Rock burst fragment ejection speed predicting method based on releasable elastic strain energy
A technology for elastic strain energy and velocity prediction, which is applied in special data processing applications, instruments, electrical digital data processing, etc., and can solve problems such as difficulty in grasping accuracy and reliability
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Embodiment 1
[0051] Project Overview:
[0052] Excavate a circular tunnel (with the characteristics of large buried depth and high ground stress) in a certain area, the radius of the tunnel is R=5m, and the excavation footage is L e =3m, the initial stress value P at the tunnel 0 =30MPa, rock mass elastic modulus E=20GPa, Poisson's ratio ν=0.22, density ρ=2500kg / m 3 , compressive strength σ c =80MPa, initial microcrack length 2a=0.004m, K Ιc = σ c / 82.
[0053] A method for predicting the ejection velocity of rockburst fragments based on releasable elastic strain energy, the method comprising the following steps:
[0054] Step 1: Based on the initial in-situ stress field and the cross-sectional shape of the tunnel excavation, the surrounding rock stress calculation model is established, such as figure 1 As shown, analyze the stress state of the surrounding rock after excavation, and calculate the elastic strain energy U of the surrounding rock after excavation 1 ,Such as image 3 s...
Embodiment 2
[0080] Figure 4 The average ejection velocity of rockburst fragments actually measured during the blasting and excavation of four different sections (sections A, B, C, D) of the tunnel and the average ejection velocity of rockburst fragments predicted by the existing means and the method of the present invention are given. Ejection speed, from Figure 4 It can be seen from the figure that compared with the existing method for predicting the ejection velocity of rockburst fragments, the method for predicting the ejection velocity of rockburst fragments based on releasable elastic strain energy provided by the present invention, the prediction results are closer to the measured data, and the prediction accuracy higher.
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