Extended fuzzy signature based model for qualification of residential buildings

Bukovics, G. Fogarasi, L. Kóczy

Research output: Chapter in Book/Report/Conference proceedingChapter


Residential buildings can be qualified and ranked based on many viewpoints. For the intervening decision-supporting survey of old residential buildings in the course of our former researches we have created a fuzzy signature based model which defines status evaluation and ranking of buildings on the basis of the condition of load-bearing structures and other building structures. We have extended and changed this model in a way so that it should take into account other viewpoints, too, which, in addition to the load bearing viewpoints strongly influence the manner of intervening. Since in addition to the importance of the given structure the relevance of the building structures of residential buildings are determined also by their quantities and other features, in our case it was necessary to determine relative and absolute relevance weights. We use a structure of fuzzy signature with variable aggregations, where the definition of aggregations is made by parameters, and the value of parameters are changing depending on the specific application, which follow the changes of relevance of given subtrees. The developed method is examined on the basis of a database for which we were used status evaluating expert reports relating to real stock of residential buildings.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer Verlag
Number of pages7
Publication statusPublished - Jan 1 2020

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X



  • Decision support
  • Fuzzy signatures
  • Parametric aggregation
  • Residential building

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Bukovics, Fogarasi, G., & Kóczy, L. (2020). Extended fuzzy signature based model for qualification of residential buildings. In Studies in Computational Intelligence (pp. 91-97). (Studies in Computational Intelligence; Vol. 819). Springer Verlag. https://doi.org/10.1007/978-3-030-16024-1_12