مدل پیش بینی بلند مدت سنگ های پشت سر هم در دهانه های زیرزمینی با استفاده از الگوریتم های هیوریستیک و ماشین بردار پشتیبانی

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σθ, rocks’ uniaxial compressive strength σc, rocks’ uniaxial tensile strength σt, stress coefficient σθ/σc, rock brittleness coefficient σc/σt and elastic energy index Wet. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.

A rockburst (Ortlepp, 1997) is a sudden and violent expulsion of rock from the surrounding rock mass. In China, with the increase of mining depth and conditions become more complex, as well as more and more large-scale underground projects are under construction in the deep zone of intense tectonic activities, such as water conservancy and hydropower, transportation, defense and basic physics experiments. These projects provide an important opportunity for the progress of geo-engineering. However, serious challenges also exist because of the complicated geological conditions and potential geological hazards (especially high-strength rockburst) during tunnel construction, causing great losses of life and property. Only from 2001 to 2007, China has deep engineering metal mine disaster as a result of accidents amounted to more than 13,000, the death people exceed 16,000 and resulted in substantial valuable resources can not mine; In recent years, in the field of water and traffic engineering, every year due to disaster-induced deep engineering projects have up to thousands of accidents, the number of casualties have nearly a thousand people, many engineering schedule delays more than six months or even 1 year, tens of millions or even billions of machinery and equipment obsolescence, huge economic losses (see http://www.whrsm.ac.cn/zt/emdct973/xmgk/). And with the increase of mining depth, in situ stress shows a linear or nonlinear increasing tendency, ground temperature increases, the osmotic pressure of groundwater will be further raised, rock become hard and brittle, deteriorating geological conditions, the risk of rock burst will be further increased. Therefore, there is a need for the development of suitable computational methods for the prediction and control of rockbursts particularly for a safe and economical underground excavation for construction or mining in burst-prone ground (Sun and Wang, 2000 and Board and Fairhurst, 1983). For this case, since the first record of rockburst appeared at a tin mine in Britain in 1738, numerous related research works, concerning about the mechanism, characteristics or type, formation condition and disaster control of rock burst have been conducted by many researchers (Deng et al., 2011 and Ortlepp and Stacey, 1994). For example, Cook et al. (1966) provided a theoretical method of predicting rockburst based on the opinion that violent damage of rock occurs when an excess of energy becomes available during the postpeak deformation stage. Casten and Fajklewicz (1993) analyzed the rock-burst risk in the case of the Dickebank seam coal mines (Casten and Fajklewicz, 1993); Shivakumar et al. (1996) summarized the spatial distribution characteristics of rockburst at the Kolar Gold Mines. In order to understand the rockburst mechanism, Zubelewicz and Mroz (1983) simulated the dynamic instability of rock burst; Linkov (1996) studied the rockburst in an unstable rock mass and Jiang et al. (2010) presented the local energy release rate (LERR) to simulate the conditions causing rockburst. Kaiser et al. (1996) developed a handbook to guide rockburst support based on energy release approaches. As CRRP (1996) pointed out, rock burst is a violent failure phenomenon associated with (not caused by) a seismic event, which can be classified into three types: fault-slip burst, strainburst and conbination burst. Currently, based on the time and scope of rockburst prediction, the methods for predicting rockburst hazard degrees can be classified into two categories: long-term forecasts and short-term small-scale regional forecasts (Peng et al., 2010). Short-term prediction of rock burst is measured by means of some underground rock scene or the prediction of rock burst phenomena, determining the exact location of rock burst occurrence and the specific time, the current short-term forecast model is acquired some useful eigenvalue by the appropriate field measurement method, then established a mathematical model to analyze the amount of features, and then prediction the rockburst. Several on-site testing methods commonly used are: the photoelastic method, method of drilling bits, the resistance method, seismic parameters and rock mechanics methods, the microseismic monitoring system method, microgravity method, geophysical prospecting covers seismic, electromagnetic, geological radar and acoustic emission method (Jha and Chouhan, 1994, Mansurov, 2001, Frid, 1997, Alcott et al., 1998 and Dowding and Andersson, 1986). For example, Brady and Leighton recorded a seismicity phenomenon before a moderate rock burst (Brady and Leighton, 1977) while Tang et al. introduced the feasibility in principle of monitoring and prediction of rockbursts using microseismic monitoring techniques during tunnel construction of Jinping II hydropower station (Tang et al., 2010). Tang and Xia presented a seismological method for prediction of areal rockbursts in deep mine on the basis of the seismic source mechanism and unstable failure theory (Tang and Xia, 2010). Long-term prediction of rockburst refers to a preliminary prediction of rockburst trend and qualitative judgments in project regions during initial project, currently long-term prediction of rockburst is mainly used by: • Theoretical prediction method from various perspectives (Tang et al., 2010 and Shi et al., 2010), such as the strength theory, the rigidity theory, the burst liability theory, the energy theory, the instability theory, the catastrophe theory, the bifurcation theory, the theory of dissipative structures and the theory of chaos have been proposed to study of deformation localization and stability of the mechanical system in rock. • Nonlinear science method, such as Pan et al. (2006) proposed a catastrophe theory to analysis of circular tunnel rockburst, Xie and Pariseau (1993) investigated the rockburst mechanism and prediction methods based on fractal geometry, and bifurcation and chaos theory, etc. • Criterion values method (Tang et al., 2010 and Chen et al., 2009), such as the energy criterion method, impact orientation criterion method, depth prediction critical, and to a variety of rock burst of factors considered in comprehensive evaluation. Lately, Zhang and Fu (2008) tried to establish five factors comprehensive criterion for strain-mode rockburst and its classification. • Prediction rockburst method based on the priori knowledge, which can be extracted features samples and accessed knowledge from the case base, achieving to predict future requirements according to prior knowledge (Peng et al., 2010). Therefore, many theoretical and numerical models and technical means were developed to analyze the occurrence of rockbursts, it will have a better scientific and practical results to predict the future of rockburst using examples which has already the occurrence of rock burst in rock engineering. To this end, Feng and Wang (1994) described a novel approach to predict probable rock bursts in underground openings based on learning and adaptive recognition of neural networks. Subsequently developed a new artificial intelligence methods and theories have been successfully introduced into the prediction of rock burst. To achieve good results, Wang et al. (1998) proposed a fuzzy comprehensive evaluation method for rockburst prediction based on fuzzy mathematics. Recently, in terms of rockburst prediction, Chen et al. (2009) and Zhang et al. (2010) proposed an extenics evaluation method for rockburst prediction; Zhao (2005) presented a rockburst classification method based on support vector machines; Gong and Li (2007) applied the discriminant analysis method to rockburst prediction. Shi et al. (2010) established an unascertained measurement classifying model to predict the possibility and classification of rockburst. A efficacy coefficient method for predicting the classification of rockburst was proposed by Wang et al. (2010). A fisher discriminant analysis (FDA) model for the prediction of classification of rockburst in deep-buried long tunnel was established by J. Zhou et al. (2010). Yang et al. (2010) presented a model of predicting the possibility and classification of rock burst based on the combination of rough set and fuzzy set theory. These studies offered new ideas and approaches for rockburst prediction. As an important means, numerical methods and laboratory rock burst test have been adopted by many researchers gradually in recent years. A finite element model is proposed by Sharan (2004) to predict the potential occurrence of rockburst in underground openings. Single-face dynamic unloading tests under true-triaxial condition were carried out by He et al. (2010) for Paleozonic marine sedimentation limestone samples produced by blocks cored from 1140 m depth in Jiahe coal mine of China. Zhu et al. (2010) proposed a numerical model to simulate on rockburst of underground opening triggered by dynamic disturbance using the rock failure process analysis (RFPA). However, due to the complexity of rock mass and the variety of influencing factors, it is very difficult to predict space–time distribution of rockburst exactly. The results of various prediction methods should be analyzed comprehensively. Moreover, each method has its own advantages and disadvantages, and understanding, predicting and controlling the rockbursts still pose a considerable challenge for underground engineering. The Support vector machines (SVMs) is an efficient machine learning (ML) technique derived from statistical learning theory by Vapnik (1995), and has demonstrated its good performance in classification, regression, and time series forecasting and predition (Zhao and Yin, 2009, Kovačević et al., 2010, Khandelwal, 2010 and Jiang et al., 2011) in geotechnical practice and mining science, paving the way for numerous practical applications (Guyon and Christianini, 1999). For this case, in order to establish a more objective and universal nature of the prediction model, this paper presents a long-term rockburst prediction model for underground openings based on SVMs theory, and takes influencing factors of rockburst as input vectors, the grade of rockburst as output vector, simulating accurately the complicated nonlinear relations between the rockburst and its influencing factors. However, SVMs also has some factors that affect the prediction performance – these factors are usually set by non-heuristics (trial-and-error method; grid search method (GSM)). For searching optimal parameters, genetic algorithm (GA) (Goldberg, 1989, Gunyon et al., 2002 and Mib et al., 2006) and particle swarm optimization (PSO) (Kennedy and Eberhart, 1995, Ren and Bai, 2010 and Zhou et al., 2010) heuristic algorithm are introduced to optimization SVMs parameters, and then predict the rockburst proneness. In the current study, SVMs optimized by grid search method (GSM) and heuristic algorithms (genetic algorithm, GA; particle swarm optimization, PSO) are proposed to forecast rockburst, GSM, GA and PSO are to find the optimal settings of parameters in SVMs. The novel SVMs forecasting method is researched firstly, then the rockburst forecasting model is constructed by SVMs optimized by grid search method (GSM-SVMs), genetic algorithm (GA-SVMs) and particle swarm optimization (PSO-SVMs). Finally, the rockburst forecasting cases are used to testify the forecasting performance of the proposed model.

In this work, a prediction model of long-term rockburst is established by SVMs. According to mechanism of rockburst, buried depth H, rocks’ maximum tangential stress σθ, rocks’ uniaxial compressive strength σc, rocks’ uniaxial tensile strength σt, stress coefficient σθ/σc, rock brittleness coeffieient σc/σt and elastic energy index Wet are defined as the criterion indices for rockburst prediction in the proposed model. Two hybrid techniques for rockburst classification of high dimensional data were presented and compared. These techniques are based on different metaheuristic algorithms such as PSO and GA used for parameter selection using the SVMs classifier to identify potentially good gene subsets, and GSM by an accurate 5-fold cross validation method is also used for SVMs classifier to improve the actual classification. In order to examine the reliability of the model in the SVMs and investigate different input parameters impact on the predicted results, which avoids over-fitting or underfitting of the SVMs model occurring because of the improper determination of these parameters. The field datasets are used to investigate its feasibility in the rockburst prediction for underground openings. The experimental results show that the classification accuracies of PSO-SVMs is more than 90% and superior than the GA-SVMs and GSM-SVMs; whereas the accuracy of the GA-SVMs is comparable to the GSM-SVMs, and by comparing the evaluation results among three models, it is indicated that PSO-SVMs has more excellent prediction performance than GA-SVMs and GSM-SVMs for long-term rockburst prediction in underground openings.