CN113536594B - Fiber-reinforced filling body fracture prediction method - Google Patents

Abstract

The invention discloses a method for predicting the rupture of a fiber-reinforced filling body, which includes steps 1, preparing a filling body sample; 2, performing an acoustic emission test on the filling body sample under uniaxial compression; Acoustic emission amplitude sequence, and the time series of the ratio of acoustic emission rise time to acoustic emission peak amplitude; 4. Calculate the correlation dimension and phase space dimension of the filling body sample, and draw the relationship curve between correlation dimension and phase space dimension Fig. 5. Using fractal theory to predict the fracture of filling body samples. The method of the invention has simple steps and is convenient to realize. The fractal dimension of the amplitude fractal dimension and the ratio of the acoustic emission rise time to the peak amplitude of the acoustic emission can truly reflect the information of the rupture of the fiber-reinforced filling material itself, and the requirements for the approach of the fiber-reinforced filling are not high. , the data is easy to obtain, the accuracy of the data is high, and the data processing is convenient. It can be effectively applied to the fracture prediction of fiber-reinforced fillings, and the effect is remarkable, and it is easy to popularize.

Description

A Fracture Prediction Method of Fiber Reinforced Filling Body

technical field

The invention belongs to the technical field of mine filling and mining, and in particular relates to a fracture prediction method of a fiber-reinforced filling body.

Background technique

With the continuous development and utilization of mineral resources, the reserves of underground mineral resources are decreasing day by day, and the mineral resources with shallow burial depth, stable surrounding rock and high grade have been exploited. As the mining work goes deep, affected by the high ground stress, the stability of the surrounding rock is poor, and the filling mining method has become the first choice to ensure safe underground mining. Therefore, the mechanical properties of the filling body are the core issues that engineers and technicians are very concerned about, and their mechanical properties are affected by many factors, such as matrix material, lime-sand ratio, and external reinforcing agent. Adding reinforcing agent is one of the favorable means to enhance the stability of the filling body. Among them, fiber is a very effective reinforcing agent. The fiber can not only enhance the strength of the filling body, but also allow the filling body to maintain a certain compression resistance after the stress peak ability without being destroyed.

However, there is still a lack of effective methods for predicting the fracture process of fiber-reinforced fillings in the prior art.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method for predicting the rupture of fiber-reinforced filling bodies in view of the deficiencies in the above-mentioned prior art. The method has simple steps and is easy to implement. The fractal dimension of the peak amplitude ratio truly reflects the information of the rupture of the fiber-reinforced filling material itself, and the requirements for the proximity of the fiber-reinforced filling are not high. In the fracture prediction of filling body, the effect is remarkable and it is easy to popularize.

In order to solve the above-mentioned technical problems, the technical solution adopted in the present invention is: a method for predicting the rupture of a fiber-reinforced filling body, comprising the following steps:

Step 1. Prepare filling body samples, the filling body samples include fiber-free filling body samples, polyacrylonitrile fiber-reinforced filling body samples, glass fiber-reinforced filling body samples, polyacrylonitrile and glass mixed fiber-reinforced filling Body sample;

Step 2, performing an acoustic emission test under uniaxial compression on the filling body sample;

Step 3, counting the AE amplitude series of the filling body sample, and the time series of the ratio of AE rise time to AE peak amplitude;

Step 4, calculating the correlation dimension and phase space dimension of the filling body sample, and drawing the relationship curve between the correlation dimension and phase space dimension;

Step 5, using fractal theory to predict the fracture of the filling body sample.

In the method for predicting the rupture of a fiber-reinforced filling body, the specific process of preparing the filling body sample in step 1 includes: using a standard triple mold of 70.7mm×70.7mm×70.7mm to make the filling body sample, and the filling The lime-sand ratio in the body sample is 1:10.

In the aforementioned fracture prediction method of fiber-reinforced filling body, the correlation dimension of the filling body sample is calculated by using the G-P algorithm, and the relationship between the correlation dimension and the phase space dimension is drawn by MATLAB.

In the aforementioned method for predicting the rupture of a fiber-reinforced filling body, the specific process of using the fractal theory to predict the rupture of the filling body sample in step five includes: drawing the curve of stress and fractal dimension, and calculating the filling body sample The law of fractal dimension change with stress was analyzed, and the fracture process of fiber reinforced filling was predicted by fractal dimension.

In the aforementioned rupture prediction method of fiber-reinforced filling bodies, the fractal dimension includes amplitude fractal dimension and acoustic emission rise time to acoustic emission peak amplitude ratio fractal dimension.

Compared with the prior art, the present invention has the following advantages: the method of the present invention has simple steps and is convenient to implement, and adopts the fractal dimension of the amplitude fractal dimension and the ratio of the acoustic emission rise time to the peak amplitude of the acoustic emission to truly reflect the rupture of the fiber reinforced filling body material itself Information, the requirement for the proximity of fiber-reinforced fillings is not high, the data is easy to obtain, the accuracy of the data is high, and the data processing is convenient. It can be effectively applied to the fracture prediction of fiber-reinforced fillings, the effect is remarkable, and it is easy to popularize.

The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

Description of drawings

Fig. 1 is method flowchart of the present invention;

Fig. 2 is a relational graph of the correlation dimension and the phase space dimension of the filling body sample of the present invention;

Fig. 3 is the stress-fractal dimension diagram of the fiber-free filler sample of the present invention;

Fig. 4 is the stress-fractal dimension diagram of the polyacrylonitrile fiber reinforced filler sample of the present invention;

Fig. 5 is the stress-fractal dimension diagram of glass fiber reinforced filler sample of the present invention;

Fig. 6 is a stress-fractal dimension diagram of a polyacrylonitrile and glass mixed fiber reinforced filling body sample of the present invention.

Detailed ways

As shown in Figure 1, the fracture prediction method of the fiber-reinforced filling body of the present invention comprises the following steps:

Step 1. Prepare filling body samples, the filling body samples include fiber-free filling body samples, polyacrylonitrile fiber-reinforced filling body samples, glass fiber-reinforced filling body samples, polyacrylonitrile and glass mixed fiber-reinforced filling Body sample;

In this embodiment, a standard triple mold of 70.7mm×70.7mm×70.7mm is used to make a filling body sample, and the lime-sand ratio in the filling body sample is 1:10.

Step 2, performing an acoustic emission test under uniaxial compression on the filling body sample;

Step 3, counting the AE amplitude series of the filling body sample, and the time series of the ratio of AE rise time to AE peak amplitude;

During specific implementation, the ratio of the acoustic emission rise time to the peak amplitude of the acoustic emission is referred to as the RA value.

Step 4, calculating the correlation dimension and phase space dimension of the filling body sample, and drawing the relationship curve between the correlation dimension and phase space dimension;

In this embodiment, the correlation dimension of the filling body sample is calculated using the G-P algorithm, and the relationship between the correlation dimension and the phase space dimension is drawn by MATLAB.

During specific implementation, the acoustic emission amplitude sequence of the filling body sample and the time sequence of the ratio of the acoustic emission rise time to the peak amplitude of the acoustic emission are regarded as an equally spaced time sequence X:

X＝{x ₁ ,x ₂ ,x ₃ ,..., _xi }

Then, use these data to support the m-dimensional sub-phase space, that is, first obtain the first m data, and determine the first point in the m-dimensional space, and record it as X ₁ :

X ₁ ={x ₁ ,x ₂ ,x ₃ ,...,x _m }

Then delete x ₁ , and take m data x ₂ , x ₃ ,...,x _m+1 in turn to form the second point in the m-dimensional space, denoted as X ₂ , thus constructing a series of phase points:

Connect the phase points X ₁ , X ₂ ,...X _i ,... in turn to get a trajectory, let the time series generate the phase points X ₁ , X ₂ ,...,X _N in the m-dimensional phase space, given a Number r, check that there are several pairs of points (X _i , X _j ) whose distance |X _i -X _j | is less than r, and record the proportion of the points whose distance is less than r to the total number of points N ² , which is recorded as C(r):

Among them: θ(x) is the Heaviside function, if the value of r is too large, the distance of all points will not exceed it, C(r)=1, lnC(r)=0, then measure If the relationship between phase points is not shown, if the measurement scale r is appropriately reduced, there may be in a range of r:

C(r)∝r ^D

If such a relationship exists, then D is a dimension called the correlation dimension, then:

The relationship curve between the correlation dimension D and the phase space dimension m of the filling body sample is calculated and drawn through the MATLAB program, as shown in Figure 2.

From Figure 2(a) to Figure 2(d), it can be seen that when the phase space dimension m is in [4,7], the correlation dimension D curve mostly tends to a stable linear change, and the phase space dimension m is taken as 6. It can be seen from Figure 2 that the minimum value of the amplitude correlation dimension D of the non-fibrous filling body sample is greater than 1, and the size difference between the amplitude correlation dimension D and the RA correlation dimension D is less than 1, and With the increase of the phase space dimension m, the overall trend of the correlation dimension of RA rises very flat and shows a linear trend; the relationship between the amplitude correlation dimension D and the RA correlation dimension D There is little change between them. As the phase space dimension m increases, the correlation dimension curve changes linearly and the overall upward trend is very close.

Step 5, using fractal theory to predict the fracture of the filling body sample.

In this embodiment, by drawing the curve diagram of stress and fractal dimension, the law of the fractal dimension of the filling body sample changing with the stress is analyzed, and the fracture process of the fiber reinforced filling body is predicted through the fractal dimension; the fractal dimension Including amplitude fractal dimension and fractal dimension of the ratio of AE rise time to AE peak amplitude.

During the specific implementation, it can be seen from Figure 3 that in the compaction stage (10-30s), the amplitude fractal dimension is at a relatively high level, and the internal pores of the sample are compacted at this stage, resulting in fewer microcracks; when entering In the linear elastic stage (30-40s), it can be found that the amplitude fractal dimension curve has decreased, indicating that some slightly large-scale cracks have occurred in the sample, but the number is small; then it enters the crack expansion stage (40-60s) , it can be seen that the value of the fractal dimension of the amplitude has a small steep drop, indicating that a large acoustic emission event has occurred at this time, and the micro-cracks are evolving towards the macro-cracks; when the stress enters the peak stage (about 100s), the amplitude can be clearly seen The fractal dimension value dropped from 3.0 to 2.4, indicating that there were many large-scale acoustic emission events, and large-scale cracks had basically formed; in the subsequent residual stage, it can be seen that the amplitude fractal dimension curve presents a relatively sparse change with a large fluctuation range. This shows that the large-scale cracks are still increasing, and the sample is basically destabilized and destroyed.

The change trend of the whole stress state of RA fractal dimension is similar to that of amplitude fractal dimension. At the peak stress, the RA fractal dimension decreases and fluctuates more than the amplitude fractal dimension. The difference is that the RA fractal dimension and amplitude fractal dimension of the non-fiber filling body are denser at the peak, while the fiber-reinforced filling body The amplitude fractal and RA fractal are relatively dense after the peak; the amplitude fractal and RA fractal symmetry are more obvious on the whole strain curve of the fiber-reinforced filling body, while the symmetry of the amplitude fractal and RA fractal is more obvious on the whole strain curve of the fiber-reinforced filling body Not obvious.

The change of the RA fractal dimension curve and the amplitude fractal dimension curve generally present a symmetrical trend, that is, when the RA fractal dimension curve rises, the amplitude fractal dimension curve decreases; For the sample, the amplitude fractal dimension and RA fractal dimension curve fluctuate less up and down in the post-peak stage, showing a steady and progressive failure; as shown in Figure 3, for the filling body sample with relatively poor toughness, the residual It shows a large fluctuation up and down and there is an unstable damage.

In the fiber-reinforced filling body, the stress-strain curve has a relatively long period of residual deformation in the post-peak stage, and the corresponding fractal dimension has a small fluctuation up and down, and the fractal dimension curve is dense, and the amplitude of the fractal dimension fluctuation The size depends on the strength of the fiber toughness and the strength of the filling body after reinforcement; at the same time, at a certain moment in the residual stage, the fractal dimension value will also increase and decrease sharply, indicating that in the residual stage after the peak, the large-scale macroscopic Cracks will still appear; in particular, for the filling body with relatively good toughness, the fractal dimension value in the residual stage will not appear the phenomenon of steep rise and fall of the fractal dimension curve with a large slope, while the material with relatively low toughness For the fractal dimension value of the sample, there will be more steeply decreasing fractal dimension curves, indicating that more fibers are broken after the peak, and the filling sample is damaged in a large range.

The above are only preferred embodiments of the present invention, and do not limit the present invention in any way. All simple modifications, changes and equivalent structural changes made to the above embodiments according to the technical essence of the present invention still belong to the technical aspects of the present invention. within the scope of protection of the scheme.

Claims (3)

1. A method of predicting fracture of a fiber-reinforced infill, comprising the steps of:

preparing a filling body sample, wherein the filling body sample comprises a non-fiber filling body sample, a polyacrylonitrile fiber reinforced filling body sample, a glass fiber reinforced filling body sample and a polyacrylonitrile and glass mixed fiber reinforced filling body sample;

secondly, carrying out acoustic emission test on the filling body sample under uniaxial compression;

thirdly, counting an acoustic emission amplitude sequence of the filling body sample and a time sequence of a ratio of acoustic emission rise time to acoustic emission peak amplitude;

calculating the correlation dimension and the phase space dimension of the filling body sample, and drawing a relation curve graph of the correlation dimension and the phase space dimension;

step five, adopting a fractal theory to carry out fracture prediction on the filling body sample;

analyzing the rule that the fractal dimension of the filling body sample changes along with the stress by drawing a curve graph of the stress and the fractal dimension, and predicting the fiber reinforced filling body cracking process through the fractal dimension; the fractal dimension comprises an amplitude fractal dimension and a fractal dimension of a ratio of sound emission rise time to sound emission peak amplitude.

2. The method for predicting rupture of a fiber-reinforced packing according to claim 1, wherein the step one of preparing a sample of the packing comprises: a standard three-joint die with the thickness of 70.7mm multiplied by 70.7mm is adopted to prepare a filling body sample, and the sand-lime ratio of the filling body sample is 1.

3. The method for predicting fracture of a fiber-reinforced packing according to claim 1, wherein the correlation dimension of the packing sample is calculated using a G-P algorithm, and a graph of the correlation dimension and the phase space dimension is plotted by MATLAB.

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