Risk assessment of failure of rock bolts in underground coal mines using support vector machines

Jiang, Peng; Craig, Peter; Crosky, A.; Maghrebi, Mojtaba; Canbulat, Ismet; Saydam, Serkan

doi:10.1002/asmb.2273

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…The obtained error results in all phases are presented in Table 3. In Figure 10, the accuracy of the TRANCE model in the training and validation phase is quite good, with the coefficient of determination values of 0.9998 and 0.998, and the error values (MSE) are 180.70 and 282.82, respectively, which is higher than the SVM-based risk assessment of rock bolts failure in coal mines [34].…”

Section: Performance Of the Trance Modelmentioning

confidence: 95%

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

Gabhane

Rao

2023

Toxics

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Risk analysis and prediction is a primary monitoring strategy to identify abnormal events occurring in chemical processes. The accidental release of toxic gases may result in severe problems for people and the environment. Risk analysis of hazardous chemicals using consequence modeling is essential to improve the process reliability and safety of the refineries. In petroleum refineries: toluene, hydrogen, isooctane, kerosene, methanol, and naphtha are key process plants with toxic and flammable chemicals. The major process plants considered for risk assessment in the refinery are the gasoline hydrotreatment unit, crude distillation, aromatic recovery, continuous catalytic reformer, methyl–tert–butyl–ether, and kerosene merox units. Additionally, we propose a threat and risk analysis neural network for the chemical explosion (TRANCE) model for refinery incident scenarios. Significantly, 160 attributes were collected for the modeling on the basis of the significance of failure and hazardous chemical leaks in the refinery. Hazard analysis shows that the leakages of hydrogen and gasoline at the gasoline hydrotreatment unit, kerosene at the kerosene merox plant, and crude oil at crude-distillation units were areas of profound concern. The developed TRANCE model predicted the chemical explosion distance with an R2 accuracy value of 0.9994 and MSE of 679.5343.

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Section: Performance Of the Trance Modelmentioning

confidence: 95%

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

Gabhane

Rao

2023

Toxics

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“…It understood the coal mine limits as well as other aspects of the operating zone. Jiang et al (2018) have used a hazard adjustment approach based on a machine-learning algorithm to anticipate rock bolt incompetence in underground coal mines.…”

Section: Introductionmentioning

confidence: 99%

t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines

et al. 2021

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A deep learning network is introduced to predict concentrations of gases in the underground coal mine enclosed region using various IoT-enabled gas sensors installed in a metallic gas chamber. The air is sucked automatically at specific intervals from the sealed-off site utilizing a solenoid valve, suction pump, and programmed microprocessor. The gas sensors monitor the gas content in the underground coal mine and communicate gas concentration to the surface server room through a wireless network and cloud storage media. The t-SNE_VAE_bi-LSTM model is proposed in this study as a prediction model that combines the t-SNE, VAE, and bi-LSTM networks. The proposed model's t-SNE method aims to minimize the dimensionality of the recorded gas concentration; the presented model's VAE layer intends to retrieve the inner characteristics of low-dimension gas concentration. Finally, the given model's Bi-LSTM layer tries to forecast the concentrations of CH4, CO2, CO, O2, and H2 gases. The proposed model's prediction accuracy is compared with the existing two models, namely auto-regressive integrated average moving (ARIMA) and chaos time series (CHAOS). The experiment findings demonstrate that the t-SNE_VAE_bi-LSTM model forecasted mean square error (MSE) is more accurate, and it has lesser MSE value of 0.029 and 0.069 for CH4; 0.037 and 0.019 for CO2; 0.092 and 0.92 for CO; 1.881 and 1.892 for O2; and 1.235 and 1.200 for H2 than the ARIMA and CHAOS models, respectively.

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“…Whilst it is not the intention of this paper to review all such efforts, we found it pertinent to provide some examples. There are considerable evidences of SVM's varied application such as predicting medication adherence in heart failure patients [10], detection of epileptic electroencephalogram [11], financial distress and risk prediction [12,13], construction safety-risk assessment [14,15] , revenue forecasting [16], forecasting wind speed for wind farms [17], groundwater simulation [18] or apple disease detection [19]. The above examples not only illustrate the popularity of SVM across various fields, but also its competence at providing comparatively accurate predictions and classifications.…”

Section: Introductionmentioning

confidence: 99%

A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England

Hassani

Silva

Combe

et al. 2019

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Disease emergence, in the last decades, has had increasingly disproportionate impacts on aquatic freshwater biodiversity. Here, we developed a new model based on Support Vector Machines (SVM) for predicting the risk of freshwater fish disease emergence in England. Following a rigorous training process and simulations, the proposed SVM model was validated and reported high accuracy rates for predicting the risk of freshwater fish disease emergence in England. Our findings suggest that the disease monitoring strategy employed in England could be successful at preventing disease emergence in certain parts of England, as areas in which there were high fish introductions were not correlated with high disease emergence (which was to be expected from the literature). We further tested our model’s predictions with actual disease emergence data using Chi-Square tests and test of Mutual Information. The results identified areas that require further attention and resource allocation to curb future freshwater disease emergence successfully.

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Risk assessment of failure of rock bolts in underground coal mines using support vector machines

Cited by 9 publications

References 21 publications

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

t-SNE and variational auto-encoder with a bi-LSTM neural network-based model for prediction of gas concentration in a sealed-off area of underground coal mines

A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England

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