A Prediction Method of Enterprise Evaluation Based on Genetic Taboo Algorithm

Zhu, Yuping

doi:10.1109/cicn59264.2023.10402240

2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN) 2023

DOI: 10.1109/cicn59264.2023.10402240

|View full text |Cite

A Prediction Method of Enterprise Evaluation Based on Genetic Taboo Algorithm

Yuping Zhu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Risk Assessment of Underground Tunnel Engineering Based on Pythagorean Fuzzy Sets and Bayesian Networks

Wang,

Jiang,

2024

Buildings

View full text Add to dashboard Cite

With the acceleration of urbanization, the importance of risk management in underground construction projects has become increasingly prominent. In the process of risk assessment for underground construction projects, the uncertainty of subjective factors from experts poses a significant challenge to the accuracy of assessment outcomes. This paper takes a section of the Nanchang Metro Line 2 as the research object, aiming to address the subjectivity issues in the risk assessment of underground construction projects and to enhance the scientific rigor and accuracy of the assessment. The study initially conducts a comprehensive identification and analysis of risk factors in underground engineering through a literature review and expert consultation method. Based on this, this paper introduces the theory of Pythagorean fuzzy sets to improve the Delphi method in order to reduce the impact of subjectivity in expert assessments. Furthermore, this paper constructs a Bayesian network model, incorporating risk factors into the network, and quantifies the construction risks through a probabilistic inference mechanism. The research findings indicate a total of 12 key risk factors that have been identified across four dimensions: geological and groundwater conditions, tunnel construction technical risks, construction management measures, and the surrounding environment. The Bayesian network assessment results indicate that the effectiveness of engineering quality management and the state of safety management at the construction site are the two most influential factors. Based on the assessment results, this paper further conducts a risk control analysis and proposes targeted risk management measures.

show abstract

Risk Assessment of Underground Tunnel Engineering Based on Pythagorean Fuzzy Sets and Bayesian Networks

Wang,

Jiang,

2024

Buildings

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Prediction Method of Enterprise Evaluation Based on Genetic Taboo Algorithm

Cited by 1 publication

References 19 publications

Risk Assessment of Underground Tunnel Engineering Based on Pythagorean Fuzzy Sets and Bayesian Networks

Risk Assessment of Underground Tunnel Engineering Based on Pythagorean Fuzzy Sets and Bayesian Networks

Contact Info

Product

Resources

About