Scientific Journal of Earth Science December 2014, Volume 4, Issue 4, PP.206-214 Study on Formation Mechanism of Rock Burst and Rating Prediction Based on Artificial Neural Network in Rockmass Engineering
Xiaobo Xiong 1,2 1. College of architecture & civil engineering, Nantong University, Nantong 226019, China 2. Department of geotechnical engineering, Tongji University, Shanghai 200092, China # Corresponding Author Email: [email protected] Abstract
Rock burst is a type of geological disasters due to brittle surrounding rock excavation unloading in highland stress area. Prediction of rock burst takes much important significance in geotechnical engineering. In this paper, the author elaborated the mechanism of rock burst thorough. The author analyzed several factors of rockburst systematically. In this paper, the principle of neural network was introduced, and the NNT prediction model was established. The author have taken the four parameters as input values, including: n: acaccumulative number of events; lgE: acaccumulative released energy; lgV: the volume of accumulative; h: depth of pit. To utilize artificial neural network to build up the non-linear relationship between rock burst rating and the four factors. The results of rockburst also proved that the model has high accuracy, and has a good prospect in the prediction of rock burst. Keywords: Rockmass Engineering; Rockburst; Mechanism; Prediction; Neural Network
1 INTRODUCTION Rock burst is a type of instability geological disasters in high stress region. Rock burst threat to the safety of construction workers and equipment, and take effect on progress of project. Underground rock excavation unloading can touch off in brittle surrounding rock because of the sudden release of elastic strain energy; generate burst loose, peeling, and other damage. Problem of mechanism and prediction of rockburst has become the key scientific issues in rock mechanics and engineering fields. However, due to the properties of rockmass, complexity of geological conditions of rock burst, etc. Research of rock burst mechanism remained mostly in qualitative stage. Storage and release of geo-stress are important reasons to cause rockburst. Generally, mechanism of rock burst occurrence can be divided into: the strain kind of rock burst, the stereotype of rock burst, the strain and construct hybrid rock burst. Currently, many scholars have done research on strength, stiffness, stability, energy, fracture, damage, and mechanism of rock burst, and to form several theories, such as: strength theory of rock burst, stiffness theory, energy theory, and rock burst propensity theory. The neural networks are good at grasping the nonlinear relationship between the factors, which can simulate the functional abstract thinking of human brain. Rock burst event has law of time-spatial evolution, and energy conversion. Therefore, when mastered the evolution of microseismic information, we can predict the instant type of rock burst. This paper considers accumulative number of events; accumulative released energy; the volume of accumulative; depth of pit. To use artificial neural network to predict rock burst. FENG Tao, XIE Xuebin, WANG Wenxing, et al.(2000) present that: the brittleness index of rockmass is calculated based on using the strength of uniaxial stretching and the strain peak of rocks. WANG Qinghai, LI Xiaohong, GU Yi-lei, et al.(2003) presented rockburst phenomenon in the Jinping 2nd hydropower station, and collected a large data. These factors include teconics, attitude of rocks, structure, wall-rock intensity, and underground engineering
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arrangement, etc. Jiang Fanzhi, Xiang Xiaodong, Zhu Dongsheng, et al. (2003) described the current research status of rockburst domestic and abroad, in the aspects of the forming mechanism. GE Qifa, FENG Xiating. (2008) proposed a new method based on combination of ANN classifiers, and established the AdaBoost-ANN models. XU Mengguo, DU Zijian, YAO Gaohui, et al.(2008) obtained rocks from the depth underground -430 to -700 m. With the theoretical combined with the comprehensive prediction methods——fuzzy mathematics, synthesized prediction is made that the orientation of rockburst on those criteria. QIU Dao-hong, ZHANG Le-wen, XUE Yi-guo, et al.(2011) predicted the intensity of rock burst at later layer, to adopt in-site supervision measure and numerical calculation. Amoussou-Coffi Adoko, Yu-Yong Jiao, Li Wu, et al.(2013) Determind that the tunnel convergence was an indispensable task during tunnelling built by the NATM. In their research, an ANN model was established, the input parameters included: angle of internal friction, Young’s modulus, rock density, cohesion, etc.
2 BP NEURAL NETWORK’S LEARNING ALGORITHM Generally, the learning algorithms of BP-NN are divided into four stages: (1) The input mode is a layer by layer spread mode along the process which is from input layer (I) through the hidden layer (H) to the output layer (O); (2) The difference of the desired NN output (T) and the actual output (P), i.e. the error signal, it is a layer by layer correction connection weight of back propagation process which is from the output layer (O) via the intermediate hidden layer (H) to the input layer (I); (3) The forward propagation process and the back-propagation mode process constitute a round of NN training process, when it is repeated so many times, it can be taken the alternately memory training process of ANN; (4) To use dataset for learning of the network model, to meet the error conditions, Error <ε, the neural networks tend to converge eventually, so that the global error of NN tends to be the minimum value of learning convergence process. A 3-layer NN model contains an input layer, one hidden layer and an output layer, and the learning algorithm of BP neural network is:
k k k k T Set the input mode vector as X( x12 , x , , xn )( k 1,2, ,) m , where, n is the number of nodes in the input layer, m is the number of learning mode data;
k k k k T The corresponding output vector of input pattern is: Y (,,,) y12 y yq , where, q is the number of nodes in the output layer;
k k k k T k k k k T The corresponding input vector of hidden layer is: S (,,,) s12 s sp , the output vector is B (,,,) b12 b bp , where, p is the number of hidden layer nodes;
k k k k T k k k k T The input vector of output layer is: L (,,,) l12 l lq , and the actual output vector is: C (,,,) c12 c cq ; The connection weight value from the input layer to the hidden layer right is:
W{ wij |( i 1,2, ,; n j 1,2, , p )}, and the connection weight value from hidden layer to the output layer is
V{ wjt |( j 1,2, , p ; t 1,2, ,)} q ;
The threshold value of each unit in hidden layer is: {j }(jp 1,2, , ) , the threshold value of each unit in output layer is : {t }(tq 1,2, , ) . Specific steps of the algorithm are shown as follows: (1) Initialization. To assign random values for the connection weights W and V, and the threshold θ and γ, which is given in the interval of [-1, +1]. (2) Randomly selected a sample of data to constitute a learning mode pair (Xk, Yk), then supply for the network. Step (1) and (2) is the preparation process and initialization process to calculate the sample value.
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(3) To calculate the output of the input layer. Each processing unit in the input layer to the model is not processed; the output vector from the input layer is the same as the input pattern vector. (4) According to formula (1) and (2), to calculate the output and input of every neuron of intermediate hidden layer respectively:
n kk sj( w ij x i j ) ( j 1,2, , p ) (1) i1 kk bjj f( s ) ( j 1,2, , p ) (2) (5) According to formula (3) and (4), to calculate the input and actual output of each neuron of input layer:
p kk lt( v jt b j t ) ( t 1,2, , q ) (3) j1 kk ctt f( l ) ( t 1,2, , q ) (4)
Steps (3), (4) and (5) are the "mode of forward propagating" procedure for the input learning.
k (6) According to a desired output, according to the equation (5), to calculate the correction error ( dt ) of each neuron of the output layer
k k k k dt( y t c t )'() f l t ( t 1,2, ,) q (5) k (7) According to equation (6), to calculate the correction error ( e j ) of each neuron of hidden layer
q k k k ej v jt d t f'( s j ) ( j 1,2, , p ) (6) t1 (8) According to equation (7) and (8), to amend the connection weights (V) of output layer neurons, and to amend the threshold (γ) of the hidden layer, where, α is the learning rate, 0<α<1.
kk vjt d t b j ( j 1,2, , p ; t 1,2, ,) q (7) k tt d ( t 1,2, , q ) (8)
FIG.1 THE FLOW DIAGRAM TO SOLVE NEURAL NETWORKS
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(9) According to equation (9) and (10), to amend the connection weights W from input layer to output layer, and to amend the threshold valueθ of the hidden layer neurons, where, β is the learning rate, 0<β<1.
kk wij e j x i ( i 1,2, ,; n j 1,2, , p ) (9) k jj e ( j 1,2, , p ) (10) Step (6) - (9) is a "back-propagation" process of the error of neural network. (10)To select the next one learning mode randomly and provide to the network, return to (3), until the training of the mth learning pattern is completed. (11) To determine whether the global error (E) of neural network is calculated to meet the accuracy requirements, namely, E , if satisfied, come to the end, otherwise, continue to calculate. (12) Updated learning number of NNT, if the number of learning times is less than a predetermined set, return to (2), and continue. Step (10) - (12) are the process for learning, training and the convergence. (13) The end. The flow diagram to solve neural artificial networks (ANN) is shown as Fig. 1.
3 TO SET UP BP NEURAL NETWORK MODEL Tan Yi'an(1991) proposed that rockburst process can be divided into three stages: First, to split into the board, secondly, to cut into pieces, lastly, to form performance of the pieces of the catapult. The damage shape of rockburst could be divided into two kinds: shear failure and splitting failure. Domestic and foreign scholars have studied the prediction method of rock burst in underground engineering, and summarized the research status of prediction of rock burst. According to the mechanism of its occurrence, rock burst be divided into: the strain-type rock burst, the stereotype of rock burst, and the construct and strain hybrid rock burst. From the idea of energy point, unloading of stress is the process from deformation energy to kinetic energy. The kinetic energy of rockburst is equal to the difference of total deformation energy and destruction energy. Shang Yanjun, summed up more than twenty engineering examples of rockburst, in his study, proposed some discriminant strength function of the boundaries of rockburst. Qi Qingxin, proposed the theory of rock burst: the impact of rock tendency (the first factor), the geological structure (the second factor), and the stress (the third factor). There are several aspects of rockburst conditions factors affecting the rock burst, including lithology, rock occurrence of initial stress conditions, tectonics, rock structure, the impact of cross-sectional shape of the depth of engineering, underground engineering, construction. In this paper, took diversion tunnel and drainage tunnel of Jinping hydropower project as a case. Rating of rockburst has a very complex nonlinear function between the various parameters and microseismic. The parameters are: the accumulative number of events (N), accumulative release of energy (E), accumulative volume (V). Structure properties of rockmass determine the structure type of rock. Rock burst intensity will occur depends on the nature of its 3-dimensional structure in the form of a combination of nature and space structure. Complete rock accumulates a lot of elastic strain energy. The more the rigidity of rock mass is, the greater the likelihood of rock burst is. Therefore, taking into account the time factor, the microseismic parameters could be divided into: the one parameteris to reflect the total number of rock burst, deformation and strength accumulation parameter, acaccumulative number of events, the acaccumulative release of energy, the acaccumulative volume; the other parameter is to reflect the effect of rock broken time, average rupture velocity, energy, the event rate, the rate of energy release rate.
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Number of microseismic events: rock excavation will cause stress field to redistribute, and cause deformation of the surrounding rock in underground engineering, cracking, these changes will cause the rock stress wave propagation to the distant rockmass, every time the release of the elastic stress wave, it is a microseismic event. The accumulative number of microseismic events can be used with active and dynamic evolution of the trend of rock fracture evaluation. Rock quality indicators. Rock quality directly affects the amount of rock deformation and characteristics, the higher the quality of the rock is, the greater rigidity of rock is, according to the rock burst rigidity theory proposed by Cook and Hodgeim, the greater the likelihood of rock burst. The quality of the rock measure can be determined by the compressive strength of P-wave velocity and rock. But this more complicated, is not conducive to dynamic forecasting rock burst. The value of the rock quality indicators is easy to implement. The accumulative release of energy: to accumulate the energy within the cell of each microseismic event in underground space, to get the value of accumulative release of energy. When accumulated energy reaches to a certain amount of value, rock burst maybe occurs. Complex geological structure is prone to touch off rock burst, such as folds, dykes, faults and rock mutations and so on. Particularly, there is a big rock syncline stress of the shaft, accumulation of a large number of elastic deformation energy, once the excavation or mining, it is possible to produce rock burst. The accumulative of release energy, the more intense burst of rock, rock burst level is higher. Over the past decade, researchers from home and abroad to try to explain the occurrence of micro-mechanical mechanism point of rock burst. Rock microstructure analysis is part of the study of rock microstructure. Rock burst caused by rock fracture morphology, reflects the rock to a certain extent when the rock burst damage evolution process of the formation of the forces and structural damage characteristics. Rock burst formation process specific physical meaning, determine the nature of rock fracture, revealing the dynamic rupture mechanism of rock, build rock fracture of rock burst fracture morphology and mutation process of contact; fracture morphology study of rock fracture rock burst from the rock could be better reveals rock fracture morphology characteristics, crack growth, fracture mode expansion and evolution of rock burst and links. As can be seen from the study of various rock burst mechanism of the above, when various theories to explain the problem of rock burst, just from a different focus to analyse the rock burst phenomenon, various theories have their own limitations, to solve practical problems really, should be taken these various theories together, in order to better describe the actual rock burst problems. To depend on the accumulative volume: a reflection parameter of the surrounding rock on damage to the extent of the deformation. The rate of release of energy: In general, the microseismic events are released statistics by the number of days, the energy rate is reflected in important symbol evolution of rock burst strength. According to the intensity degree, rock burst summarized the main features are as follows: (1) From never rock burst to slight rock burst: wall rock steady to gradually deform, splitting, peeling, there is a slight sound, no ejection phenomenon, belong to the early stages of rock burst. (2) Medium rock burst: wall rock deformation and fracture, there is a considerable amount of rock-chip ejection, loose and sudden failure occurs, accompanied by crisp crackling sound, moderate rock burst. (3) From dramatic rock burst to strong burst: strong burst of wall rock, the rock roof and sidewalls serious rock-chip ejection, boulder projectile, serious or even destroy the entire engineering, to take a great impact on personal safety, cause serious economic losses. Therefore, considering the strength of rockburst rating and the main factors, rock burst can be classified as: no rock burst, minor rock burst, moderate rock burst, strong rock burst. ANN has a strong self-learning ability, non-linear dynamics ability. Thus, ANN is a powerful tool to solve problems of rock engineering. Use the neural network principle, the sample data for these examples to learn, to build the neural network model for prediction of rock burst grade. Among them, take the number of microseismic events, the
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accumulative release of energy, and depth of the pit etc., as the input variables of the NN model, rock burst rank as output parameters of neural networks. Prediction of rockburst rating is the use of measured data for the complex non-linear model. The process is: To use artificial neural network to establish the non-linear relationship between rock burst rating and the four factors. n: the accumulative number of events; h: Depth of Pit for rock burst; lgV: the accumulative volume; lgE: the accumulative released energy. The output of the NN Model is the Rating of Rock burst. The diagram of NN prediction model for rockburst rating is shown in Figure 2.
FIG 2. DIAGRAM OF NEURAL NETWORK PREDICTION MODEL FOR PREDICT RATING OF ROCKBURST To determine nodes of hidden layer The nodes of hidden layer take much effect on the performance properties of ANN model. The more the hidden layer nodes are, it may make the performance of ANN model better, but it may also lead to long training time. Currently, there is no ideal formula can be used to determine the number of the nodes in the hidden layer of ANN, generally, there are three empirical methods:
n i (1) CkM , where, k is the samples number, M is hidden layer neurons number, n is the number of input layer i0 i neurons. if, i > M, then CM 0 . (2) M n m a , where, n and m are represent respectively the number of neurons of the output layer and input layer, a[0,10], a = const.
n (3) M log2 , where, n is the number of input layer neurons. When the network layer is determined, to increase the nodes of neurons in the hidden layer can improve the training accuracy. Generally, the more of the nodes of the hidden layer, the more the network could approximate fit the nonlinear curve, but too much of the hidden layer neurons will decrease the convergence rate. In theory, it has been proven that the BP-NN can approximate come to any differentiable function at any precision, when the 3-layer back-propagation NN with the input layer nodes (m) and with a 2m + 1 hidden layer nodes. In this paper, the prediction model has four input variables; hidden layer neuron number is set to 6. To select one of the 51 sets of data as a trained network of learning samples from collected data. The training sample data are shown in Table 1. Training samples and test samples of Neural network Warning Method for rock burst.
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TABLE 1. TRAINING SAMPLES OF NEURAL NETWORK WARNING METHOD FOR ROCK BURST n h lgV lgE Rating 17 0.5 5.015 3.172 1 2 0 3.250 1.940 0 42 1.9 5.050 6.284 3 21 1.0 4.834 5.848 2 45 1.8 4.838 4.803 3 5 0 3.878 2.435 0 13 0.5 4.428 4.408 1 44 2 4.865 5.459 3 8 0.3 3.977 5.204 1 17 0.9 4.397 4.754 2 15 0 5.030 3.486 0 49 1.6 4.995 6.419 3 14 0.9 4.266 4.818 2 4 0 3.728 5.820 0 24 1 4.660 4.748 2 29 0.5 4.156 3.882 1 18 0.9 4.779 5.602 2 4 0 4.173 4.737 0 11 0.8 4.141 5.926 2 2 0.1 3.576 4.061 1 8 0.6 4.552 5.219 2 2 0 2.908 1.39 0 8 0.6 4.620 5.621 2 11 0.4 4.944 4.029 1 70 2.5 5.152 6.147 3 1 0 3.441 0.78 0 8 0.4 3.504 4.132 1 1 0 3.441 0.78 0 20 0.5 3.843 4.760 1 49 2.2 5.168 6.373 3 13 0 4.780 5.348 0 12 0.4 4.223 3.543 1 36 1.2 4.336 3.729 2 10 0.4 4.370 4.446 1 2 0 2.936 5.160 0 9 0.4 4.993 1.723 1 20 1 4.453 5.982 2 7 0.4 4.817 5.269 1 3 0 4.603 3.616 0 58 1.5 4.975 7.094 3 25 0.6 4.848 4.381 1 1 0 4.310 1.540 0 16 0.4 4.681 3.621 1 21 1 4.732 3.543 2 17 0 4.844 4.619 0 12 0.4 4.565 4.912 1 31 1.1 4.627 5.008 2 8 0 2.511 2.197 0 3 0.5 4.438 5.06 2 7 0 3.018 4.30 0 41 1.8 4.694 5.968 3 n: the accumulative number of events; lgE: the accumulative released energy; lgV: the accumulative volume; h: Depth of Pit for rock burst; Rating of Rock burst. Training samples and test samples of neural network warning method for rock burst, the test samples of NN for rock burst was shown in Table 2.
TABLE 2. TEST SAMPLES OF NEURAL NETWORK WARNING METHOD FOR ROCK BURST 22 1.7 4.895 5.859 3 18 0.9 4.703 5.295 2 25 0.5 4.964 3.367 1
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12 0.7 3.516 5.098 2 5 0 3.279 3.996 0 6 0.4 3.497 4.368 1 n: the accumulative number of events; h: Depth of Pit for rock burst; lgV: the accumulative volume; lgE: the accumulative released energy; Rating of Rock burst.
4 CONCLUSIONS (1) The physical properties and mechanical properties of the rockmass are complexity, and geotechanical conditions and factors affected the construction of rock, to make the mechanism of rock burst to be extremely complex. In this paper, the author summarized some factors affected the rock burst, including: lithology, initial stress conditions, tectonics, rock structure, the impact of cross-sectional shape of engineering, underground engineering. (2) In this paper, considering the variety factors of rock burst, using four parameters (n: the accumulative number of events; h: depth of rock burst pit; lgV: the accumulative volume. lgE: the accumulative released energy). As indicators such as the establishment of neural network prediction model of rock burst. Take Jinping 2nd hydropower station as a sample of rock burst instance, through study of NN model, training of NN model, so that the trained network to reflect the degree of the mapping between rock burst and the impact of various factors, to illustrate the effectiveness of this model. (3) Prediction of rockburst rating is the use of measured data for complex non-linear model. The rockburst process is very complex, this would require a sample database, and steadily improved database. The impact of factors to be considered more fully by more samples, and the accuracy of pre-side network continues to increase.
ACKNOWLEDGMENT This work was supported by Opening Fund of Ministry of Education Key Laboratory of Geotechnical and Underground Engineering (Grant No. KLE-TJGE-0802). And this work was also supported by Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology).
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Author
1 Xiaobo Xiong (1972- ), Male, Han nationality, Ph.D., Associate Professor, School of Civil Engineering, Nantong University, Master Instructor. Research direction: Geotechnical and Structural Engineering, Intelligent Application. Email: [email protected]
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