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RESEARCH PRODUCT
Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks
Athanasios K. TsarisLiborio CavaleriConstantinos C. RepapisDimitrios KarypidisAngeliki PapalouFabio Di TrapaniPanagiotis G. Asterissubject
General Computer ScienceArticle SubjectComputer scienceGeneral MathematicsStructure (category theory)020101 civil engineering02 engineering and technologylcsh:Computer applications to medicine. Medical informatics0201 civil engineeringSeismic analysislcsh:RC321-571Materials Testing0202 electrical engineering electronic engineering information engineeringInfillmedicineMathematics (all)lcsh:Neurosciences. Biological psychiatry. NeuropsychiatryMaterials Testing; Neural Networks (Computer); Neuroscience (all); Computer Science (all); Mathematics (all)Neuroscience (all)Artificial neural networkbusiness.industryGeneral NeuroscienceFrame (networking)Computer Science (all)StiffnessGeneral MedicineStructural engineeringNeural Networks (Computer)Reinforced concretelcsh:R858-859.7020201 artificial intelligence & image processingArtificial intelligenceNeural Networks Computermedicine.symptombusinessPeriod (music)Research Articledescription
The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-12-01 | Computational Intelligence and Neuroscience |