Fuzzy logic for piping risk assessment (pfLOPA)

Markowski, A.; Mannan, M. Sam

doi:10.1016/j.jlp.2009.06.011

Cited by 121 publications

(34 citation statements)

References 13 publications

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“…The fuzzy risk matrix has been introduced and described in detail in previous work [4]. There are some studies in the literature which use the fuzzy risk matrix to risk assessment [5][6][7]. In order to synthesize the severity and probability, this paper introduces a fuzzy risk matrix using some trapezoidal fuzzy numbers, so that the severity and probability can be put into a unified scale.…”

Section: Define the Fuzzy Assessment Matrixmentioning

confidence: 99%

A synthetic risk assessment model based on AHP

Zhao

Jiao

Zhao

2014

2014 Reliability and Maintainability Symposium

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Section: Define the Fuzzy Assessment Matrixmentioning

confidence: 99%

A synthetic risk assessment model based on AHP

Zhao

Jiao

Zhao

2014

2014 Reliability and Maintainability Symposium

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“…However, the different databases of accidents reported a number of pipeline failures with a release of transported medium to the environment and substantial losses. Adam et al explore the application of the fuzzy logic for risk assessment of major hazards connected with transportation of flammable substances in long pipelines [5].…”

Section: Piping Risk Assessmentmentioning

confidence: 99%

Fuzzy Logic Applications In Chemical Processes

Emami¹

2010

J. Math. Computer Sci.

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Abstract:Fuzzy logic a new approach was intended to emulate human reasoning using calculations and operations with fuzzy groups and linguistic variables. Fuzzy variables describe qualitative expressions such as very slow, slow, fast, very fast, and so on. The application of fuzzy logic techniques has been increasing rapidly in the last few years. Fuzzy logic is used in target tracking, pattern recognition, robotics, power systems, controller design, chemical engineering, biomedical engineering, vehicular technology, economy management and decision making, aerospace applications, communications and networking, electronic engineering, and civil engineering. In many chemical engineering systems, the classification of product quality characteristics is performed by human experts, due to the absence of measuring devices. The development of mathematical models for such systems is a rather difficult task, since no equations based on first principles can be written. Chemical engineering has employed fuzzy logic in the detection of chemical agents as well as gas recognition. It has also been applied to processes control, batch distillation column, separation process, and kinetics. In this research, we investigate these applications in more detail.

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“…Fuzzy logic is the most popular risk assessment and is a risk analysis method that can be deployed in almost any field [5,6]. Fuzzy logic is a type of logic with many values, in which the range of truth values lies between 0 and 1.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Risk Index Model Based on Hierarchical Fuzzy Logic for Underground Risk Assessment

et al. 2017

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Available space in congested cities is getting scarce due to growing urbanization in the recent past. The utilization of underground space is considered as a solution to the limited space in smart cities. The numbers of underground facilities are growing day by day in the developing world. Typical underground facilities include the transit subway, parking lots, electric lines, water supply and sewer lines. The likelihood of the occurrence of accidents due to underground facilities is a random phenomenon. To avoid any accidental loss, a risk assessment method is required to conduct the continuous risk assessment and report any abnormality before it happens. In this paper, we have proposed a hierarchical fuzzy inference based model for under-ground risk assessment. The proposed hierarchical fuzzy inference architecture reduces the total number of rules from the rule base. Rule reduction is important because the curse of dimensionality damages the transparency and interpretation as it is very tough to understand and justify hundreds or thousands of fuzzy rules. The computation time also increases as rules increase. The proposed model takes 175 rules having eight input parameters to compute the risk index, and the conventional fuzzy logic requires 390,625 rules, having the same number of input parameters to compute risk index. Hence, the proposed model significantly reduces the curse of dimensionality. Rule design for fuzzy logic is also a tedious task. In this paper, we have also introduced new rule schemes, namely maximum rule-based and average rule-based; both schemes can be used interchangeably according to the logic needed for rule design. The experimental results show that the proposed method is a virtuous choice for risk index calculation where the numbers of variables are greater.

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Fuzzy logic for piping risk assessment (pfLOPA)

Cited by 121 publications

References 13 publications

A synthetic risk assessment model based on AHP

A synthetic risk assessment model based on AHP

Fuzzy Logic Applications In Chemical Processes

An Integrated Risk Index Model Based on Hierarchical Fuzzy Logic for Underground Risk Assessment

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