Prediction of the fundamental period of infilled rc frame structures using artificial neural networks

Fabio Di Trapani, Liborio Cavaleri, Constantinos C. Repapis, Athanasios K. Tsaris, Dimitrios F. Karypidis, Angeliki Papalou, Panagiotis G. Asteris

Risultato della ricerca: Article

48 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)1-12
Numero di pagine12
RivistaComputational Intelligence and Neuroscience
Volume2016
Stato di pubblicazionePublished - 2016

Fingerprint

Frame Structure
Artificial Neural Network
Reinforced concrete
Concrete Structures
Reinforced Concrete
Neural networks
Concrete construction
Prediction
Seismic design
Stiffness
Large Data Sets
Datasets
Predict

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Neuroscience(all)
  • Mathematics(all)

Cita questo

Prediction of the fundamental period of infilled rc frame structures using artificial neural networks. / Di Trapani, Fabio; Cavaleri, Liborio; Repapis, Constantinos C.; Tsaris, Athanasios K.; Karypidis, Dimitrios F.; Papalou, Angeliki; Asteris, Panagiotis G.

In: Computational Intelligence and Neuroscience, Vol. 2016, 2016, pag. 1-12.

Risultato della ricerca: Article

Di Trapani, Fabio ; Cavaleri, Liborio ; Repapis, Constantinos C. ; Tsaris, Athanasios K. ; Karypidis, Dimitrios F. ; Papalou, Angeliki ; Asteris, Panagiotis G. / Prediction of the fundamental period of infilled rc frame structures using artificial neural networks. In: Computational Intelligence and Neuroscience. 2016 ; Vol. 2016. pagg. 1-12.
@article{c251ea9d29a848b9bdd5ee380be1c110,
title = "Prediction of the fundamental period of infilled rc frame structures using artificial neural networks",
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.",
keywords = "Computer Science (all), Materials Testing, Mathematics (all), Neural Networks (Computer), Neuroscience (all)",
author = "{Di Trapani}, Fabio and Liborio Cavaleri and Repapis, {Constantinos C.} and Tsaris, {Athanasios K.} and Karypidis, {Dimitrios F.} and Angeliki Papalou and Asteris, {Panagiotis G.}",
year = "2016",
language = "English",
volume = "2016",
pages = "1--12",
journal = "Computational Intelligence and Neuroscience",
issn = "1687-5265",
publisher = "Hindawi Publishing Corporation",

}

TY - JOUR

T1 - Prediction of the fundamental period of infilled rc frame structures using artificial neural networks

AU - Di Trapani, Fabio

AU - Cavaleri, Liborio

AU - Repapis, Constantinos C.

AU - Tsaris, Athanasios K.

AU - Karypidis, Dimitrios F.

AU - Papalou, Angeliki

AU - Asteris, Panagiotis G.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Computer Science (all)

KW - Materials Testing

KW - Mathematics (all)

KW - Neural Networks (Computer)

KW - Neuroscience (all)

UR - http://hdl.handle.net/10447/223003

UR - http://www.hindawi.com/journals/cin

M3 - Article

VL - 2016

SP - 1

EP - 12

JO - Computational Intelligence and Neuroscience

JF - Computational Intelligence and Neuroscience

SN - 1687-5265

ER -