Identification model of water inrush source based on statistical analysis in Fengyu minefield, Northwest China

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Chenguang Song ◽  
Leihua Yao ◽  
Jun Gao ◽  
Chengya Hua ◽  
Qihang Ni
Keyword(s):  
Statistical Analysis ◽  
Water Inrush ◽  
Northwest China ◽  
Identification Model

scholarly journals Correction to: A failure case study of tunnel water inrush source identified by statistical analysis model

2021 ◽  
Vol 14 (20) ◽  
Author(s):  
Li-Gang Yuan ◽  
Xiao-Li Li ◽  
Xin Li ◽  
Yi-Lin Yu ◽  
Li-Guang Chen ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Water Inrush ◽  
Analysis Model

Rapid identification model of mine water inrush sources based on extreme learning machine

2017 ◽  
Vol 13 (4) ◽  
pp. 286 ◽  
Author(s):  
Ya Wang ◽  
Mengran Zhou ◽  
Pengcheng Yan ◽  
Feng Hu ◽  
Wenhao Lai ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Mine Water ◽  
Water Inrush ◽  
Rapid Identification ◽  
Learning Machine ◽  
Identification Model

Statistical analysis of groundwater chemistry of the Tarim River lower reaches, Northwest China

2011 ◽  
Vol 65 (6) ◽  
pp. 1807-1820 ◽  
Author(s):  
Jianhua Xu ◽  
Yaning Chen ◽  
Weihong Li ◽  
Lijun Zhang ◽  
Yulian Hong ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Northwest China ◽  
Groundwater Chemistry ◽  
Tarim River ◽  
The Tarim River

scholarly journals Dynamic Evaluation Method of the EW-AHP Attribute Identification Model for the Tunnel Gushing Water Disaster under Interval Conditions and Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bo Wu ◽  
Huihao Chen ◽  
Wei Huang ◽  
Guowang Meng
Keyword(s):  
Evaluation Method ◽  
Water Inrush ◽  
Risk Identification ◽  
Theory And Practice ◽  
Assessment System ◽  
Evaluation Index ◽  
Attribute Identification ◽  
Tunnel Section ◽  
Water Disaster ◽  
Identification Model

The gushing water disaster in tunnels is a kind of harmful and risky engineering disaster. It has become a key problem to evaluate the risk of tunnel gushing water accurately and objectively. A case study of a typical highway tunnel is performed for theory and practice analysis. For this reason, the risk identification is carried out on the assessed objects, and 10 evaluation indexes are determined. In turn, the risk evaluation index system and classification standard are established. Furthermore, the entropy weight method and the analytic hierarchy process are combined to assign the weight to each evaluation index. Therefore, a dynamic risk assessment system, including the pre-evaluation model and the postevaluation model, is constructed with the attribute identification model. As a result, the tunnel section with a high risk of water inrush is accurately assessed, which is consistent with the construction situation on site. Moreover, it is verified that the assessment results are reliable, which can provide a reference for the similar projects.

scholarly journals Statistical analysis of distribution patterns of coal seams in fold zones in Northwest China

2018 ◽  
Vol 28 (5) ◽  
pp. 819-828 ◽  
Author(s):  
Anye Cao ◽  
Guangcheng Jing ◽  
Linming Dou ◽  
Yun Wu ◽  
Chengguo Zhang
Keyword(s):  
Statistical Analysis ◽  
Distribution Patterns ◽  
Northwest China ◽  
Coal Seams

scholarly journals A Normal Cloud Model-Based Method for Risk Assessment of Water Inrush and Its Application in a Super-Long Tunnel Constructed by a Tunnel Boring Machine in the Arid Area of Northwest China

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 644 ◽  
Author(s):  
Xin Wang ◽  
Kebin Shi ◽  
Quan Shi ◽  
Hanwei Dong ◽  
Ming Chen
Keyword(s):  
Risk Assessment ◽  
Cloud Model ◽  
Water Inrush ◽  
Ideal Point ◽  
Tunnel Boring Machine ◽  
Northwest China ◽  
Arid Area ◽  
Dynamic Monitoring ◽  
Tunnel Boring ◽  
The Impact

Tunnel water inrush is complex, fuzzy, and random, and it is affected by many factors, such as hydrology, geology, and construction. However, few papers have considered the impact of dynamic monitoring on water inrush in previous research. In this study, considering geological, hydrological, and construction factors, as well as dynamic monitoring, a new multi-index evaluation method is proposed to analyze the risk of tunnel water inrush based on the normal cloud model. A new weight algorithm combining analytic hierarchy process and entropy method is used to calculate the index weight. The certainty degree of each evaluation index belonging to the corresponding cloud can be obtained by the cloud model theory. The final level of tunnel water inrush is determined via the synthetic certainty degree. The proposed method is applied to analyze the risk of water inrush in the SS (Shuang-san) tunnel constructed by a tunnel boring machine in the arid area of Northwest China. The evaluation results are not only basically identical to the results calculated by the ideal point and gray relation projection methods, but also agree well with the actual excavation results. This demonstrates that this new risk assessment method of water inrush has high accuracy and feasibility. Simultaneously, it also provides a new research idea to analyze the probability of tunnel water inrush and can provide a reference for related projects.

A failure case study of tunnel water inrush source identified by statistical analysis model

2021 ◽  
Vol 14 (17) ◽  
Author(s):  
Li-Gang Yuan ◽  
Xiao-Li Li ◽  
Xin Li ◽  
Yi-Lin Yu ◽  
Li-Gang Chen ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Water Inrush ◽  
Analysis Model

scholarly journals Identification of Mine Water Inrush Source Based on PCA-FDA: Xiandewang Coal Mine Case

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Li ◽  
Qiang Wu ◽  
Zijie Liu
Keyword(s):  
Chemical Composition ◽  
Coal Mine ◽  
Mine Water ◽  
Source Identification ◽  
Water Inrush ◽  
Water Sources ◽  
Identification Accuracy ◽  
Fisher Discriminant Analysis ◽  
Water Chemical Composition ◽  
Identification Model

When mine water inrush accidents occur, timely and accurately identifying the water inrush source plays an important role in determining the cause of water inrush and making a solution to a disaster. According to the differences of water chemical composition in each water sources of mine, eight kinds of indicators of water chemical composition were selected as sample variables for water inrush source identification. On this basis, an identification model of water inrush source was established by using principal component analysis (PCA) and Fisher discriminant analysis (FDA) combined. The model was used to identify the water inrush source of 14 groups of training samples and 12 groups of samples to be judged in different water sources of the Xiandewang coal mine, and it was compared with the results of the conventional identification model which used the FDA method. Results of this study showed that having processed data by using the PCA method can effectively eliminate the effects of information superposition between sample indicators, and the identification accuracy of mine water inrush source was significantly increased. Related study in this paper can provide some basis and reference for the study of mine water inrush source identification technology.

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