Risk Evaluation Approach and Application Research on Fuzzy-FMECA Method Based on Cloud Model

Xu, Zhengjie; Zhang, Youpeng; Su, Hongsheng

doi:10.11591/telkomnika.v12i2.3886

Cited by 4 publications

(4 citation statements)

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“…is paper has found that the project objective, i.e., time, cost, and quality criteria, should be weighted to obtain the ranking of the risk variable. It differs from other studies in which they do not weight the project objectives, such as [26,27].…”

Section: Introductionmentioning

confidence: 70%

“…ey have adopted this technique in order to simplify and enhance the risk assessment procedures in traditional FMECA [26], taking advantage of the basic idea of TOPSIS that allows measurement of the Euclidean distance of an alternative from an ideal goal. To solve the problem of risk variable ranking, we apply fuzzy concepts related to linguistic variables and integrated with the TOPSIS to solve multicriteria problems.…”

Section: Introductionmentioning

confidence: 99%

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Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

Husin

Fachrurrazi

Rizalihadi

et al. 2019

Advances in Civil Engineering

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Managing construction risks with a large number of risks with small impact can increase the additional effort and cost of inefficient construction. Therefore the variables need to be eliminated. The aim of this study is ranking the risk variable based on its frequency of occurrence by integrating time, cost, and quality criteria simultaneously and selecting the top ten variables with the order of the most significant impact. The risk variable ranking based on triple project objective of cost, time, and quality simultaneously is a challenge for particular projects or regions contributing to the risk context. A number of 127 qualitative risk variables of 14 factors occurring in a project to be eliminated require a method/technique. A fuzzy TOPSIS method involving linguistics data is proposed to capture vague conditions. Results show that the top ten rankings of risk variables based on integrating the different weights of cost, time, and quality are successfully identified by concluding that the labour factor is the most dominant variable affecting project risk in context the rehabilitation and reconstruction posttsunami disaster, especially in Aceh-Indonesia. The variables are lack of labour, unskilled labour, undisciplined labour, and low productivity of labour. This condition can differ from different risk contexts. This research is different from other studies that only review cost, time, and quality separately. We stated that to integrate all three criteria of cost, time, and quality simultaneously is more logic to analyze risk variable ranking.

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Section: Introductionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

Husin

Fachrurrazi

Rizalihadi

et al. 2019

Advances in Civil Engineering

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“…Cloud model theory mainly realizes the interconversion of qualitative concepts and quantitative values through two major cloud model numerical generators, namely the forward cloud generator and the inverse cloud generator [9][10][11] .…”

Section: Cloud Model Generatormentioning

confidence: 99%

Risk hierarchical management and control model based on AHP-DEMATEL-cloud model

2024

ferm

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In this paper, for the risk grading evaluation of China's coal mining enterprises, there are problems such as imperfect risk indicator system and subjectivity of evaluation results, etc. From the four elements of "human-machine-environment-pipe" safety production management, we construct a risk evaluation indicator system that meets the characteristics of mine safety production and construct a risk grading evaluation model based on AHP-DEMATEL-Cloud model. Based on AHP-DEMATEL-Cloud model, we construct a risk grading evaluation model. Taking a coal mining enterprise as the research object, the comprehensive risk assessment and grading application is carried out to obtain the risk level of the enterprise and the corresponding improvement opinions.

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“…A cloud is composed of numerous cloud droplets. Each cloud droplet (x, µ T (x)) is a point where the qualitative concept maps to the number field space [33].…”

Section: The Cloud Modelmentioning

confidence: 99%

Quality Risk Perception of Rectification and Reinforcement in a High-Rise Building under Uncertainty

Bu,

Yue

2024

Buildings

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There are many complex and uncertain factors in the process of building rectification and reinforcement that can easily lead to construction quality failures. This study develops a novel hybrid risk analysis approach to perceive the construction quality risk under uncertainty by integrating the extension theory (ET), the cloud model (CM), the Dempster–Shafer (D-S) evidence theory and the dynamic Bayesian network (DBN). The extended cloud model (ECM) combining the ET and the CM is not only effective in avoiding information loss, but is also capable of dealing with the ambiguity and randomness in risk assessment. The ECM is employed to construct the basic probability assignments (BPA) of risk factors across different risk states. The improved D-S evidence theory considering the expert importance coefficient is used for the fusion of expert judgments. A DBN model integrating monitoring indicators is established to predict the dynamics of overall quality risk during rectification and reinforcement. Then, the measured data of settlement difference and settlement rate are fed back to the DBN model to update the risk assessment results in real time. Finally, a case study of the rectification and reinforcement in a high-rise building is taken to verify the feasibility and validity of the developed risk analysis approach. The risk assessment results better reflect the unexpected risk events in actual construction. The proposed approach provides a research paradigm for quality risk assessment of similar rectification and reinforcement projects.

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Risk Evaluation Approach and Application Research on Fuzzy-FMECA Method Based on Cloud Model

Cited by 4 publications

References 0 publications

Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

Implementing Fuzzy TOPSIS on Project Risk Variable Ranking

Risk hierarchical management and control model based on AHP-DEMATEL-cloud model

Quality Risk Perception of Rectification and Reinforcement in a High-Rise Building under Uncertainty

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