Hierarchical modelling of timber reference properties using probabilistic methods: Maximum Likelihood Method, Bayesian Methods and Probability Networks

In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.

Численное моделирование нелинейной динамики дрейфовых вихревых структур в неоднородной вязкой ионосферной плазме

გამოკვლეულია ძლიერად ლოკალიზებული გრიგალური სტრუქტურების გენერაციისა და არაწრფივი დინამიკის თავისებურებანი მაგნიტოაქტიურ ნაწილობრივ-იონიზებულ არაერთგვაროვან დისიპაციურ პლაზმაში. მიღებულია გრძელ-ტალღოვანი დრეიფული ტალღური სტრუქტურების გადატანის არაწრფივი განტოლება. განმხოლოებული გრიგალური სტრუქტურების ერთმანეთთან და გარემოსთან ურთიერთქმედების დადგენილი სურათი აჩვენებს, რომ არაერთგვაროვან პლაზმურ გარემოში გრიგალურ სტრუქტურებს შეუძლიათ განაპირობონ ძლიერი დრეიფული ტურბულენტობის, ნივთიერებისა და სითბოს ანომალური გადატანის ფორმირება.

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