Zhou Keping

Virtual reality simulation system for underground mining process

It is a kind of innovation in management by using virtual reality technology to construct a virtual mine and support mine planning and mining design. The process of virtual mine scene modeling and underground mining process simulation was introduced. And based on OSG and VS2005 development platform, the virtual reality simulation for underground mining process of Kafang polymetallic ore deposits was established. The system was divided into four function modules which includes data management module, scene modeling module, virtual scene module and stereoscopic display module. At last, the mine surface, development and transportation system, orebody occurrence situation and underground mining process were successfully represented in 3D model. Moreover, the system can allow users to roam in the scene freely and interact with the mining equipments real-timely.

Reliability analysis of inclined loaded piles using a high-order response surface

The response surface method is a convenient tool to evaluate the reliability of pile foundation. This paper proposes a new response surface method, which make use of high-order response surface function and the geometric properties of JC method to determine the iterative centre criterion. The application procedure of the new surface method for inclined load pile is developed. Based on this, the application procedure is then applied to carry out reliability of inclined load pile. This paper investigates two models of failure: pile head horizontal displacement and maximum bending moment of the pile, and comparisons are made with quadratic response surface and Monte Carlo simulation. The results of the reliability analysis show that the improved response surface method can considerably reduce the iterations, improve the computational accuracy and the stability of numerical experimentations.

The Optimization Research on Large-Diameter Longhole Blasting Parameters of Underground Mine Based on Artificial Neural Network

This paper combines with Kafang’s engineering practice of Xinshan mining area, makes crater tests, and then determines the blasting parameters under experimental conditions. Train the key stakeholders blasting parameters both at home and abroad based on the BP artificial neural network (ANN) model. On the basis that the best charge depth is 1.09m which under the experimental conditions of blasting crater test. Conduct optimizing calculation of blasting parameters by using EasyNN-plus software. Through a comprehensive analysis of optimization ways and parameter error, recommend blasting parameters under experimental conditions: charge depth L=1.09m, the best crater radius Rj=0.77-0.79m, the best crater volume Vj=0.5-0.6m3, and explosive consumption 1.0-1.1kg/t.

Natural safety prediction of non-coal mine accident based on BP neural network

Mine disaster system has the typical non-linear features. The traditional, previously function-setting evaluation methods and prediction methods have appeared their limitations. The BP neural network, with the nonlinear dynamic characteristics, eliminated the drift value brought about by man-made factors during the weight determination using the previous method. It is a promising natural safe-forecasting method. First, obtain the network weight parameters meets the convergence conditions through studying the known samples. Then using them as foundation to calculate mine forecast indicator system parameters, made safety prediction of forecast mines. The error between BP calculated predictive value and the actual value range from 2.22 to 5.54 percent, which showed that the training model is more accurate and reliable to forecast. The study contents have important guiding significance to mine safety management and scientific decision-making.