Developing a new fuzzy inference system for pipeline risk assessment

Jamshidi, Ali Ashraf; Yazdani-Chamzini, Abdolreza; Yakhchali, Siamak Haji; Khaleghi, Sohrab

doi:10.1016/j.jlp.2012.10.010

Cited by 175 publications

(93 citation statements)

References 49 publications

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“…FIS has been used in many applications since then. It has been applied in environmental models including: waste management (Vesely et al, 2016) forecasting air quality (Carbajal-Hernández et al, 2012a;Fisher, 2006;Sowlat et al, 2011) water quality (Carbajal-Hernández et al, 2012b;Che Osmi et al, 2016;Gharibi et al, 2012;Ocampo-Duque et al, 2006) models for performing risk assessment (Camastra et al, 2015;Jamshidi et al, 2013;Rodríguez et al, 2016) in the field of manufacturing and sales for supporting customers' requirements (Juang et al, 2007) forecasting automobile sales (Wang et al, 2011) stock price prediction (Chang & Liu, 2008) supplier selection (Lima Junior et al, 2013) measuring customer satisfaction (Zani et al, 2013). Similarly to our model, FIS has been applied in models for evaluating the performance level of several fields (Nadali et al, 2011;Nilashi et al, 2015).…”

Section: A Fuzzy Logic Approachmentioning

confidence: 99%

“…A combination of AHP and FIS has also been employed in several applications (Carreño Donevska et al, 2011;Nilashi et al, 2015;Rodríguez et al, 2016). A fuzzy logic inference system is a process from a given input of empirical values to an output including three main parts (Carbajal-Hernández et al, 2012b;Jamshidi et al, 2013;Ocampo-Duque et al, 2006;Ross, 2004): (1) membership functions; (2) fuzzy set operations; (3) IF-THEN inference rules. A Mamdani-type inference system (Mamdani & Assilian, 1975) is used in our model because of the more intuitive and human-like nature of its rules compared to other types (Che Osmi et al, 2016;Kovac et al, 2012).…”

Section: A Fuzzy Logic Approachmentioning

confidence: 99%

“…Max-min composition, as one of the most commonly used in the Mamdani inference model (Jamshidi et al, 2013;Monjezi & Rezaei, 2011;Ross, 2004), is applied:…”

Section: )mentioning

confidence: 99%

See 2 more Smart Citations

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

Oblak¹,

Kuzman²,

Grošelj³

2017

Istrazivanja I Projektovanja Za Privredu

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Section: A Fuzzy Logic Approachmentioning

confidence: 99%

Section: A Fuzzy Logic Approachmentioning

confidence: 99%

See 1 more Smart Citation

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

Oblak¹,

Kuzman²,

Grošelj³

2017

Istrazivanja I Projektovanja Za Privredu

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show abstract

“…The most common and popular membership functions were categorized as triangular, trapezoidal and Gaussian types (Markowski and Mannan, 2008;Jamshidi et al, 2012;Wulan and Petrovic, 2012). Suitable types of functions are selected based on experience, knowledge, problem, statements (Grima et al, 2000) and characteristics of variables (Xie, 2003).…”

Section: Fuzzificationmentioning

confidence: 99%

“…Mamdani's fuzzy model is the most commonly seen fuzzy inference system (Jamshidi et al, 2012). It uses the fuzzy sets and fuzzy logic concepts to translate linguistic and subjective terms into an algorithm (Mamdani and Assilian, 1975).…”

Section: Fuzzy Inferencementioning

confidence: 99%

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Karimpour

Zarghami

Moosavian

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-A new risk assessment methodology by using fuzzy logic is proposed in this paper. The new Fuzzy Inference System (FIS) was established by the Mamdani algorithm based on different consequences of an incident. A combination of two FIS formed the proposed fuzzy method. Human knowledge and brainstorming were the devices for making the rules and interdependencies between variables in the new model. Different types of consequences and effective parameters were considered as inputs for the first fuzzy inference system. The final consequence was the preliminary result of the first inference model. It added to the probability of failures, as inputs of the second inference model. The result of the second inference model was the risk factor, which was considered as the final output of the proposed new fuzzy model. This model makes risk assessment more convenient in the absence of suitable data. In addition, decision-making will be easier, since its results are more understandable than the results of classical methods. A case study and a comparison between the classic method and the new fuzzy model illustrated that the results of the proposed model are more accurate, reliable and convenient for use in decision-making.Résumé -Nouveau modèle à logique floue pour une évaluation de risque basée sur différents types de conséquences -Une nouvelle méthode d'évaluation des risques en utilisant une logique floue est proposée dans le présent article. Le nouveau système d'inférence flou a été établi en utilisant l'algorithme de Mamdani basé sur les différentes conséquences d'un incident. Une combinaison de deux systèmes d'inférence flous a donné naissance à la méthode floue proposée. Les connaissances humaines et un brainstorming ont été les outils qui ont permis d'établir les règles et interdépendances entre des variables du nouveau modèle. Différents types de conséquences et de paramètres effectifs ont été pris en considération comme entrées pour le premier système d'inférence flou. La conséquence finale qui est le résultat préliminaire du premier modèle d'inférence, est combinée avec la probabilité de risques, en tant qu'entrées du second modèle d'inférence. Cette combinaison conduit à considérer le facteur de risque comme le résultat final du nouveau modèle flou proposé. Ce modèle rend l'évaluation des risques plus facile, en l'absence de données adaptées. En outre, la prise de décision sera plus simple, puisque ses résultats sont plus compréhensibles que les résultats des méthodes classiques. Une étude de cas et une comparaison entre la méthode classique et le nouveau modèle flou ont démontré que les résultats du modèle proposé étaient plus précis, plus fiables et plus faciles à utiliser dans la prise de décision.

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Pipeline risk assessment using artificial intelligence: A case from the colombian oil network

Guzman-Urbina

Aoyama

2017

Process Safety Progress

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Currently, in order to make decisions regarding the safety of pipelines, the risk values and risk targets are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve a large number of variables and deal with high amounts of uncertainty. Therefore, there is a strong need for a powerful tool to cope with that uncertainty, and one of the best tools dealing with uncertainty is the implementation of artificial intelligence methods using fuzzy logic. Hence, this study aims to present an artificial intelligence inference system that minimizes the uncertainty of traditional approaches of risk assessment in pipelines. Also, in order to show the applicability of the model developed, this study presents a case from the Colombian oil transportation network. Besides that, this study presents an uncertainty analysis for the risk values, comparing the results of the inference system with traditional approach. The results show that the inference system performs better since the magnitude of the average error and its standard deviation are less than the traditional approach. © 2017 American Institute of Chemical Engineers Process Process Saf Prog 37:110–116, 2018

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Developing a new fuzzy inference system for pipeline risk assessment

Cited by 175 publications

References 49 publications

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Pipeline risk assessment using artificial intelligence: A case from the colombian oil network

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