Knowledge-based and data-driven fuzzy modeling for rockburst prediction

Adoko, Amoussou Coffi; Gökçeoğlu, Candan; Wu, Lenan; Zuo, Qing Jun

doi:10.1016/j.ijrmms.2013.02.010

Cited by 192 publications

(57 citation statements)

References 23 publications

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“…Quantitative statistical analysis was conducted through a large number of geological survey data and seismic data [205]. Adoko et al [206] used fuzzy inference system (FIS), adaptive neurofuzzy inference systems (ANFIS), and field measurements data to predict rockburst intensity. He et al [207] applied data mining (DM) techniques to the database to develop predictive models for the rockburst maximum stress and rockburst risk index that required the determination of the test results.…”

Section: Some Comprehensive Analysis Methods Based On Statistical Thementioning

confidence: 99%

Dynamic Stability Analysis of Rockmass: A Review

Dong

Wang

et al. 2018

Advances in Civil Engineering

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ere are a series of human or natural activities, including earthquakes, explosions, and rockbursts, which have caused a number of safety accidents in geotechnical engineering. is review paper summarized the theories and methods for dynamic stability analysis of rockmass. First, numerical simulation methods, including finite element method (FEM), discrete element method (DEM), finite difference method (FDM), boundary element method (BEM), discontinuous deformation analysis method (DDA), and numerical manifold method (NMM), are summarized. Second, the laboratory experiments, containing shaking table test, split-Hopkinson pressure bar test (SHPB), improved true-triaxial test, dynamic centrifugal model test, acoustic emission (AE) technique, and dynamic infrared monitoring, are considered. ird, the in situ tests including microseismic (MS) technique, velocity tomography, stress-strain monitoring, and electromagnetic radiation monitoring are considered. Finally, some comprehensive analysis methods based on statistical theories are also provided. It is pointed out that the study foundation for the dynamic stability of rockmass is weak to explain the mechanism. So, a set of general comprehensive theories integrating the different methods, including theoretical analysis, numerical methods, laboratory experiments, and in situ tests, should be completely established. is is the most effective way for further investigation.

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Section: Some Comprehensive Analysis Methods Based On Statistical Thementioning

confidence: 99%

Dynamic Stability Analysis of Rockmass: A Review

Dong

Wang

et al. 2018

Advances in Civil Engineering

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“…remote sensing and GIS based landslide susceptibility assessment [6]; landslide susceptibility mapping [7][8][9]; early warning landslide susceptibility model using geographic information system (GIS) [10]; regional prediction of landslide hazard [11]; predicting of rockburst classification [12,13]; predicting destructive effect of masonry structure under blasting vibration of open-pit mine [14]; prediction of seismic liquefaction of sand soil [15]; classification of rocks surrounding in tunnel [16,17]; classification of top coal cavability of the steep seam [18]; comprehensive evaluation for seismic stability of slopes [19]; prediction rock mechanical behaviors [4]; predicting landslide deformation [20,21]; predicting of P-wave velocity and anisotropic property of rock [22]; estimating rock properties using sound levels produced during drilling [23]; automated tunnel rock classification using rock engineering systems [24]; estimation of the rock mass deformation modulus using a rock classification system [25]; predicting blast disaster in open pit blasting operation [26,27]; evaluation of penetration rate of tunnel boring machine in hard rock condition [28]; comparative study of cognitive systems for ground vibration measurements [29]; prediction of longitudinal wave velocity [30]; optimization of tunnel construction [31]; prediction of the rock mass diggability Mathematical Problems in Engineering index [32]; prediction of rock properties from sound levels produced during drilling [5]; modeling mine gas gushing forecasting on virtual environment [33]; rainfall reliability evaluation for stability of municipal solid waste landfills on slope [34]; determination of reservoir induced earthquake [35]; seismic event identification…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers

Dong

2013

Mathematical Problems in Engineering

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The relationships between geological features and rockmass behaviors under complex geological environments were investigated based on multiple intelligence classifiers. Random forest, support vector machine, bayes' classifier, fisher's classifier, logistic regression, and neural networks were used to establish models for evaluating the rockmass stability of slope. Samples of both circular failure mechanism and wedge failure mechanism were considered to establish and calibrate the comprehensive models. The classification performances of different modeling approaches were analyzed and compared by receiver operating characteristic (ROC) curves systematically. Results show that the proposed random forest model has the highest accuracy for evaluating slope stability of circular failure mechanism, while the support vector Machine model has the highest accuracy for evaluating slope stability of wedge failure mechanism. It is demonstrated that the established random forest and the support vector machine models are effective and efficient approaches to evaluate the rockmass stability of slope.

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“…However, few studies were tried to combine the various factors relating to rockburst hazard. Recently, some interesting models were derived using artificial intelligence, such as a neural network (Chen et al 2002), fuzzy theory (Adoko et al 2013;Wang et al 2015), and distance discriminant analysis method (Gong et al 2007), along with other integrated analysis methods. These research results indicated that the occurrence of rockbursts was closely related to the mechanical properties of rockmass, the geological structure, and the surrounding stress.…”

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confidence: 99%

“…In recent years, this highly practical theory and method were achieved many gratifying results in the field of engineering technology. In addition, artificial intelligence methods were used, such as fuzzy inference system (FIS) and adaptive neuro-fuzzy inference systems (Adoko 2013) and Rough set theory and genetic algorithms (Yu 2009). These were seismological theory and methods were used to predict the rockburst such as the peak velocity and dynamic energy, the seismic risk assessment method and mining and seismological parameters (Srinivasan et al 1997;Li et al 2011;Stewart 1995).…”

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confidence: 99%

Predicting Model of Rockburst Based on Nondeterministic Theory

Wang

2017

Evolution, Monitoring and Predicting Models of Rockburst

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Predicting is the basis of prevention and controlling of rockburst hazards. According to the predicting results, the feedback design of rock engineering and controlling measures are taken in time. It is of great theoretical and practical value for the safety and efficiency of deep mine. So far, there are many methods to judge rockburst tendency, such as rock integrity coefficient method, strength criterion discrimination method, rock brittleness index method, elastic energy index method, dynamic failure time method, rockburst energy ratio method, impact energy index method, impact tendency criterion method, and resistivity method. Most of these methods and judging indexes, which is only considered the individual factors, were one-sidedness, limitations and complications. Therefore, the influencing factors of rockburst are comprehensively based on the non-deterministic theory for established a more accurate rockburst predicting model. Rockburst is a complex dynamic instability phenomenon (Hedley 1992), which can occur during underground excavation in areas with large in situ stress. As a result of the sudden release of accumulated strain energy, rocks can be come loose, crack, and even eject violently (Canadian Rockburst Research Program 1996). As a result, rockbursts were considered a major technical challenge in deep mining. Duo to the characteristic of sudden, disruptive, and complex, the accurate prediction of rockbursts was difficult and an urgent problem need to be solved (Blake et al. 2003).The phenomenon was discussed extensively by many scholars. Rockburst tendency is an important metric to quantify the risk and potential intensity of occurrences and grade the hazard of an affected mine. However, there are still no accurate prediction methods or effective control measures. In recent decades, many meaningful advances have been made by many scholars (Singh 1989;Dou et al. 2009;Marek 2009;Patynska et al. 2009;Marian 2011). Rockburst mechanism was better understand using some proposed some criteria, such as strength theory, stiffness theory and energy theory. These models could be explained the origin and mechanism of

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Knowledge-based and data-driven fuzzy modeling for rockburst prediction

Cited by 192 publications

References 23 publications

Dynamic Stability Analysis of Rockmass: A Review

Dynamic Stability Analysis of Rockmass: A Review

Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers

Predicting Model of Rockburst Based on Nondeterministic Theory

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