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 - 206 - http://www.j-es.org 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. - 207 - http://www.j-es.org (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 - 208 - http://www.j-es.org (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.