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Showing 2 results for Mazandarani

A. Gholami, T.h. Shah , M. Mazandarani ,
Volume 18, Issue 2 (International Journal of Engineering 2007)
Abstract

Abstract: The big share of electrical breakdown in electrical devices failure among other factors is caused by multitasking such as electrical insulation, mechanical support, energy dissipation, Energy storage, etc. which brings many attentions to lifetime estimation of said insulation material. Up to now, there was no-general theory had been suggested for lifetime estimation of mentioned insulation material the main reason of that was the lack of knowledge on interfering mechanisms. This paper is devoted to suggest a new state-of-art lifetime estimation method with the interest to reduce test procedure time consumption. At first briefly, suggested method has been surveyed to bold its advantages and drawbacks. The lifetime of insulating material estimated from our method, which has been named as HAMD, was better than estimated from the other tests and found to show good agreement with the experimental results.

  


Mahdi Ruhparvar, Hamed Mazandarani Zadeh, Farnad Nasirzadeh,
Volume 25, Issue 2 (IIJEPR 2014)
Abstract

An equitable risk allocation between contracting parties plays a vital role in enhancing the performance of the project. This research presents a new quantitative risk allocation approach by integrating fuzzy logic and bargaining game theory. Owing to the imprecise and uncertain nature of players’ payoffs at different risk allocation strategies, fuzzy logic is implemented to determine the value of players’ payoffs based on the experience and subjective judgment of experts involved in the project. Having determined the players' payoffs, bargaining game theory is then applied to find the equitable risk allocation between the client and contractor. Four different methods including symmetric Nash, non-symmetric Nash, non-symmetric Kalai–Smorodinsky and non-symmetric area monotonic are implemented to determine the equitable risk allocation. To evaluate the performance of the proposed model, it is implemented in a pipeline project and the quantitative risk allocation is performed for the inflation risk as one of the most significant identified risks.

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