scholarly journals A Bayesian network approach for geotechnical risk assessment in underground mines

2021 ◽  
Vol 121 (6) ◽  
Author(s):  
R. Mishra ◽  
L. Uotinen ◽  
M. Rinne
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Expert Knowledge ◽  
Underground Mining ◽  
Hybrid Approach ◽  
Underground Mines ◽  
Parameter Learning ◽  
Good Tool ◽  
Roof Fall ◽  
Geotechnical Risk

SYNOPSIS Underground mining gives rise to geotechnical hazards. A formal geotechnical risk assessment can help to forecast and mitigate these hazards. Frequentist probability methods can be used when the hazard does not have many variables and a lot of data is available. However, often there is not enough data for probability distributions, such as in the case of new projects. The risk assessment is often subjective and qualitative, based on expert judgement. The purpose of this research is to present the use of Bayesian networks (BNs) as an alternative to existing risk assessment methods in underground mines by combining expert knowledge with data as it becomes available. Roof fall frequency forecasting using parameter learning is demonstrated with 1141 sets of roof fall data across 12 coal mines in the USA. The prediction is nearly identical for individual mines, but when multiple mines are evaluated it is difficult to find a single best fit distribution for annual roof fall frequency. The BN approach with TNormal distribution was twice as likely to fit the observed data compared to the Poisson distribution assumed in the past. A hybrid approach using BN combining multiple probability distribution curves from historical data to predict annual roof fall is proposed. The BN models can account for variability for multiple parameters without increasing the complexity of the calculation. BNs can work with varying amounts of data, which makes them a good tool for real-time risk assessment in mines. Keywords: Bayesian network; expert opinion models; geotechnical risk; incident forecasting; parameter learning; roof fall risk.

scholarly journals Geotechnical Risk Classification for Underground Mines / Klasyfikacja Poziomu Zagrożenia Geotechnicznego W Kopalniach Podziemnych

2015 ◽  
Vol 60 (1) ◽  
pp. 51-61
Author(s):  
Ritesh Kumar Mishra ◽  
Mikael Rinne
Keyword(s):  
Risk Assessment ◽  
Rock Mass ◽  
Underground Mining ◽  
Mining Operation ◽  
Mining Activity ◽  
Mine Planning ◽  
Underground Mines ◽  
Mining Operations ◽  
Prediction And Prevention ◽  
Geotechnical Risk

Abstract Underground mining activities are prone to major hazards largely owing to geotechnical reasons. Mining combined with the confined working space and uncertain geotechnical data leads to hazards having the potential of catastrophic consequences. These incidents have the potential of causing multiple fatalities and large financial damages. Use of formal risk assessment in the past has demonstrated an important role in the prediction and prevention of accidents in risk prone industries such as petroleum, nuclear and aviation. This paper proposes a classification system for underground mining operations based on their geotechnical risk levels. The classification is done based on the type of mining method employed and the rock mass in which it is carried out. Mining methods have been classified in groups which offer similar geotechnical risk. The rock mass classification has been proposed based on bulk rock mass properties which are collected as part of the routine mine planning. This classification has been subdivided for various stages of mine planning to suit the extent of available data. Alpha-numeric coding has been proposed to identify a mining operation based on the competency of rock and risk of geotechnical failures. This alpha numeric coding has been further extended to identify mining activity under ‘Geotechnical Hazard Potential (GHP)’. GHP has been proposed to be used as a preliminary tool of risk assessment and risk ranking for a mining activity. The aim of such classification is to be used as a guideline for the justification of a formal geotechnical risk assessment.

scholarly journals Risk Assessment at Workplace in Underground Lead and Zinc Mine with Application of Fuzzy Topsis Method

2020 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Risk Assessment ◽  
Underground Mining ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Underground Mines ◽  
Topsis Method ◽  
Lead And Zinc ◽  
Zinc Mine ◽  
Serious Injuries ◽  
Lead And Zinc Mine

Underground mining is considered one of the most dangerous industries, because serious injuries or accidents often occur at the workplace. In recent years, fuzzy multiple criteria decision-making has found increasing application in job risk assessment, taking into account a number of influential parameters. This paper uses fuzzy TOPSIS method for workplace risk assessment in an underground lead and zinc mine, where the results are be compared with the number of injuries and accidents that have occurred in individual workplaces to assess its accuracy. Accurate workplace risk assessment in underground mines is very important so that appropriate safety measures can be taken in a timely manner to avoid injuries and deaths at work.

scholarly journals Geological CO2 quantified by high-temporal resolution stable isotope monitoring in a salt mine

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander H. Frank ◽  
Robert van Geldern ◽  
Anssi Myrttinen ◽  
Martin Zimmer ◽  
Johannes A. C. Barth ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Underground Mining ◽  
Stable Carbon Isotope ◽  
Underground Mines ◽  
Background Concentration ◽  
High Temporal Resolution ◽  
Salt Mine ◽  
Gas Migration ◽  
Mining Operations ◽  
Anthropogenic Contributions

AbstractThe relevance of CO2 emissions from geological sources to the atmospheric carbon budget is becoming increasingly recognized. Although geogenic gas migration along faults and in volcanic zones is generally well studied, short-term dynamics of diffusive geogenic CO2 emissions are mostly unknown. While geogenic CO2 is considered a challenging threat for underground mining operations, mines provide an extraordinary opportunity to observe geogenic degassing and dynamics close to its source. Stable carbon isotope monitoring of CO2 allows partitioning geogenic from anthropogenic contributions. High temporal-resolution enables the recognition of temporal and interdependent dynamics, easily missed by discrete sampling. Here, data is presented from an active underground salt mine in central Germany, collected on-site utilizing a field-deployed laser isotope spectrometer. Throughout the 34-day measurement period, total CO2 concentrations varied between 805 ppmV (5th percentile) and 1370 ppmV (95th percentile). With a 400-ppm atmospheric background concentration, an isotope mixing model allows the separation of geogenic (16–27%) from highly dynamic anthropogenic combustion-related contributions (21–54%). The geogenic fraction is inversely correlated to established CO2 concentrations that were driven by anthropogenic CO2 emissions within the mine. The described approach is applicable to other environments, including different types of underground mines, natural caves, and soils.

Agreement and Reliability Analysis of Machine Learning Scaling in the Assessment of Acute Proximal Weakness by Experts and Non-Experts: Clinical Feasibility Study (Preprint)

2021 ◽  
Author(s):  
Eunjeong Park ◽  
Kijeong Lee ◽  
Taehwa Han ◽  
Hyo Suk Nam
Keyword(s):  
Machine Learning ◽  
Reliability Analysis ◽  
Gold Standard ◽  
Assessment Tool ◽  
Expert Knowledge ◽  
Hybrid Approach ◽  
Assessment Model ◽  
Lower Limbs ◽  
Neurological Deficits ◽  
Percent Agreement

BACKGROUND Assessing the symptoms of proximal weakness caused by neurological deficits requires expert knowledge and experienced neurologists. Recent advances in artificial intelligence and the Internet of Things have resulted in the development of automated systems that emulate physicians’ assessments. OBJECTIVE This study provides an agreement and reliability analysis of using an automated scoring system to evaluate proximal weakness by experts and non-experts. METHODS We collected 144 observations from acute stroke patients in a neurological intensive care unit to measure the symptom of proximal weakness of upper and lower limbs. A neurologist performed a gold standard assessment and two medical students performed identical tests as non-expert assessments for manual and machine learning-based scaling of Medical Research Council (MRC) proximal scores. The system collects signals from sensors attached on patients’ limbs and trains a machine learning assessment model using the hybrid approach of data-level and algorithm-level methods for the ordinal and imbalanced classification in multiple classes. For the agreement analysis, we investigated the percent agreement of MRC proximal scores and Bland-Altman plots of kinematic features between the expert- and non-expert scaling. In the reliability analysis, we analysed the intra-class correlation coefficients (ICCs) of kinematic features and Krippendorff’s alpha of the three observers’ scaling. RESULTS The mean percent agreement between the gold standard and the non-expert scaling was 0.542 for manual scaling and 0.708 for IoT-assisted machine learning scaling, with 30.63% enhancement. The ICCs of kinematic features measured using sensors ranged from 0.742 to 0.850, whereas the Krippendorff’s alpha of manual scaling for the three observers was 0.275. The Krippendorff’s alpha of machine learning scaling increased to 0.445, with 61.82% improvement. CONCLUSIONS Automated scaling using sensors and machine learning provided higher inter-rater agreement and reliability in assessing acute proximal weakness. The enhanced assessment supported by the proposed system can be utilized as a reliable assessment tool for non-experts in various emergent environments.

Sign in / Sign up

Export Citation Format

Share Document