Underground mining extraction causes the displacement and changes of stress fields in the surrounding rock mass. The determination of the changes is extremely important when the mining activity takes place in the proximity of post-flotation tailing ponds, which may affect the stability of the tailing dams. The deterministic modeling based on principles of continuum mechanics with the use of numerical methods, e.g. finite element method (FEM) should be used in all problems of predicting rock mass displacements and changes of stress field, particularly in cases of complex geology and complex mining methods. The accuracy of FEM solutions depends mainly on the quality of geomechanical parameters of the geological strata. The parameters, e.g. young modulus of elasticity, may require verification through a comparison with measured surface deformations using geodetic methods. This paper presents application of FEM in predicting effects of underground mining on the surface displacements in the area of the KGHM safety pillar of the tailing pond of the OUOW Żelazny Most. The area has been affected by room and pillar mining with roof bending in the years 2008-2016 and will be further exposed to room-and-pillar extraction with hydraulic filling in the years 2017–2019.
The paper presents differences between technical states and technical operation states of haul trucks in the technical operation process. The specification and analysis of operational parameters of technological vehicles used in surface mining is possible only due to more and more frequently used diagnostic – telemetric systems. While a detailed analysis of machines operation data can result in the more effective management of mining plant operations and the mining process itself. The determination of operational state indices and their individual components allows preventive actions to be commenced, resulting in improving the work organization of the entire mine machinery system. Moreover, the future technical state of machines operated in surface mining is strictly related to the current state and also depends on the events that occurred in the extraction system. A set of parameter values of individual state characteristics, which allow the haul trucks technical and operational state to be characterized, is a direct effect of a telemetric – diagnostic system operation.
An analysis of the impact of mining with caving on the surface shows that a type of rock mass strata seems to be one of the critical factors affecting the process. Correlating the values of mining-induced surface deformation with the rock mass structure and the state of its disturbance is of crucial importance. Therefore, if other mining conditions are left unaffected, then those factors exert the key influence on a course and distribution of subsidence and rock mass deformation. A proper description of rock mass type and properties also seems rational for a proper determination of prediction parameters, especially in the case of a multi-seam coal mining, and/or the exploitation carried out at considerable depths. A general outcome of the study discussed in this paper is the development of the methodology and model practices for determining the rock mass type and, as a result, for selecting the optimal values of parameters for predicting the values of surface subsidence in relation to particular geological and mining conditions. The study proves that the type of rock mass may be described by such factors as the influence of overburden strata, the influence of Carboniferous layers, the disturbance of rock mass and the depth of exploitation.
The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V 4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.
Geodesic measurements of mining area deformations indicate that their description fails to be regular, as opposed to what the predictions based on the relationships of the geometric-integral theory suggest. The Knothe theory, most commonly applied in that case, considers such parameters as the exploitation coefficient a and the angle of the main influences range tgβ, describing the geomechanical properties of the medium, as well as the mining conditions. The study shows that the values of the parameters a = 0.8 and tgβ = 2.0, most commonly adopted for the prediction of surface deformation, are not entirely adequate in describing each and every mining situation in the analysed rock mass. Therefore, the paper aims to propose methodology for determining the value of exploitation coefficient a, which allows to predict the values of surface subsidence caused by underground coal mining with roof caving, depending on geological and mining conditions. The characteristics of the analysed areas show that the following factors affect surface subsidence: thickness of overburden, type of overburden strata, type of Carboniferous strata, rock mass disturbance and depth of exploitation. These factors may allow to determine the exploitation coefficient a, used in the Knothe theory for surface deformation prediction.