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A causation mechanism for coal bursts during roadway development based on the major horizontal stress in coal: Very specific structural geology causing a localised loss of effective coal confinement and Newton’s second law
مکانیسم سببی برای انفجار زغال سنگ در حین توسعه جاده بر اساس فشار عمده افقی در زغال سنگ: زمین شناسی ساختاری بسیار خاص باعث از بین رفتن موضعی سلول زغال سنگ موثر و قانون دوم نیوتن-2020 In 2017, one of the international authorities on coal bursts, Mark Christopher, published a paper entitled
‘‘Coal bursts that occur during development: A rock mechanics enigma”, in which several relevant technical
issues were identified. This paper outlines what is considered to be a credible, first-principles,
mechanistic explanation for these three current development coal burst conundrums by reference to
early published coal testing work examining the significance of a lack of ‘‘constraint” to coal stability
and an understanding of how very specific structural geology and other geological features can logically
cause this to occur in situ, albeit on a statistically very rare basis. This basic model is examined by reference
to published information pertaining to the development coal-burst that occurred at the Austar Coal
Mine in New South Wales, Australia, in 2014 and from the Sunnyside District in Utah, the United States.
The ‘‘cause and effect” model for development of coal bursts presented also offers a meaningful explanation
for the statistical improbability for what are nonetheless potentially highly-destructive events, being
able to explain the statistical rarity being just as important to the credibility of the model as explaining
the local conditions associated with burst events. The model could also form the basis for a robust, riskbased
approach utilising a ‘‘hierarchy of controls”, to the operational management of the development
coal burst threat. Specifically, the use of pre-mining predictions for likely burst-prone and non-burstprone
areas, the use of the mine layout to avoid or at least minimise mining within burst-prone areas
if appropriate, and finally the development of an operational Trigger Action Response Plan (TARP) that
reduces the likelihood of inadvertent roadway development into a burst-prone area without suitable
safety controls already being in place. Keywords: Development coal burst | Wing-cracks | Austar Mine | Sunnyside Mine | Major horizontal stress in coal |
مقاله انگلیسی |
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Economic feasibility valuing of deep mineral resources based on risk analysis: Songtao manganese ore - China case study
ارزیابی امکان سنجی اقتصادی منابع معدنی عمیق بر اساس ریسک تجزیه و تحلیل: سنگ معدن منگنز Songtao - مطالعه موردی چین-2020 The exploitation of deep mineral resources is an inevitable choice under economic development and resource
shortage. Assessing the economic feasibility of deep mineral resource exploit projects is a prerequisite for
resource industry development. Mining industry have some problems influence its economic feasibility,
including long mining period, high infrastructure investment and lack flexibility, and have risks of geology
instability and economic reserve degrade. On the other hand, with the increase of the buried depth of mineral
resources, some problems have intensified the uncertainty of the profit of deep resource utilization project, such
as high stress, high lithology, high temperature environment, and increase of upgrading cost. Net Present Value
(NPV) and Internal Rate of Return (IRR) are traditional economic evaluation means which difficult to identify
and assess risks precisely. Decoupled Net Present Value (DNPV) provides an efficiency tool to separate the time
value and risk cost which is helpful to finds the real value of projects. A manganese mining project which is
located Guizhou province, China is analyzed, paper choices several mainly risks of influence expected revenue to
analysis project feasibility based on the DNPV technology, which includes the thickness of ore body, ore grade,
market price, operation cost and nature disaster. The cost of potential environmental risk (carbon emission cost)
also is analyzed. Paper constructs a risk management framework by risk identify, assess and classification, and
analyzes the corresponding measures to reduce risk costs. The mainly risk cost of study case from market price
shock and unexpected ore grade decline, which accounting for 80% of the total risk cost. In the process of deep
mineral resources exploit, effective cost control measures can reduce the risk cost to a certain extent, including
improving productivity, reducing unit cost of ore, improving mine sustainability and exploration accuracy. Green
mineral construction is a feasible direction of deep resource utilization. For improve the accuracy of economic
feasibility evaluation of deep mineral resources utilization, further improvement is needed in the selection and
construction of different risk assessment model. Keywords: Deep mining | Risk value assess | DNPV | Risk management | Songtao manganese |
مقاله انگلیسی |
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Hydrological modelling of karst catchment using lumped conceptual and data mining models
مدل سازی هیدرولوژیکی حوضه کارست با استفاده از مدلهای مفهومی و داده کاوی بهم چسبیده-2019 Hydrological modelling is a challenging and significant issue, especially in nonhomogeneous catchments in
terms of geology, and it is an essential part of water resources management. In this study, daily rainfall-runoff
modelling was carried out using the lumped conceptual model, the artificial neural network (ANN), the deepneural
network (DNN), and regression tree (RT) data mining models for the nonhomogeneous karst Ljubljanica
catchment and four of its sub-catchments in Slovenia with different geological characteristics. Model performance
was evaluated using several performance criteria and additional investigation of low and high flows was
carried out. The results of the study indicate that the Génie Rural à 4 paramètres Journalier (GR4J) lumped
conceptual model yielded better modelling performance compared to the data-driven models, namely ANN, DNN
and RT models. Moreover, the enhanced version of the GR4J model (i.e. GR6J) also yielded good performance in
terms of the recession part. The RT model yielded the worst performance regarding runoff forecasting among the
examined models in the case of all five investigated catchments. However, ANN and DNN data-driven models
were slightly more successful in modelling the hydrograph recession in the case of karst sub-catchments compared
to the GR4J lumped conceptual model structure. Inclusion of additional meteorological variables to ANN
and DNN does not significantly improve modelling results. Keywords: Hydrological model | Lumped conceptual model | Data mining | Karst | Nonhomogeneous catchment | Ljubljanica River |
مقاله انگلیسی |
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Chemical identification of metamorphic protoliths using machine learning methods
شناسایی شیمیایی پروتکل های دگرگونی با استفاده از روش های یادگیری ماشین-2019 The fundamental origins of metamorphic rocks as sedimentary or igneous are integral to the proper interpretation
of a terrane’s tectonic and geodynamic evolution. In some cases, the protolith class cannot be determined
from field relationships, texture, and/or compositional layering. In this study, we utilize machine learning
to predict a metamorphic protolith from its major element chemistry so that accurate interpretation of the
geology may proceed when the origin is uncertain or to improve confidence in field predictions. We survey
the efficacy of several machine learning techniques to predict the protolith class (igneous or sedimentary)
for whole rock geochemical analyses using 9 major oxides. The data are drawn from a global geochemical
database with >533 000 geochemical analyses. In addition to metamorphic samples, igneous and sedimentary
analyses are used to supplement the dataset based on their similar chemical distributions to their metamorphic
counterparts. We train the classifiers on most of the data, retaining ∼10% for post-training validation. We
find that the RUSBoost algorithm performs best overall, achieving a true-positive rate of >95% and >85%
for igneous- and sedimentary-derived samples, respectively. Even the traditionally-difficult-to-differentiate
metasedimentary and metaigneous rocks of granitic–granodioritic composition were consistently identified
with a >75% success rate (92% for granite; 85% for granodiorite; 88% for wacke; 76% for arkose). The
least correctly identified rock types were iron-rich shale (58%) and quartzolitic rocks (6%). These trained
classifiers are able to classify metamorphic protoliths better than common discrimination methods, allowing
for the appropriate interpretation of the chemical, physical, and tectonic contextual history of a rock. The
preferred classifier is available as a MATLAB function that can be applied to a spreadsheet of geochemical
analyses, returning a predicted class and estimated confidence score. We anticipate this classifier’s use as a
cheap tool to aid geoscientists in accurate protolith prediction and to increase the size of global geochemical
datasets where protolith information is ambiguous or not retained. Keywords: Data processing | Machine learning | Protolith discrimination | Igneous geochemistry | Sedimentary geochemistry |
مقاله انگلیسی |
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Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data
کاربرد یادگیری ماشین برای طبقه بندی خودکار مواد معدنی سنگین در ماسه رودخانه با استفاده از داده های SEM / EDS-2019 Heavy minerals are generally trace components of sand or sandstone. Fast and accurate heavy mineral classification
has become a necessity. Energy Dispersive X-ray Spectrometers (EDS) integrated with Scanning Electron
Microscopy (SEM) were used to obtain rapid heavy mineral elemental compositions. However, mineral identification
is challenging since there are wide ranges of spectral datasets for natural minerals. This study aimed to
find a reliable, machine learning classifier for identifying various heavy minerals based on EDS data. After
selecting 22 distinct heavy minerals from modern river sands, we obtained their elemental data by SEM/EDS.
The elemental data from a total of 3067 mineral grains were collected under various instrumental conditions. We
compared the classification performance of four classifiers (Decision Tree, Random Forest, Support Vector
Machine, Bayesian Network). Our results indicated that machine learning methods, especially Random Forest,
can be used as the most effective classifier for heavy mineral classification. Keywords: Heavy mineral | Machine learning | Energy dispersive X-ray spectrometers | Sand | Classification | Sedimentology | Geology |
مقاله انگلیسی |
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Classification of drilling stick slip severity using machine learning
طبقه بندی شدت لغزش چوب با استفاده از یادگیری ماشین-2019 Rate of penetration (ROP) is a key metric used to monitor the success of drilling a well. It is directly affected by
drilling vibrations since excessive vibrations result in a reduction of ROP. Vibration modeling and monitoring is
a complex process often requiring many simplifying assumptions that may not always generalize to different
BHAs, reservoirs, geology and formations. Therefore, it would be desirable to minimize drill string vibrations
using data driven models using readily available drilling data. The hypothesis tested is the classification of stick
slip severity due to drilling vibrations using open source machine learning algorithms. The stick slip index (SSI) –
measuring the severity of stick slip due to drilling vibrations – is classified as low or high using machine learning
classification algorithms such as logistic regression, support vector machines, random forests, gaussian mixture
models and discriminant analysis. Each algorithm was evaluated based on classification accuracy, F-1 score and
area under the receiver operating characteristic curve (AUC). The random forest algorithm outperforms other
algorithms with an average accuracy of 90% (F-1 score of 0.91 and AUC score of 0.89). The classification model
can then be used within a ROP optimization model (or framework) to determine optimal operation parameters
which do not result in stick-slip conditions while drilling addressing a serious limitation of previously published
ROP optimization papers. Keywords: Vibrations | Logistic regression | Gaussian mixture models | Linear discriminant analysis | Machine learning |
مقاله انگلیسی |
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Assessment of geologic programs in higher educational institutions of Chile
بررسی برنامه های زمین شناسی در موسسات آموزش عالی در شیلی-2018 In Chile, the subject of geology has historically been significant mostly due to the presence of world-class mineral deposits and highly profitable mines. Considering variable trends in mining, academic institutions with geology programs in Chile were analyzed to provide an evaluation of their current state and projected development across the country. Through the compilation of 5 years of data, a comparison was undertaken in relation to the age of the programs, their respective lifespans, geographic distributions, vacancies, annual entry fees, yearly tuitions, scores on admission tests, curricula, and human resources and infrastructure. The main results indicate the following: most of the new programs are located in or near the Metropolitan Region due to population trends rather than the locations of mines, the actual number of new students may double the total amount of vacancies, the student program fee tends to increase with time and varies between the programs with no apparent relationship to quality, there exists a strong variation in scores needed to enter into the geology programs, and currently there are more individuals studying geology than total graduates. When considering the unfavorable projections for mining in Chile, it is conclusive that this career will not yield the anticipated benefits for graduates unless new, more diverse professional opportunities develop in other sectors.
keywords: Geology program |Chile |Mining |Higher education |
مقاله انگلیسی |
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Performance prediction of hard rock Tunnel Boring Machines (TBMs) in difficult ground
پیش بینی عملکرد هاردراک دستگاه حفاری تونل (TBMS) در زمینی دشوار-2016 Performance prediction of TBMs is an essential part of project scheduling and cost estimation. This pro- cess involves a good understanding of the complexities in the site geology, machine specification, and site management. Various approaches have been used over the years to estimate TBM performance in a given ground condition, many of them were successful and within an acceptable range, while some missing the actual machine performance by a notable margin. Experience shows that the best approach for TBM per- formance prediction is to use various models to examine the range of estimated machine penetration and advance rates and choose a rate that best represents the working conditions that is closest to the setting of the model used for the estimation. This allows the engineers to avoid surprises and to identify the parameters that could dominate machine performance in each case. This paper reviews the existing mod- els for performance prediction of TBMs and some of the ongoing research on developing better models for improved accuracy of performance estimate and increasing TBM utilization.© 2016 Published by Elsevier Ltd.
Keywords: Performance prediction | Rate of penetration (ROP) | Advance rate (AR) | Utilization (U) | Tunnel Boring Machine (TBM) | Rock tunnelling |
مقاله انگلیسی |
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مهندسی در لبه ی پرتگاه: بهسازی جاده کوهستانی در فیلیپین
سال انتشار: 2002 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 21 توپولوژی و شرایط زمین برای مهندسی جاده های کوهستانی در رشته کوه های مرکزی لوزون در شمال فیلیپین (تصویر 1) یکی از حادترین و ناپایدارترین موارد در جهان است. رشته کوه های مرکزی، گستره ای از کوه هایی است که در اثر رانش سریع، شکاف خوردن دره ها، فرسایش شیب ها، و غالباً زمین لغزه های عظیم، ایجادشده و شکل گرفته اند (تصویر 2). بزرگراه هالزما، 180 کیلومتر طول دارد، این جاده ی پر پیچ و خمی است که از این رشته کوه ها عبور می کند و شریان راهبردی و اقتصادی حیاتی برای نواحی کوهستانی لوزون شمالی است که جزو مناطق عمدتاً زراعی محسوب می شوند. در سال 1990، این جاده در نتیجه ی زمین لرزه ی 7.8 ریشتری و طوفان و بارندگی های سیل آسای پس از آن آسیب و خرابی بسیار سنگینی را متحمل شد. به دلیل اهمیت راهبردی و اجتماعی-اقتصادی این بزرگراه، دولت فیلیپین به دنبال کمک های بین المللی برای بهسازی این خط ارتباطی پس از تخریب بود و مطالعات امکان سنجی و طراحی در اواخر سال 1996 آغاز شد. این مقاله برخی از بررسی های ژئوتکنیکی انجام گرفته برای این مطالعات را ارائه می دهد، انواع شرایط ناپایداری و وضعیت زمین که بر جاده تأثیر می گذارند و روش مورد استفاده برای به حداکثر رسانیدن شناخت زمین و فراهم نمودن انتخاب های مهندسی ژئوتکنیکی برای بهسازی جاده را بررسی می کند.
کلیدواژه ها: استحکامات خاکی | نقشه های زمین شناسی مهندسی | خطرات زمین شناختی | ژئومورفولوژی | بزرگراه |
مقاله ترجمه شده |