Poniższa praca miała na celu określenie, które z elementów budynków należy kontrolować nawet w trakcie mniej dokładnych przeglądów oraz jakie parametry są istotne w przypadku określania przybliżonej wartości zużycia technicznego budynków.
The method presented below is based on the extracted data processing by means of artificial neural networks. The aim is to learn the artificial neural network configurations for a set of data containing values of the technical deterioration and information about building repairs in last years (or other information and building parameters) and next to analyze new buildings by the instructed neural network. The profit from using ANN is the reduction of the number of parameters. Instead of more then forty parameters describing a building, about ten are usually sufficient for satisfactory accuracy. The net of Multilayer Perceptron type reached the best result (with architecture 10-4-1). Estimating the technical deterioration of building using ANN: has lower accuracy, can work on incomplete data, is more resistant to errors in data, needs small range of data as an input, it can be applied as one of many methods for estimating technical deterioration.
This paper presents result of the inspection of ninety five buildings in Warsaw. New method for analysis of a technical state of large-panel residential buildings has been proposed. This method is based on elements extracted from the classical methods and on data about repairs and modernization collected from building documentations.
Thierry J.: Remonty budynków i wzmacnianie konstrukcji. Arkady. Warszawa 1982.
Zużycie obiektów budowlanych. WACETOB, Warszawa 2000.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.