Machine learning for structural stability: Predicting dynamics responses using physics-informed neural networks

被引：6

作者：

Li, Zhonghong ^{[1
]}

Yan, Gongxing ^{[2
]}

机构：

[1] Chongqing Chem Ind Vocat Coll, Sch Architectural Engn & Art Design, Chongqing 401228, Peoples R China

[2] Luzhou Vocat & Tech Coll, Sch Intelligent Construct, Luzhou 646000, Sichuan, Peoples R China

来源：

COMPUTERS AND CONCRETE | 2022年 / 29卷 / 06期

关键词：

bi-directional FG concrete nanobeam; DSC-IM; NS; SGT; physics-informed neural networks; vibrational problem; FORCED VIBRATION CHARACTERISTICS; WALLED CARBON NANOTUBES; SPRING-MASS SYSTEMS; OF-THE-ART; FREQUENCY-CHARACTERISTICS; SENSITIVITY-ANALYSIS; BOUNDARY-CONDITIONS; BUCKLING ANALYSIS; BEHAVIOR; BEAM;

D O I：

10.12989/cac.2022.29.6.419

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This article deals with the vibrational response of a nanobeam made of bi-directional FG materials which is modeled via nonlocal strain gradient theory along with HSDT. Also, the nanobeam is placed on a Winkler-Pasternak foundation and is under axial mechanical loading. By using the variational energy method, the formulation and end conditions are obtained. Then, DSC-IM, as the numerical solution procedure is employed to extract the results. The material properties of the nanobeam are FG which varies in two directions with in exponential manner. The results from DDN are verified by using other papers. Lastly, a thorough parametric investigation is presented to investigated the effect of different parameters.

引用

页码：419 / 432

页数：14

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