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Most of the coal mining in China is underground, which will inevitably cause surface deformation and trigger a series of geological disasters. Therefore, it is essential to find a suitable method to forecast the ground sinking caused by underground mining. The most commonly used prediction model in China is the probability integral model (PIM). But when this model is used in the geological condition of mining under thick loose layers, the predicted edge of the sinking basin will converge faster than the actual measured sinking situation. A geometric model (GM) with a similar model shape as the PIM but with a larger boundary value was established in this paper to solve this problem. Then an improved cuckoo search algorithm (ICSA) was proposed in this paper to calculate the GM parameters. The stability and reliability of the ICSA were verified through a simulated working face. At last, the ICSA, in combination with the GM and the PIM, was used to fit 6 working faces with the geological mining condition of thick loose layers in the Huainan mining area. The results prove that GM can solve the above-mentioned PIM problem when it is used in geological mining conditions of thick loose layers. And it was obtained through comparative analysis that the GM and the PIM parameters can take the same value except for the main influence radius. [ABSTRACT FROM AUTHOR]

The probability integral method is the most commonly used mining subsidence prediction model, but it is only applicable to ordinary geological mining conditions. When the loose layer in the geological mining conditions where the mining face is located is too thick, many inaccurate phenomena will occur when the movement deformation value is predicted by the probability integral method. The most obvious one is the problem that the predicted value converges too fast compared with the measured value in the edge of the sinking basin. In 2012, Wang and Deng proposed a modified model of probability integral method for the marginal errors in the model of probability integral method and verified the feasibility of the method through examples. In this paper, the method is applied to the prediction of surface movement under thick and loose layers after modified. Through practical application, it is found that due to the angle between the working face and the horizontal direction, the average mining depth in the strike direction is different from the average mining depth in the inclined direction, and the main influence radius of the two main sections are often. Therefore, based on this problem, this paper divides the main influence radius into trend and tendency and adjusts the parameters in the model to find the rules of the parameters. The original method uses a dynamic scale factor to adjust the predicted shape of the graph by adjusting the sinking coefficient. This study is aimed to set the scale factor to 0.5 and fix the value of the sinking factor, and propose to adjust the integral range and then adjust the shape of the graph to make it more in line with the actual measurement situation. [ABSTRACT FROM AUTHOR]

The retardation factor (Rd) is one of the main important solute transport parameters. Its value can vary significantly depending on the method used for its determination. In this paper, the sodium Rd is experimentally determined using undisturbed sandy soil columns to compare four methods of Rd determination and assess the impact of the chosen method on the prediction of sodium movement. Column experiments in undisturbed soil samples and analytical analysis were performed. The results showed that the soil has dual-porosity and preferential pathways. The breakthrough curves were in accordance with the soil's physical characteristics. The Rd values ranged from 1.7 to 7.77 depending on the initial concentration and on the method used. These differences arise from the conceptual model of each Rd determination method. The experimental and analytical analysis indicated that the higher the Rd, the slower the movement. The methods that best reproduced the laboratory sodium movement were Ogata and Banks' (1961), Langmuir and Freundlich's isotherms. The prediction models presented smaller errors with the increase of the initial concentration. In these cases, the predicted concentrations can be overestimated up to 22.5% when using a not suitable method. These results suggest that the Rd determination method can strongly affect the prediction of the sodium movement. Because of that, it is of vital importance to evaluate each method and how they can be adequate for the soil under investigation when determining Rd. [ABSTRACT FROM AUTHOR]

Rock burst is one of main engineering geological problems greatly threatening the safety of construction. Prediction of rock burst is always an important issue concerning the safety of workers and equipments in tunnels. In this paper, a novel PNN-based rock burst prediction model is proposed to determine whether rock burst will happen in the underground rock projects and how much the intensity of rock burst is. The probabilistic neural network (PNN) is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the prediction of rock burst. Some main control factors, such as rocks' maximum tangential stress, rocks' uniaxial compressive strength, rocks' uniaxial tensile strength and elastic energy index of rock are chosen as the characteristic vector of PNN. PNN model is obtained through training data sets of rock burst samples which come from underground rock project in domestic and abroad. Other samples are tested with the model. The testing results agree with the practical records. At the same time, two realworld applications are used to verify the proposed method. The results of prediction are same as the results of existing methods, just same as what happened in the scene, which verifies the effectiveness and applicability of our proposed work. [ABSTRACT FROM AUTHOR]