Implementation of Fuzzy Reliability Analysis for Elastic Settlement of Strip Footing on Sand Considering Spatial Variability

Pramanik, R.; Baidya, Dilip Kumar; Dhang, Nirjhar

doi:10.1061/(asce)gm.1943-5622.0001514

Cited by 14 publications

(2 citation statements)

References 43 publications

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“…The estimation methods for reliability characteristics must be modified based on the fuzzy lifetimes to attain a more realistic analysis and exploit the uncertainty or imprecision in the data. The concept of the FS has also received considerable attention from system reliability analysis researchers such as Mahapatra and Roy (2012), Pan et al (2015), Pramanik et al (2019) and El-Damcese et al (2014).…”

Section: Introductionmentioning

confidence: 99%

The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution

2022

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In this paper, the two-parameter Pareto lifetime distribution is considered with vague shape and scale parameters, where parameters are set as generalized intuitionistic fuzzy numbers. A new L-R type intuitionistic fuzzy number is introduced, and cuts of the new fuzzy set are provided. The generalized intuitionistic fuzzy reliability characteristics such as reliability, conditional reliability, hazard rate and mean time to failure functions are defined, along with the special case of the two-parameter Pareto generalized intuitionistic fuzzy reliability analysis. Furthermore, the series and parallel system reliability are evaluated by the generalized intuitionistic fuzzy sets. Finally, for certain cases of the fuzzy shape and scale parameters and cut set values, the generalized intuitionistic fuzzy reliability characteristics are provided and compared through several illustrative plots.

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Section: Introductionmentioning

confidence: 99%

The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution

2022

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“…In recent years, various methods have been adopted to analyze the factors causing roof accidents and to accurately assess roof risk. Normally, the neural network and neuro-fuzzy models have been widely adopted in the risk assessment of engineering [14][15][16][17][18][19]. Scholars have proposed relevant models to assess the risk probability and to use decision-making principles to manage risks based on the analysis of most coal mine roof accidents [20].…”

Section: Introductionmentioning

confidence: 99%

The Comprehensive Identification of Roof Risk in a Fully Mechanized Working Face Using the Cloud Model

et al. 2021

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Roof accidents seriously affect the safe and efficient mining of the working faces. Therefore, it is necessary to assess and identify the possible and influencing factors on the occurrence of roof risk in a fully mechanized mining workface. In this study, based on the analytic hierarchy process and fuzzy comprehensive evaluation, a comprehensive standard cloud model was established through constructing a quantitative grade interval and calculating the weight of each index to achieve the aim of a roof risk assessment and identification. The accuracy of risk assessment was ensured by using the comprehensive analyses of various aspects, such as cloud digital features, risk assessment cloud image and standard cloud image. This showed that the main influencing factors on the occurrence of roof accidents were roof separation distance, weighting intensity and rib spalling followed by the coal body stress concentration, initial support force and geological conditions. Taking 42,115 fully mechanized working faces in the Yushen coal mining area as an engineering background, this model was adopted to assess and identify the risk of roof accidents through generating comprehensive assessment cloud images and introducing the Dice coefficient to calculate the similarity degree. The results showed that the overall risk of roof accidents in 42,115 working faces was regarded as grade II (general risk) through the overall index of comprehensive risk evaluation and a similarity degree of 0.8606. The impact of roof condition was mainly influenced by the risk of roof accidents, while the support status, personal working status and coal body condition had a limited effect on the risk of roof accidents. The comprehensive standard cloud model proposed in this study had strong visibility and discovered the key parts of risk indexes easily to solve the problems of ambiguity and quantitative identification in traditional roof risk evaluation methods. Therefore, this model was worth promoting, because it laid the foundation for the intelligent identification and early warning system of roof accident risk in a fully mechanized mining workface.

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Numerical Investigation of the Bearing Capacity Factors Using the Finite Element Method

Pramanik

Baidya

Dhang

2020

Lecture Notes in Civil Engineering

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Implementation of Fuzzy Reliability Analysis for Elastic Settlement of Strip Footing on Sand Considering Spatial Variability

Cited by 14 publications

References 43 publications

The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution

The reliability analysis based on the generalized intuitionistic fuzzy two-parameter Pareto distribution

The Comprehensive Identification of Roof Risk in a Fully Mechanized Working Face Using the Cloud Model

Numerical Investigation of the Bearing Capacity Factors Using the Finite Element Method

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