Risk assessment of water inrush in karst tunnels excavation based on normal cloud model

Wang, Xintong; Li, Shucai; Hu, Jie; Pan, Dongdong; Xu, Zhenhao

doi:10.1007/s10064-018-1294-6

Cited by 106 publications

(46 citation statements)

References 30 publications

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“…The standardization process and the AHP method are employed to establish the novel Cloud model for evaluating water inrush risk in tunnel through water-rich fault. In the researches of risk assessment based on Cloud model, the standardization of evaluation index is not a necessary process, and these researches have also obtained well evaluation and prediction results (Liu et al 2013;Wang, Li, et al 2019). To analyze the validity of the standardization process on the assessment results, the AHP-Cloud method without standardization process, namely without standardization process of evaluation index in section3.2, is adopted to evaluate the water inrush risk in Longjinxi Tunnel.…”

Section: Discussionmentioning

confidence: 99%

“…There are many parameters can reflect the degree of groundwater recharge. Among which, the monitoring of water pressure in tunnel construction is relatively simple, so water pressure is selected as the evaluation parameter to reflect groundwater recharge, which is beneficial to the engineering application of the evaluation standard system (Peng et al 2016;Wang, Li, et al 2019).…”

Section: Hydrologic Geological Conditionsmentioning

confidence: 99%

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Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model

Peng

Zuo

et al. 2020

Geomatics, Natural Hazards and Risk

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2020)Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model, ABSTRACTWater inrush is a serious geological disaster in tunnel. Due to the complexity of geological conditions, large-scale water inrush is prone to occur in tunnel through water-rich fault during construction. Based on the comprehensive analysis of influencing factors, 8 evaluation indexes and the corresponding grading criteria are put forward to assess the risk of water inrush, and the risk of water inrush is divided into 4 levels. Synthesizing the standardization process and the analytic hierarchy process (AHP), a novel Cloud model is established to assess the risk of tunnel water inrush. The model is applied to the Longjinxi Tunnel to assess the risk level of water inrush. It is proved that the assessment results of the tunnel through water-rich fault is consistent with the actual situation, which verifies the reliability of evaluation model. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Section: Hydrologic Geological Conditionsmentioning

confidence: 99%

Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model

Peng

Zuo

et al. 2020

Geomatics, Natural Hazards and Risk

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“…However, the influencing factors of water inrush in karst tunnels is very complex and various. According to the previous research results, the influencing factors can be categorized into disaster-related environment factors and disaster-causing factors [6,13,16]. Since the static evaluation occurs in the design stage, the disaster-related environmental factors were selected as evaluation indexes.…”

Section: Fine Risk Evaluation Methods Of Water Inrushmentioning

confidence: 99%

“…Hao et al [12] proposed a methodology based on the geo-mechanical model and quantification model of margins and uncertainties to evaluate the risk of water inrush. Wang et al [13] proposed a weighting method combining the analytic hierarchy process, entropy method, and statistical methods, and established a new assessment method based on the normal cloud model. With the wide application of computers in risk assessment, Bukowski [14] proposed a risk assessment system based on inflow intensity and the amount of suspended material.…”

Section: Introductionmentioning

confidence: 99%

Study on Early Warning Method for Water Inrush in Tunnel Based on Fine Risk Evaluation and Hierarchical Advance Forecast

Wang

et al. 2019

Geosciences

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Water inrush is one of the most frequent and harmful geological disasters in tunnel construction. In order to effectively prevent and control the occurrence of water inrush, an early warning method based on fine risk evaluation and hierarchical advanced forecast is proposed. Water inrush is a complex dynamic coupling factors system, the relationship between influencing factors and water inrush is strongly nonlinear. Therefore, the efficacy coefficient model, which has the advantages of standardization, conciseness, and freedom from subjective factors, is improved nonlinearly. The fine risk evaluation theory and method based on the improved efficacy coefficient model consisted of two parts: one is static evaluation used in design stage, and the other is dynamic evaluation applied in the construction stage. The index weights are determined scientifically and reasonably by Analytical Hierarchy Process (AHP) and the entropy method. According to the fine risk evaluation results, combined with the advantages and disadvantages of various forecasting methods, a multistep hierarchical detection method of disaster resources for water inrush is proposed to identify the occurrence characteristics and failure level of disaster sources. The theory has been successfully applied to the #3 inclined well of Yuelongmen Tunnel in Cheng-Lan Railway. The evaluation results had good agreement with the actual excavation data, which indicates that the model is of high credibility and feasibility. The method could improve the prediction accuracy of water inrush and explore geometric characteristics and filling of disaster-causing structures. It is of great significance for avoiding water inrush and guiding the rapid and safe tunnel construction.

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“…Mathematically, the distribution of x, can be determined by three numerical characteristics: expectation parameter Ex, entropy parameter En and hyper entropy parameter He [29][30][31]. According to Figure 1, Ex is the expectancy of the cloud drop in the universe of discourse and the best characterization of a qualitative concept.…”

Section: The Normal Cloud Modelmentioning

confidence: 99%

A Normal Cloud Model-Based Method for Water Quality Assessment of Springs and Its Application in Jinan

Wang

Yang

et al. 2019

Sustainability

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Springs are a source of drinking water and a famous tourist attraction in Jinan, China. In this paper, a multi-index evaluation method was proposed based on a normal cloud model. This model is new graphic model, which could synthetically picture the randomness and fuzziness of concepts. Ten parameters were selected, and water quality was classified into five levels. Three numerical characteristics were calculated, and the weights were assigned by an integrated weighting algorithm. The uncertainty of each spring was calculated by a cloud generator and the integrated certainty grades of water quality were determined. To ensure the accuracy of the normal cloud model, the proposed method was used to assess the water quality of springs in Jinan, China. The results obtained by the proposed method were compared with that of the other four methods. The results obtained by different methods are highly consistent. The proposed cloud model-based method can reflect the water quality level and provides a practical guide for water quality evaluation, as demonstrated in Jinan springs.

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Risk assessment of water inrush in karst tunnels excavation based on normal cloud model

Cited by 106 publications

References 30 publications

Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model

Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model

Study on Early Warning Method for Water Inrush in Tunnel Based on Fine Risk Evaluation and Hierarchical Advance Forecast

A Normal Cloud Model-Based Method for Water Quality Assessment of Springs and Its Application in Jinan

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