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基于人工神经网络的填充墙钢筋混凝土框架结构基本周期预测

Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks.

作者信息

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

机构信息

Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, Greece.

Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo, Viale delle Scienze, 90128 Palermo, Italy.

出版信息

Comput Intell Neurosci. 2016;2016:5104907. doi: 10.1155/2016/5104907. Epub 2015 Dec 28.

DOI:10.1155/2016/5104907
PMID:27066069
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4809391/
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.

摘要

基本周期是结构抗震设计中最关键的参数之一。有几种文献方法可用于估计基本周期，但这些方法往往相互冲突，其适用性存疑。此外，尽管填充墙会增加结构的刚度和质量，从而导致基本周期发生显著变化，但这些方法中的大多数都没有考虑结构中填充墙的存在。在本文中，人工神经网络（ANN）被用于预测填充钢筋混凝土（RC）结构的基本周期。为了对人工神经网络进行训练和验证，基于对影响钢筋混凝土结构基本周期的参数进行详细研究，使用了一个大数据集。将预测值与分析值进行比较，结果表明，考虑到影响其值的关键参数，利用人工神经网络预测填充钢筋混凝土框架结构的基本周期具有潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/4724791d2bff/CIN2016-5104907.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/86bb089bc100/CIN2016-5104907.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/65362e2412cd/CIN2016-5104907.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/b9d723f70004/CIN2016-5104907.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/2a8bf4846a0b/CIN2016-5104907.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/6a700182dce9/CIN2016-5104907.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/f51b547dbff3/CIN2016-5104907.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/0d9fb7b91f54/CIN2016-5104907.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/a8474296708f/CIN2016-5104907.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/4724791d2bff/CIN2016-5104907.013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/86bb089bc100/CIN2016-5104907.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/9689d7dcd752/CIN2016-5104907.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/893bc74c4d9c/CIN2016-5104907.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/f6ef5dc16582/CIN2016-5104907.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/80f01873654f/CIN2016-5104907.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/65362e2412cd/CIN2016-5104907.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/b9d723f70004/CIN2016-5104907.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/2a8bf4846a0b/CIN2016-5104907.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/6a700182dce9/CIN2016-5104907.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/f51b547dbff3/CIN2016-5104907.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/0d9fb7b91f54/CIN2016-5104907.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/a8474296708f/CIN2016-5104907.012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60dd/4809391/4724791d2bff/CIN2016-5104907.013.jpg

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