A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model
Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lin, Chunjin [verfasserIn] Zhang, Meng [verfasserIn] Zhou, Zongqing [verfasserIn] Li, Liping [verfasserIn] Shi, Shaoshuai [verfasserIn] Chen, Yuxue [verfasserIn] Dai, Wenjie [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Tunnelling and underground space technology - Amsterdam [u.a.] : Elsevier Science, 1986, 95 |
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| Übergeordnetes Werk: |
volume:95 |
| DOI / URN: |
10.1016/j.tust.2019.103136 |
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| Katalog-ID: |
ELV003158454 |
|---|
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| 245 | 1 | 0 | |a A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model |
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| 520 | |a Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. | ||
| 650 | 4 | |a Water inrush | |
| 650 | 4 | |a Risk assessment | |
| 650 | 4 | |a Variable weight | |
| 650 | 4 | |a Cloud model | |
| 650 | 4 | |a Software development | |
| 700 | 1 | |a Zhang, Meng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Zongqing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Liping |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Shaoshuai |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yuxue |e verfasserin |4 aut | |
| 700 | 1 | |a Dai, Wenjie |e verfasserin |4 aut | |
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10.1016/j.tust.2019.103136 doi (DE-627)ELV003158454 (ELSEVIER)S0886-7798(19)30619-4 DE-627 ger DE-627 rda eng 690 DE-600 56.22 bkl Lin, Chunjin verfasserin aut A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. Water inrush Risk assessment Variable weight Cloud model Software development Zhang, Meng verfasserin aut Zhou, Zongqing verfasserin aut Li, Liping verfasserin aut Shi, Shaoshuai verfasserin aut Chen, Yuxue verfasserin aut Dai, Wenjie verfasserin aut Enthalten in Tunnelling and underground space technology Amsterdam [u.a.] : Elsevier Science, 1986 95 Online-Ressource (DE-627)320620808 (DE-600)2022637-8 (DE-576)259485365 1878-4364 nnns volume:95 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.22 Unterirdisches Bauen Tunnelbau AR 95 |
| spelling |
10.1016/j.tust.2019.103136 doi (DE-627)ELV003158454 (ELSEVIER)S0886-7798(19)30619-4 DE-627 ger DE-627 rda eng 690 DE-600 56.22 bkl Lin, Chunjin verfasserin aut A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. Water inrush Risk assessment Variable weight Cloud model Software development Zhang, Meng verfasserin aut Zhou, Zongqing verfasserin aut Li, Liping verfasserin aut Shi, Shaoshuai verfasserin aut Chen, Yuxue verfasserin aut Dai, Wenjie verfasserin aut Enthalten in Tunnelling and underground space technology Amsterdam [u.a.] : Elsevier Science, 1986 95 Online-Ressource (DE-627)320620808 (DE-600)2022637-8 (DE-576)259485365 1878-4364 nnns volume:95 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.22 Unterirdisches Bauen Tunnelbau AR 95 |
| allfields_unstemmed |
10.1016/j.tust.2019.103136 doi (DE-627)ELV003158454 (ELSEVIER)S0886-7798(19)30619-4 DE-627 ger DE-627 rda eng 690 DE-600 56.22 bkl Lin, Chunjin verfasserin aut A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. Water inrush Risk assessment Variable weight Cloud model Software development Zhang, Meng verfasserin aut Zhou, Zongqing verfasserin aut Li, Liping verfasserin aut Shi, Shaoshuai verfasserin aut Chen, Yuxue verfasserin aut Dai, Wenjie verfasserin aut Enthalten in Tunnelling and underground space technology Amsterdam [u.a.] : Elsevier Science, 1986 95 Online-Ressource (DE-627)320620808 (DE-600)2022637-8 (DE-576)259485365 1878-4364 nnns volume:95 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.22 Unterirdisches Bauen Tunnelbau AR 95 |
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10.1016/j.tust.2019.103136 doi (DE-627)ELV003158454 (ELSEVIER)S0886-7798(19)30619-4 DE-627 ger DE-627 rda eng 690 DE-600 56.22 bkl Lin, Chunjin verfasserin aut A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. Water inrush Risk assessment Variable weight Cloud model Software development Zhang, Meng verfasserin aut Zhou, Zongqing verfasserin aut Li, Liping verfasserin aut Shi, Shaoshuai verfasserin aut Chen, Yuxue verfasserin aut Dai, Wenjie verfasserin aut Enthalten in Tunnelling and underground space technology Amsterdam [u.a.] : Elsevier Science, 1986 95 Online-Ressource (DE-627)320620808 (DE-600)2022637-8 (DE-576)259485365 1878-4364 nnns volume:95 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.22 Unterirdisches Bauen Tunnelbau AR 95 |
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10.1016/j.tust.2019.103136 doi (DE-627)ELV003158454 (ELSEVIER)S0886-7798(19)30619-4 DE-627 ger DE-627 rda eng 690 DE-600 56.22 bkl Lin, Chunjin verfasserin aut A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. Water inrush Risk assessment Variable weight Cloud model Software development Zhang, Meng verfasserin aut Zhou, Zongqing verfasserin aut Li, Liping verfasserin aut Shi, Shaoshuai verfasserin aut Chen, Yuxue verfasserin aut Dai, Wenjie verfasserin aut Enthalten in Tunnelling and underground space technology Amsterdam [u.a.] : Elsevier Science, 1986 95 Online-Ressource (DE-627)320620808 (DE-600)2022637-8 (DE-576)259485365 1878-4364 nnns volume:95 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.22 Unterirdisches Bauen Tunnelbau AR 95 |
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Lin, Chunjin @@aut@@ Zhang, Meng @@aut@@ Zhou, Zongqing @@aut@@ Li, Liping @@aut@@ Shi, Shaoshuai @@aut@@ Chen, Yuxue @@aut@@ Dai, Wenjie @@aut@@ |
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Lin, Chunjin |
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Lin, Chunjin ddc 690 bkl 56.22 misc Water inrush misc Risk assessment misc Variable weight misc Cloud model misc Software development A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model |
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690 DE-600 56.22 bkl A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model Water inrush Risk assessment Variable weight Cloud model Software development |
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A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model |
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Lin, Chunjin Zhang, Meng Zhou, Zongqing Li, Liping Shi, Shaoshuai Chen, Yuxue Dai, Wenjie |
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a new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model |
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A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model |
| abstract |
Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. |
| abstractGer |
Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. |
| abstract_unstemmed |
Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation. |
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|
| score |
7.39884 |