Disaster prediction of coal mine gas based on data mining

The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.

Research on prediction for coal and gas outburst based on Matlab neural network toolbox and its application

In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.

Support principles of NPR bolt/cable and control techniques of large-deformation disasters

Deep mining has been paid much more attention because of the depletion of shallow mining resources.Traditional bolts could be invalid to accommodate large displacement and deformation in geomaterials.Consequently, alternative support and reinforcement bolts need to be studied and their constitutive models also need to be developed to help understanding for the complex stress-strain responses of rock masses under loadings. The effect of Negative Poisson's Ratio(NPR) that is attributed to the swelling phenomenon along the lateral direction may appear in metal materials under tensional loadings. Thence NPR materials often have an advantage over NPR ones in mechanical behavior such as impact resistance, antishearing, and energy absorbed. From the characteristics of NPR materials, a series of bolt and cable supports with the effect of NPR and constant-resistance have been recently developed. We here firstly introduce the structural features of NPR support. Then the constitutive model of NPR support is presented and its corresponding equation of energy equilibrium. Its basic principle interacted on rock masses is further discussed. Finally, NPR cables are employed to support the slope of an open-pit mine. The applications show that NPR cables can ease failure within the slope and play an important role in predicting and providing early warning of slope failure, together with a monitoring system of slope stability.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.

A brief introduction to disaster rock mass mechanics

Rock mass mechanics can be classified into engineering rock mass mechanics and disaster rock mass mechanics based on science and application.Their conception,object,scientific essence and application were elaborated.The connotation,studying method and theoretical framework of disaster rock mass mechanics were described.Disaster rock mass mechanics is a strongly nonlinear discipline which is a strong tool to study natural and artificially-induced disasters.The rock mass system where disasters happen exhibits extremely spatial-temporal nonlinearity in the critically unstable state.Hence,the potentially effective prediction and forecasting of disasters depends on statistical analysis of highly probable events.The direction of efforts for predicting and forecasting disasters could be to find the quantitative or semi-quantitative relationship between physical and biological information and instability of rock mass system.

Debris flow susceptibility mapping in mountainous area based on multi-source data fusion and CNN model-taking Nujiang Prefecture,China as an example

Efforts to evaluate the susceptibility of debris flows in large areas,especially in mountainous regions,are often hampered by the alpine and canyon terrain.This paper proposes a convolution neural network(CNN)model named dense residual shuffle net(DRSNet).It is successfully applied to Nujiang Prefecture in Yunnan Province of China,a typical alpine area with frequent debris flows.DRSNet uses digital elevation model,remote sensing,lithology,soil type and precipitation data as input.First,dense connection and residual structure were used to extract the shallow features of various data.Next,channel shuffle,fuse block and fully connection were applied to strengthen the correlation between different shallow features and give inner danger scores.Finally,precipitation as the activation factor was introduced giving the valleys susceptibility.To verify the feasibility of DRSNet,comparative tests were conducted on 7 CNN models and 3 other machine learning(ML)methods.Experimental results show that DRSNet can achieve 78.6%accuracy in debris flow valley classification,which is at least 7.4%higher than common CNN models and 15.2%higher than other ML methods.This article provides new ideas for debris flow susceptibility evaluation.