Dam Safety Evaluation Based on Interval-Valued Intuitionistic Fuzzy Sets and Evidence Theory

Considering the multi-sources, heterogeneity and complexity of dam safety assessment, a dam safety assessment model based on interval-valued intuitionistic fuzzy set and evidence theory is proposed to perform dam safety reliability evaluations. In the proposed model, the dynamic reliability based on the supporting degree is applied to modify the data from homologous information. The interval-valued intuitionistic fuzzy set is used to describing the uncertainty and fuzziness between heterogeneous information. Evidence theory is employed to integrate the data from heterogeneous information. Finally, a multiple-arch dam undergoing structural reinforcement is taken as an example. The evaluation result before reinforcement shows that the safety degree of the dam is low and the potential risk is more likely to be located at the dam section #13. From the geological survey before reinforcement, there exist weak fracture zone and broken mud belt in the foundation of the dam section #13. The comparison between the evaluation results before and after reinforcement indicates that the dam become safer and more stable after reinforcement.

[1]  Yang Liu,et al.  Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory , 2017, Inf. Fusion.

[2]  Muhammet Deveci,et al.  Evaluation of service quality in public bus transportation using interval-valued intuitionistic fuzzy QFD methodology , 2019 .

[3]  Irfan Deli,et al.  Intuitionistic fuzzy parameterized soft set theory and its decision making , 2013, Appl. Soft Comput..

[4]  Tianyi Hu,et al.  A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data , 2018, Structural Health Monitoring.

[5]  Zhiguo Zhou,et al.  A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base , 2017 .

[6]  Chiakuei Peng,et al.  Twistor bundle theory and its application , 2004 .

[7]  Anjali Awasthi,et al.  Using AHP and Dempster-Shafer theory for evaluating sustainable transport solutions , 2011, Environ. Model. Softw..

[8]  L. Lu,et al.  Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory , 2012, Int. J. Geogr. Inf. Sci..

[9]  Yafei Song,et al.  Sensor dynamic reliability evaluation based on evidence theory and intuitionistic fuzzy sets , 2018, Applied Intelligence.

[10]  P. Smets,et al.  Assessing sensor reliability for multisensor data fusion within the transferable belief model , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  B. K. Tripathy,et al.  Fuzzy Soft Set Theory and Its Application in Group Decision Making , 2016 .

[12]  Bowen Wei,et al.  Combination forecast model for concrete dam displacement considering residual correction , 2019 .

[13]  Huaizhi Su,et al.  Deep Stability Evaluation of High-Gravity Dam under Combining Action of Powerhouse and Dam , 2013 .

[14]  Harish Garg,et al.  TOPSIS method based on the connection number of set pair analysis under interval-valued intuitionistic fuzzy set environment , 2016, Computational and Applied Mathematics.

[15]  J. Kacprzyk,et al.  Advances in the Dempster-Shafer theory of evidence , 1994 .

[16]  Xiaohong Yuan,et al.  Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory , 2007, Inf. Fusion.

[17]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[18]  Fuyuan Xiao,et al.  Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy , 2019, Inf. Fusion.

[19]  Cengiz Kahraman,et al.  A corridor selection for locating autonomous vehicles using an interval-valued intuitionistic fuzzy AHP and TOPSIS method , 2019, Soft Computing.

[20]  Huaizhi Su,et al.  Multisource information fusion‐based approach diagnosing structural behavior of dam engineering , 2018 .

[21]  Surapati Pramanik,et al.  TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment , 2014, Neural Computing and Applications.

[22]  Yong Deng,et al.  Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[24]  Yafei Song,et al.  An evidential view of similarity measure for Atanassov's intuitionistic fuzzy sets , 2016, J. Intell. Fuzzy Syst..

[25]  Aminah Robinson Fayek,et al.  A multi-criteria optimization framework for industrial shop scheduling using fuzzy set theory , 2010, Integr. Comput. Aided Eng..

[26]  Janusz Kacprzyk,et al.  Entropy for intuitionistic fuzzy sets , 2001, Fuzzy Sets Syst..

[27]  Dieu Tien Bui,et al.  A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam , 2018, Neural Computing and Applications.