A new application area of ANN and ANFIS: determination of earthquake load reduction factor of prefabricated industrial buildings

The earthquake load reduction factor, R, is one of the most important parameters in the design stage of a building. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last earthquakes in Turkey and the experts agreed that they resulted mainly...

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Bibliographic Details
Published in:Civil engineering and environmental systems 2010-03, Vol.27 (1), p.53-69
Main Authors: Ceylan, M., Arslan, M. H., Ceylan, R., Kaltakci, M. Y., Ozbay, Y.
Format: Article
Language:English
Subjects:
adaptive neuro-fuzzy inference system
Applied sciences
Artificial intelligence
artificial neural network
Buildings
Buildings. Public works
Computation methods. Tables. Charts
damage
earthquake load reduction factor
Exact sciences and technology
Geotechnics
Industrial facility (building, storage, etc.)
Neural networks
Parameter optimization
prefabricated industrial buildings
Seismic engineering
Structural analysis. Stresses
Structure-soil interaction
Types of buildings
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Summary:The earthquake load reduction factor, R, is one of the most important parameters in the design stage of a building. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last earthquakes in Turkey and the experts agreed that they resulted mainly from the incorrectly selected earthquake load reduction factor, R. In this study, an attempt was made to estimate the R coefficient for prefabricated industrial structures having a single storey, one and two bays, which are commonly constructed for manufacturing and warehouse operation with variable dimensions. According to the selected variable dimensions, 280 sample (140 samples for one bay (S-1) and 140 samples for two bays (S-2)) frames' load-displacement relations were computed using pushover analysis and the earthquake load reduction factor, R, was calculated for each frame. Then, formulated three-layered artificial neural network methods (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were trained by using 214 of the 280 sample frames. Then, the methods were tested with the other 66 sample frames. Accuracy rates were found to be about 94% and 96% for ANN and ANFIS, respectively. The use of ANN and ANFIS provided an alternative way for estimating the R and it also showed that ANFIS estimated R more successfully than ANN.
ISSN:1028-6608
1029-0249
DOI:10.1080/10286600802506726