Bridge safety monitoring and evaluation based on hesitant fuzzy set

Yan, Yi; Wu, Xuantao; Wu, Zhangyong

doi:10.1016/j.aej.2021.06.101

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…Early warning for bridges is seen as an effective means of ensuring safe operation [ 25 , 26 ]. By leveraging the vast monitoring data provided by the SHM systems, structural information can be extracted to accurately predict future responses of bridges and achieve early warnings [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Jiang,

Yang,

Liu

et al. 2024

Sensors

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Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures.

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Section: Introductionmentioning

confidence: 99%

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Jiang,

Yang,

Liu

et al. 2024

Sensors

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“…Recently, Yan et al (2022) presented a new bridge safety monitoring and evaluation method based on HF sets. For more latest and significant work on the theory of HF sets, the readers are suggested to Krishankumar et al (2022); Qin et al (2022); Wang et al (2022) and Yang et al (2023).…”

Section: Introductionmentioning

confidence: 99%

“…Mishra, Chen, and Rani (2022) introduced a multi‐attribute decision making technique by the fusion of both modified VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method and Fermatean HF sets see also Mishra, Liu, and Rani (2022). Recently, Yan et al (2022) presented a new bridge safety monitoring and evaluation method based on HF sets. For more latest and significant work on the theory of HF sets, the readers are suggested to Krishankumar et al (2022); Qin et al (2022); Wang et al (2022) and Yang et al (2023).…”

Section: Introductionmentioning

confidence: 99%

A new method of multi‐attribute group decision making based on hesitant fuzzy soft expert information

2023

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Interesting insights into uncertain multi‐attribute decision‐making systems have come from the theory of hesitant fuzzy sets. Outputs become more reliable when a decision‐maker is not forced to produce one single assessment in the presence of hesitation. Hesitancy in fuzzy environments grants more flexibility to the decision‐maker, therefore enhancing the validity of all subsequent results. Due to these facts, hesitation has become a landmark in the enhancement of fuzzy‐soft‐inspired theories, and techniques using models such as hesitant fuzzy soft sets or hesitant (fuzzy) ‐soft sets have thrived. However, the literature lacks a comparable improvement for fuzzy soft expert information. The main goal of this paper is the integration of both hesitation and fuzziness with the characteristics of soft expert sets. The resulting hybrid model is therefore called hesitant fuzzy soft expert set, and its utilization for multiple attribute group decision‐making (MAGDM) will be investigated too. To that purpose, we first study the required algebraic operations and properties (subsethood and equality, complement, union, intersection, plus AND and OR operation). Respective numerical examples illustrate their utilization. We also establish some important laws for hesitant fuzzy soft expert sets, including commutativity, associativity, distributivity, and De Morgan's laws. Moreover, we define four novel types of level soft expert sets for fuzzy soft expert sets, and three types of fuzzy soft expert sets for hesitant fuzzy soft expert sets, which are: ‐level soft expert sets, level soft expert sets regarding fuzzy threshold, mid‐level soft expert sets and top‐level soft expert sets. Armed with these tools, we present a natural algorithm for MAGDM under hesitant fuzzy soft expert information that is accompanied by an illustrative application that concerns a selection problem for the samples of electric vehicles manufactured by different companies. This is followed by a comparison with existing mathematical tools such as hesitant fuzzy soft sets and fuzzy soft expert sets, in order to show the advantages of our work over them. In the end, we provide some future research directions.

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“…Recently, the study of decision-making problems with the use of hesitant fuzzy information has become a significant focus of research, such as that of Liu et al [ 20 ], who introduced a correlation coefficient approach to determine the strength of association between HFSs, which can be used to evaluate whether they are negatively or positively associated. Zeng et al [ 21 ] introduced the weighted dual HFS, along with a few fundamental mathematical operations for weighted dual hesitant fuzzy elements, including union, intersection, multiplication, and complement; and Yan et al [ 22 ] proposed a mathematical model for monitoring and evaluation bridge safety based on HFS. The HFS comprises a significant weakness in terms of data loss; to address this flaw, an extension of HFS called the PHFS has been proposed that enhances the HFS with probability and is capable of retaining more information than the HFS.…”

Section: Introductionmentioning

confidence: 99%

Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model

Sultan

Sałabun

Faizi

et al. 2022

Sensors

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A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. However, it has a significant problem in its uses, i.e., considerable data loss. The probabilistic hesitant fuzzy set (PHFS) has been proposed to improve the HFS. It provides probability values to the HFS and has the ability to retain more information than the HFS. Previously, fuzzy regression models such as the fuzzy linear regression model (FLRM) and hesitant fuzzy linear regression model were used for decision making; however, these models do not provide information about the distribution. To address this issue, we proposed a probabilistic hesitant fuzzy linear regression model (PHFLRM) that incorporates distribution information to account for multi-criteria decision-making (MCDM) problems. The PHFLRM observes the input–output (IPOP) variables as probabilistic hesitant fuzzy elements (PHFEs) and uses a linear programming model (LPM) to estimate the parameters. A case study is used to illustrate the proposed methodology. Additionally, an MCDM technique called the technique for order preference by similarity to ideal solution (TOPSIS) is employed to compare the PHFLRM findings with those obtained using TOPSIS. Lastly, Spearman’s rank correlation test assesses the statistical significance of two rankings sets.

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Bridge safety monitoring and evaluation based on hesitant fuzzy set

Cited by 8 publications

References 26 publications

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

Early Warning for Continuous Rigid Frame Bridges Based on Nonlinear Modeling for Temperature-Induced Deflection

A new method of multi‐attribute group decision making based on hesitant fuzzy soft expert information

Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model

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