Slope movement hazards of the mining-dumps in the Dorog Basin, Hungary

2021 ◽  
Author(s):  
Gáspár Albert ◽  
Dávid Gerzsenyi ◽  
Réka Pogácsás
Keyword(s):  
Machine Learning ◽  
Waste Treatment ◽  
Slope Angle ◽  
Mining Area ◽  
Mining Waste ◽  
Cement Industry ◽  
Machine Learning Classification ◽  
Geomorphological Mapping ◽  
The Stability ◽  
Natural Slopes

<p>The Dorog Basin was a mining area in northern central Hungary for more than two centuries. Tunnel mining and quarrying of Eocene coal was the main industrial activity in the basin from the mid-19<sup>th</sup> century until the late 1990s. Extensive quarrying of the Cretaceous marl and Triassic limestone for the cement industry is also present in the area, along with pits of sand and fire clay and travertine quarries. Though the waste treatment is controlled by law and strict directives, the morphology and the material characteristics of the waste heaps are often enough to increase the chance of slope failures. As the mining waste heaps and tailings are often adjacent to residential and agricultural areas, they are considered as hazard sources. The combined use of remote sensing and machine learning methods can help to evaluate the stability of the waste heaps and select the sites where further hazard assessment is needed on the field.</p><p>The slopes of the area were sorted into six stability categories (scarps, transitional slopes, debris, low-lying accumulation areas, hilltops, stabile slopes) with random forest machine learning classification. The sample areas for the analysis were selected based on geomorphological mapping in the area and the re-evaluation of the recorded landslides from the landslide inventory. The classifier (Rstudio) analysed one lithological and two to six morphometric predictor variables. We tested several sets of different variables and selected the best performing set, which included the slope angle, profile curvature, TWI, mean upslope area, and the normalized height morphometric indices.</p><p>After the classification, the distribution of the stability categories was computed for three different areas: the mining waste heaps, the remediated quarries, and the natural slopes. The mining waste sites and the quarries were delineated using the national mining waste inventory, satellite images and topographic maps. Then a likelihood ratio analysis was done to calculate the relative frequencies of the stability categories in the different area types. It was expected that the stability category representing the slope debris at rest will be the most frequent in the waste heap areas. The statistical analysis reinforced this hypothesis by resulting a 54% larger likelihood compared to the natural slopes. It was also revealed that the most dangerous category, the scarps, are less likely on the waste heaps than on the natural slopes, which is a reassuring result. However, the transitional types (slopes that are still in movement) are more likely by 25% on the waste heaps. Even this slightly increased likelihood makes the local villages more prone to hazardous events, so an increased concern is also justified.</p><p>From the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme. D. G.: The study was supported by the ÚNKP-19-3 New National Excellence Program of the Ministry for Innovation and Technology, Hungary.</p>

scholarly journals Study on shaping slope stability of dump in eastern grassland open-pit mine

2020 ◽  
Vol 194 ◽  
pp. 04043
Author(s):  
Guo Xiaoli ◽  
Yan Jiancheng ◽  
Li Xueliang ◽  
Wen Xin ◽  
Li Xingli
Keyword(s):  
Slope Stability ◽  
Slope Angle ◽  
Mining Area ◽  
Open Pit Mine ◽  
Open Pit ◽  
Open Pit Mining ◽  
Dump Slope ◽  
The Stability ◽  
Step Type ◽  
Mine Dump

The dumps in the open-pit mining area in the eastern grassland are prone to landslides due to the fragile ecological environment, so it is inevitable to reshape the dump slopes. In order to explore a more scientific method for slope shaping of open-pit mine dump, slope stability analysis were used to compare effect of three types of slope-type (wave-shaped, slope-shaped and step-shaped slope shaping method)in outside dumping site of Baori Hiller open-pit mine. The results show that the slope stability is negatively correlated with the slope angle, and the stability of different shaping slopes is realized as wave-shaped slope (F=2.711)> Slope-shaped slope(F=2.513)>Step-shaped slope(F=1.047), in which the wave type and slope type are all within the safe range, but the step type slope is unstable; in consideration of cost, stability and erosion resistance, it is better to set the slope angle of the dump to 15°.The wave-shaped shaping method of the natural dumping of the excavation field outside the Baori Hiller open-pit mine has the best effect and is worth promoting.

Equilibrium Position Analysis for Offloading Operations With Turret-Moored FPSO

2018 ◽  
Author(s):  
Alex S. Huang ◽  
Felipe M. Moreno ◽  
Eduardo A. Tannuri ◽  
Joselito G. A. Câmara
Keyword(s):  
Machine Learning ◽  
Equilibrium Points ◽  
Environmental Data ◽  
Machine Learning Classification ◽  
Environmental Disasters ◽  
Deep Waters ◽  
Campos Basin ◽  
The Stability ◽  
Time Of Operation ◽  
The Given

With the expansion of oil exploration in deep waters, assessing the risks associated with offloading operations becomes essential in preventing accidents that may cause huge environmental disasters. In this paper, it will be studied the system composed of a turret-moored FPSO connected to a shuttle tanker which is assisted by a tug boat to maintain its position during an offloading operation. Using environmental data collected over a period of 6 years, from 2004 to 2009, from the Campos Basin in Brazil, it was calculated the equilibrium positions of the system considering its constraints (vessels have to keep a minimum alignment), and verified the stability of those equilibrium points for all the given conditions. The results obtained were simplified and grouped into operational rules using machine learning classification techniques. Based on this static analysis, it was calculated the FPSO headings probabilities during offloading operation and the expected down time of operation in the Campos Basin.

scholarly journals The Failure Mechanism and Stability of the End Slope of Inclined Composite Coal Seam

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Haoran Li ◽  
Han Du ◽  
Runcai Bai ◽  
Guangwei Liu ◽  
Mingyuan Zhao ◽  
...  
Keyword(s):  
Coal Seam ◽  
Slope Angle ◽  
Mining Area ◽  
Open Pit ◽  
The West ◽  
Evolution Law ◽  
Weak Layer ◽  
Side Slope ◽  
Western Side ◽  
The Stability

In view of the inclined occurrence of coal seam in Heishan open-pit coal mine, in the longitudinal exploitation process of the first mining area, the height of the slope at the west end is increasing, and the occurrence of weak interlayer in the slope is in production. The failure mechanism and stability of the end slope of the inclined composite coal seam, which is typical of the West end slope of Heishan open-pit mine, are studied by means of field investigation, theoretical analysis, limit equilibrium analysis, and numerical simulation. The factors affecting the stability of the western side slope and the potential landslide mode are analyzed. The residual thrust method and simplified Bishop method were used to study the stability of two potential landslide modes on the western slope, and the landslide mode, final slope angle, and slope morphology were determined. FLAC3D was used to simulate the western end of the slope, reveal its landslide mechanism, and clarify the evolution law of the slope rock mass displacement. The results show that the landslide mode at the west end of the first mining area is the combined sliding mode of “cutting and bedding.” With the decrease of longitudinal mining depth, the final slope angle functions from 40° to 37°. The stress and strain are concentrated at the weak layer of the coal floor of the western side slope 13-2 and at the foot of the slope. Meanwhile, the failure also occurs inside the slope. The internal cracks connect through the weak layer of the coal floor and slide along the weak layer of the coal floor 13-2. The displacement evolution law of the slope monitoring point shows that the deformation of the slope is mainly horizontal displacement, the maximum displacement deformation is at the foot of the slope, and the degree of displacement deformation of the slope surface is obviously greater than that of the inside of the slope.

scholarly journals EEG Channel Selection Using A Modified Grey Wolf Optimizer

2021 ◽  
Vol 5 (1) ◽  
pp. 17-24
Author(s):  
Hussien Rezk Hussien ◽  
El-Sayed M. El-Kenawy ◽  
Ali I. El-Desouky
Keyword(s):  
Machine Learning ◽  
Search Space ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Eeg Signals ◽  
Data Set ◽  
Machine Learning Classification ◽  
The Stability ◽  
Exponential Change ◽  
The Brain

Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel of communication between a computer and the brain. However, extracting features of random time-varying EEG signals and their classification is a major challenge that faces current BCI. This paper proposes a modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed. This allows (MGWO) to opt for optimal EEG channels as well as helping machine learning classification in its tasks when doing training to the classifier with the dataset. (MGWO), which imitates the grey wolves leadership and hunting manner nature and which consider metaheuristics swarm intelligence algorithms, is an integration with two modification to achieve the balance between exploration and exploitation the first modification applies exponential change for the number of iterations to increase search space accordingly exploitation, the second modification is the crossover operation that is used to increase the diversity of the population and enhance exploitation capability. Experimental results use four different EEG datasets BCI Competition IV- dataset 2a, BCI Competition IV- data set III, BCI Competition II data set III, and EEG Eye State from UCI Machine Learning Repository to evaluate the quality and effectiveness of the (MGWO). A cross-validation method is used to measure the stability of the (MGWO).

scholarly journals The risk of landslide occurrence in the waste dump belonging to the Ruget quarry and measures to combat it

2019 ◽  
Vol 290 ◽  
pp. 11010
Author(s):  
Constantin Rada ◽  
Florin Faur
Keyword(s):  
Stability Analysis ◽  
Slope Angle ◽  
Mining Waste ◽  
Waste Dump ◽  
Rock Support ◽  
Landslide Occurrence ◽  
Surrounding Landscape ◽  
The Stability ◽  
High Degree ◽  
Degree Of Instability

The mining waste dump has emerged as a result of lignite extraction from Ruget quarry. The waste dump should have 6 steps with a total height of 90 m (as designed), but, at present, only 5 steps have been constructed (in different stages of execution) due to cessation of the activity. Since the stability analysis showed a high degree of instability for some steps there are needed specific redesigning works (slope angle reduction, terraces, levelling etc.) as well as hydrotechnical works (central and side collector drains and collector tubes at the base of the dump). At present, no properly designed works to eliminate the risk of landslide (not even consolidation) and no ecological rehabilitation projects are in progress. Thus, in this paper there are proposed and designed stabilization works (by reshaping, construction of rock support walls at the base of the dump etc.), as well as recultivation with specific plants, which aim at eliminating the geotechnical risks in the area and the reintegration of the dump in the surrounding landscape.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

Sign in / Sign up

Export Citation Format

Share Document