ArticleComputational intelligence and neuroscience2016
Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks.
Article in Computational intelligence and neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Predictive analysis of concrete slump using a stochastic search-consolidated neural network.Heliyon · 2024Article
- Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning.PloS one · 2024Article
- Estimation of concrete materials uniaxial compressive strength using soft computing techniques.Heliyon · 2023Article
- Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.Journal of cellular and molecular medicine · 2022Article
- Prediction of the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network.Materials (Basel, Switzerland) · 2021Article
- Article
- The FP4026 Research Database on the fundamental period of RC infilled frame structures.Data in brief · 2016Article
- Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash.Materials (Basel, Switzerland) · 2016Article
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7 authors.
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Abstract
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.
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