扩展主要由剪应力控制,这与理论分析结果一致.裂纹最终扩展演化形态也与物理试验相近,充分表明运用扩展有限单元法研究岩体裂纹问题的可靠性.
To find discriminating features in seismograms for the classification of mine seismic events,signal databases of blasts and microseismic events were established based on manual identification.Criteria including the repetition of waveforms,tail decreasing,dominant frequency and occurrence time of day were considered in the establishment of the databases.Signals from databases of different types were drawn into a unified coordinate system.It is noticed that the starting-up angles of the two types tend to be concentrated into two different intervals.However,it is difficult to calculate the starting-up angle directly due to the inaccuracy of the P-wave arrival＇s picking.The slope value of the starting-up trend line,which was obtained by linear regression,was proposed to substitute the angle.Two slope values associated with the coordinates of the first peak and the maximum peak were extracted as the characteristic parameters.A statistical model with correct discrimination rate of greater than 97.1% was established by applying the Fisher discriminant analysis.
The seismic records of target response spectrum used in the time-history analysis should be allowed to meet the norms. However, the previous fitting methods of target spectrum are mostly for the situations that the target spectrum is a smooth curve. In many cases, it needs to match unsmooth target spectrum for single determined response spectrum. An adjustment of time history via wavelet packet transform was presented, which is able to fit unsmooth target spectrum. It was found that there is a certain bias between the band center frequency of the component of seismic record after wavelet packet decomposition and the peak frequency of response spectra of wavelet packet components. For this reason, five strategies were presented to select iteration points, and the effects of the five strategies were compared with two calculation examples. It was turned out that the peak frequency of the response spectrum of wavelet packet component can lead to good fitting effect when it is selected as the iteration point. In the iteration process, it shows great promise in fitting non-smooth target spectrum and has a trend of converge.
