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By R. H. Zeng

Many subsidence researchers in the U. S. have developed new empirical function methods to predict subsidence, or attempted to validate some empirical functions developed by foreign researchers for use in the U. S. An attempt is made in this paper to develop a new empirical function to predict a surface subsidence basin due to longwall mining. In the U. S., the emphasis of empirical methods for the subsidence prediction over longwall panels has been in large part on determining the profile functions for the major cross-sections of a subsidence basin. In this approach, the surface monuments are usually laid out along the major cross-sections and thus, the subsidence profiles developed are restricted for subsidence prediction along the major cross-sections. However in practice many monuments or surface structures for which subsidence prediction for damage evaluation is required, are not laid out along the major cross-section of the basin. In such cases, it is difficult to get either the empirical profile function for the cross-sections where these structures are located or to predict their subsidence based on the profile function developed for the major cross-sections. Therefore, there exists a strong need to develop a subsidence basin function with a reasonable degree of accuracy, which can be determined or confirmed by the data of scattered monuments on a surface basin. The same subsidence basin function can also be used for prediction of subsidence for any arbitrary points over a longwall panel. In 1982, He (1) proposed the clastic theory, for which he developed the basic differential equations describing the movement and deformation of overburden due to underground mining, and performed the selection of best solution for the equations. He found that the Weibull dietribution is the optimal solution. The Weibull distribution has three parameters that vary to fit the subsidence profile so well that the profile function derived from it provides a high degree of accuracy in subeidence prediction. However the results

Jan 1, 1986

By Shu DM, Moxon P

Subsidence profiles over longwalls at West Cliff Colliery (WCC) are predicted by three methods, NCB Emperical Method, Salustowicz's Profile Functions and Superposition of Two Critical Profiles. A method of deriving maximum subsidence using local data is also presented. For the first four longwalls, the results are compared with those from the subsidence surveys conducted during and after the extraction of each longwall. Longwall V has not yet been extracted, so the results are for the predicted subsidence profile. Superposition of two critical profiles is carried out in an attempt to check the principle of superposition. In addition, strata movement above the extracted longwalls is discussed.

Jan 1, 1986

This paper reviews the current techniques employed in the prediction of subsidence and how they have been applied to partial extraction systems. The method using influence functions to describe the shape of the profile is investigated in detail and has been adopted to model the subsidence over workings in Huntly. It is compared with the profile methods developed in the United Kingdom and found to predict the subsidence profile more accurately than these methods.

Jan 1, 1991

By Mao Bai

In predicting the integrity of under-mined structures, surface horizontal displacements and curvatures are frequently of greater importance than the more homogeneous vertical displacements. Accurate evaluation of mining induced subsidence and horizontal displacement further requires that the predictive model adequately represents the subsurface geology, mining geometry and extraction sequencing. The following documents an extension of the Subsidence Prediction and System Identification method (SPASID) to the evaluation of horizontal strains. Comparisons are made between predictions from SPASID and two other empirical methods against measured data from a case study. The system identification method is illustrated to predict horizontal displacements and strains most consistently with the in situ data.

Jan 1, 1989

By W. L. Zhong

Probability function integration method is one of the influence function method. It is a widely accepted method in many mining districts in China and Poland mainly because its theory and formulae can well-represent the surface subsidence basins due to longwall mining of flat or near-flat seams. This paper introduces the basic theory of the probability function integration method and determines its 4 basic parameters by analyzing the measured subsidence profiles in eight longwall panels in Northern Appalachian Coal field. It also discusses the two methods for determining the 4 parameters. Finally the acquired parameters are used to predict the subsidence and deformation of the houses for damage assessment. Comparisons are made between the measured and the predicted subsidence. Probability Function Integration Method The probability function integration method for predicting surface movements and deformations is widely used in China and Poland. The governing equation of the method contains five parameters: subsidence factor, a, offset of inflection point, d, tangent of the major influence angle, tan fl, horizontal movement constant, b, and major dispersion angle e (e = 90. for flat seams). Its applicable range iis much larger including seams less than 30' from the horizontal, critical and noncritical mining and soft to hard overburden.

Jan 1, 1986

By P. Myllykoski, J. Nylander, A. S. Korhonen, J. Larkiola

The coefficient of friction and the deformation resistance have been determined from the measured rolling parameters by applying the Bland-Ford-Ellis rolling force model and the artificial neural network model (ANN). The dependence of friction on the parameters such as reduction, rolling speed, front and back tensions, contact pressure and deformation resistance have been studied by the ANN model. Measured data of over 10 000 coils have been used in the training stage. Various dependencies have been examined and the results show that the friction coefficient does not depend much on the rolling speed. The reduction and deformation resistance seem to have the strongest effect on the friction coefficient. A friction model based on neural computing has been included into the existing rolling force model.

Jan 1, 1994

By K. Aoki, Y. Mito, S. Shirasagi

The authors developed the TBM Excavation Control System (the TBM Navigator), in order to realize the advanced observational construction. During the excavation using this system, the rock strength value is immediately calculated from the TBM driving data as an index of rock properties, and the drill energy coefficient is also calculated from the drill logging data that is carried out using the probe hole ahead of the tunnel face. In this study, a new, geostatistical simulation-based method is proposed for the automatic prediction of the rock properties ahead of the tunnel face with good precision, using both the drill logging data and the TBM driving data. The applicability of this method is validated through the prediction at the actual tunnel excavation that passes through the faults. Les auteurs ont mis au point un Système de Contrôle de forage avec la machine à pleine section (Navigateur de la machine à pleine section), en vue de réaliser un système de surveillance plus efficace. Avec ce système, la résistance de la roche est immédiatement calculée, au cours du forage, à partir des données de conduite de la machine, comme indice des propriétés de la roche, et le coefficient d'énergie de la foreuse est également calculé à partir des données de log de forage collectées dans un trou de sonde foré dans le front de taille. Dans cette étude, on propose une nouvelle méthode basée sur la simulation géostatistique qui permet de prévoir d’une manière automatique, des propriétés de roches se trouvant derrière le front de taille avec une précision satisfaisante, à l’aide des données de log de forage et celles de conduite de la machine à pleine section. Cette méthode s’est avérée suffisamment précise au cours du forage d’un tunnel réel qui traversait une zone de failles. Die Autoren entwickelten das TBM Ausschachtungssteuerungssystem (den TBM Navigator), um noch besser überwachte Bauarbeiten zu ermöglichen. Unter Einsatz dieses Systems wird die Gesteinsfestigkeit während der Ausschachtungsarbeiten unmittelbar aus den TBM Antriebsdaten als ein Index für die Gesteinseigenschaften berechnet. Gleichzeitig wird auch der Bohrenergiekoeffizient aus den Bohrlogdaten berechnet, die an Hand der Probebohrung am Stirnende des Tunnels erhalten wurden. In der vorliegenden Studie wurde ein neuartiges, auf geostatistischen Methoden basierendes Verfahren für die automatische und angemessen genaue Vorhersage der Gesteinseigenschaften am Stirnende des Tunnels vorgeschlagen, für das sowohl die Bohrlogdaten als auch die TBM Antriebsdaten verwendet werden. Die Anwendbarkeit dieser Methode wird durch die Vorhersage bei einer tatsächlichen, durch Verwerfungen verlaufenden Tunnelausschachtung bewiesen.

Jan 1, 2003

By A. Karadogan, G. Tuncer, A. Kahriman, S. Gorgun

This paper presents the results of ground vibration measurements induced by blasting operations at five different sites located close to the residential areas. Within the scope of this study, ground vibration components were measured for all blast events over a long period of time in order to predict peak particle velocity and determine the slope of the attenuation curve for five sites. During the study, while the parameters of scaled distance were being recorded carefully, suitable monitors for all blast events of five blasting sites measured the ground vibration components. At the end of evaluation of the data pairs, an empirical relation, which gives an average line at 95% confidence level, and upper bound 95% prediction line with a good correlation coefficient was established and suggested for each site

Jan 1, 2003

By K. Oraee

Rate of penetration of Tunnel Boring Machines (TBM) has a significant role in the planning, measurement of productivity and performance of any tunneling project. In this paper, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) in prediction of TBM penetration rate is evaluated. For this purpose, a database including Rock Quality Designation (RQD), Uni-axial Compressive Strength (UCS) of the rock, the Distance between Planes of Weakness (DPW) in the rock mass, and empirical data regarding rate of penetration of TBM from several tunneling projects are collected. The Rate of Penetration is then estimated by using ANFIS. These results are then compared with measured TBM penetration rates (actual data). It is concluded that ANFIS can be applied successfully for such purpose and result in high accuracy for prediction for the rate of penetration of TBM. The method provided in this paper can assist the mining engineer to estimate the performance of tunneling accurately.

Jan 1, 2012

By R. G. Shen

The relation curve of strain and acceleration is presented in this paper. The steel pipe pile of 609.6 mm in external diameter driven by IDH-45 diesel motor hammer on the esturial shoal of Yangtze River. The ultimate bearing capacity of pile is analyzed by one dimensional wave equation and the effect of computational parameters of the equation is discussed.

Jan 1, 2010

By Vahid Tajdar

The behavior similarity between pile and cone penetration test, has guided the researchers to use the CPT results in prediction of piles bearing capacity. In this article, artificial neural networks have been used to study about relation between cone tip and sleeve friction strength obtained from cone penetration test and ultimate pile bearing capacity, obtained from static pile load test. Artificial neural networks "ANNS" is a form of artificial intelligence. ANN tries to dramatize the human nerves & brain system biology structure in self-structure. In this article, the results of relevant studies about the prediction of ultimate pile bearing capacity in especial area of soils are presented by use of back - propagation neural networks. The results of static pile load test up to failure on 63 piles with closed end and CPT test results in the place of construction these piles use in order to confirm and creation ANN model. Comparing predicted ultimate pile bearing capacity by utilizing ANN with the numbers which are predicted by 5 common traditional methods shows that using of artificial neural networks is a suitable method for predicted ultimate pile bearing capacity in identified area of texture of soil moreover it has a better operation in comparison with common traditional methods.

Jan 1, 2006

By Pratoomkhuan N. Lertsgoon

A large underground cavern with dimensions of 25m × 175m × 49m (W×L×H) was excavated in sandstone and siltstone to house 4 units of hydropower generator and was completed in 2000. In feasibility study stage, the property of the siltstone which governs the ceiling of the cavern was questionable due to slaking phenomenon. Parameters on rock properties for design were back calculated from actual ground response measured from a trial excavation. For safety of construction, comparison between prediction and actual ground responses during excavation had proceeded. The prediction was provided by means of PHASES and PHASE2 software and the actual rock response was detected by the provided monitoring system.

Jan 1, 2003

By D. Dunbar

Flooding of open pits now receives considerable attention from the mining industry as an option that may minimize environmental impacts, particularly when anoxia in pit lake deep waters can effectively eliminate oxidation of pit-wall sulphides. A state-of-the-art, coupled physical-geochemical pit lake model is used to verify that a cost-effective, environmentally responsible closure strategy for the open pit at the Kori Kollo gold mine, southeast of La Paz, Bolivia is to adopt a controlled, two-stage infilling of the open pit upon cessation of operations. By filling the pit initially with highly saline waters from evaporation/infiltration basins and naturally saline deep groundwater, followed by capping with fresh river water, a very stable and strongly stratified pit lake will be developed. In addition, the stratification will result in the isolation of metal-rich saline bottom waters from surface waters of good quality. Results of this model provide defensible predictions of the physical stability and evolution of water quality in the Kori Kollo pit lake over time. The model results indicate that anoxic conditions will develop below the fresh-saline water boundary within 1 to 2 years, followed by the rapid onset of sulphate reducing conditions in the bottom waters. Hydrogen sulphide produced by sulphate reduction will readily react with dissolved metals to form highly insoluble metal sulphides. The process results in the gradual removal of metals from the water column and the concomitant improvement of water quality over time. A notable benefit of this strategy is that it can be fully integrated with other mine site closure options as well as future environmental management strategies. The proposed plan allows for: 1) safe storage of saline evaporation basin waters; 2) the potential for passive treatment in anoxic bottom waters of ARD seepages from other mine site operations should they arise; and 3) a repository for future reactive wastes that could be effectively isolated in a physically and chemically stable environment.

Jan 1, 2001

By Quin Dennis

Process Optimization ?What is it really? It is NOT ?a larger fan to increase draft through a process ?a larger pump on a wet circuit ?a wider conveyor belt for solid materials ?a larger feed elevator into or out of a process These relieve individual constraints ??bottlenecks??and allow operation to the next constraint in the process ?It is also NOT about automating the operator ?which captures consistency but is not continuous optimization

Nov 1, 2011

By D T. Feinstein, M B. Gotkowitz

In Wisconsin, state laws and regulations governing metallic mine permitting stipulate that protection of groundwater quality be assured indefinitely through all phases of the mining project, including development, active mining, reclamation, and long-term closure. By rule, the Wisconsin Department of Natural Resources (WDNR) requires that permit applicants use a numerical model to assist in the assessment of compliance with this requirement for all proposed mine-waste facilities. As a part of the compliance assessment, model calculations are used to predict groundwater solute concentrations through time at designated boundaries for comparison to current groundwater quality standards. In practice, this modelling requirement has involved the application of numerical solute-transport models. Our review of a solute-transport model submitted by the developers of a proposed mine near Crandon, Wisconsin, provides us with an opportunity to assess the usefulness of transport modelling as a permitting requirement. Recent evaluations of solute-transport models by other workers suggest limitations in predictive accuracy. Our review of the model submitted for this project also identified important limitations due primarily to uncertainty in calibration of model parameters at the undeveloped site. The utility of any model to predict project compliance or non-compliance with numerical standards is limited by the range of uncertainty of key model parameters, such as dispersivity and porosity. Uncertainty in the future behaviour of the solute source(s) also reduces the predictive power. Moreover, the site characterisation provided by the model may have limited power to describe the site condition post-development. Although these inherent shortcomings accompany the use of solute transport models at undeveloped sites, the requirement for model development and the ensuing technical review has substantially assisted in our evaluation of site conditions and project design. Through this process we have identified elements of the waste facility design that are crucial to the environmental viability of the project, resulting in modifications to original project proposals. While results from this model review will be used to evaluate compliance, additional benefits come from the identification of critical design features that enhance groundwater protection.

Jan 1, 2003

By Y Xie, D Willingham

Predictive maintenance (also known as ‘condition/health monitoring’) refers to the intelligent monitoring of equipment to avoid future failures. In contrast to conventional preventive maintenance, the maintenance schedule is not determined by a prescribed timeline; instead, it is determined by analytic algorithms, such as machine learning, using data collected from equipment sensors and/or via simulation.Predictive maintenance offers the following benefits for operators and equipment manufacturers:- reduces equipment downtime by identifying issues before failure, thereby enabling convenient scheduling of equipment service and extending equipment lifetime- determines the root cause of the failure, enabling appropriate service to be performed without utilising resources to determine a diagnosis- avoids the unnecessary costs of maintenance and unexpected failures- improves safety and product quality.Algorithms and data are critical to predictive maintenance success. Sensor data is first preprocessed using advanced statistical and signal processing techniques; it is then combined with predictive modelling techniques, such as machine learning, to estimate equipment health.Once tested, predictive maintenance algorithms may be used in an operational setting within an IT environment, such as a server or cloud. Alternatively, algorithms may be implemented in an embedded system directly on the equipment, allowing for faster response times and significantly reducing the amount of data sent over the network.CITATION:Willingham, D and Xie, Y, 2017. Predictive maintenance modelling for bearing-based equipment, in Proceedings Iron Ore 2017, pp 627–630 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 24, 2017

By Greg Partington, Darryl Thorburn, Charlene Wildman, Elisa Puccioni, Connie Payne, Campbell McKenzie

"Mineral prospectivity modelling of polymetallic nodule deposits has been completed over the Cook Islands Exclusive Economic Zone (CIEEZ) using the GIS based Weights of Evidence modelling technique. Polymetallic nodules, also known as manganese nodules, have been explored as a possible resource for a number of metals including cobalt since the early 1970s. Recent data on the nodule composition and abundance in the CIEEZ has led to a renewed interest in the prospectivity for manganese nodules in the region. The predictive modelling has used the most recently accepted mineral model for polymetallic deep-sea nodule deposition to identify factors that are most likely to map the presence of this type of mineralisation in the CIEEZ.Manganese nodules form on the deep seafloor as concretions formed by concentric layering of iron and manganese hydroxides around a core and they grow at a very slow rate, on the order of millimeters per million years. The available data for the CIEEZ indicates a greater abundance in the area of nodules formed by hydrogenetic processes, which are the result of slow precipitation of metallic components from seawater. The environment in which the nodules are most abundant is at depths between 4,000 m and 6,000 m in areas of low sedimentation rate. Hydrogenous nodules commonly grow on siliceous and pelagic clay sediment, they are less common on other types of sediment."

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