• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

循环加载下钢筋混凝土剪力墙混凝土本构模型的有效预测

Effective Prediction of Concrete Constitutive Models for Reinforced Concrete Shear Walls under Cyclic Loading.

作者信息

To Quoc Bao, Shin Jiuk, Kim Sung Jig, Kim Hye-Won, Lee Kihak

机构信息

Deep Learning Architecture Research Center, Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea.

Department of Architectural Engineering, Gyeongsang National University, Jinju 52828, Republic of Korea.

出版信息

Materials (Basel). 2024 Apr 18;17(8):1877. doi: 10.3390/ma17081877.

DOI:10.3390/ma17081877
PMID:38673233
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11051482/
Abstract

One of the most challenging elements of modeling the behaviour of reinforced concrete (RC) walls is combining realistic material models that can capture the observable behaviour of the physical system. Experiments with realistic loading rates and pressures reveal that steel and concrete display complicated nonlinear behaviour that is challenging to represent in a single constitutive model. To investigate the response of a reinforced concrete structure subjected to dynamic loads, this paper's study is based on many different material models to assess the advantages and disadvantages of the models on 2D and 3D RC walls using the LS-DYNA program. The models consisted of the KCC model and the CDP model, which represented plasticity and distinct tensile/compressive damage models, and the Winfrith model, which represented plasticity and the smeared crack model. Subsequently, the models' performances were assessed by comparing them to experimental data from reinforced concrete structures, in order to validate the accuracy of the overall behaviour prediction. The Winfrith model demonstrated satisfactory results in predicting the behaviour of 2D and 3D walls, including maximum strength, stiffness deterioration, and energy dissipation. The method accurately predicted the maximum strength of the Winfrith concrete model for the 2D wall with an error of 9.24% and for the 3D wall with errors of 3.28% in the X direction and 5.02% in the Y direction. The Winfrith model demonstrated higher precision in predicting dissipation energy for the 3D wall in both the X and Y directions, with errors of 6.84% and 6.62%, correspondingly. Additional parametric analyses were carried out to investigate structural behaviour, taking into account variables such as concrete strength, strain rate, mesh size, and the influence of the element type.

摘要

对钢筋混凝土(RC)墙的行为进行建模,最具挑战性的因素之一是结合能够捕捉物理系统可观测行为的逼真材料模型。对实际加载速率和压力进行的实验表明,钢材和混凝土呈现出复杂的非线性行为,这使得在单一本构模型中表示这种行为具有挑战性。为了研究钢筋混凝土结构在动态荷载作用下的响应,本文的研究基于许多不同的材料模型,使用LS-DYNA程序评估这些模型在二维和三维RC墙上的优缺点。这些模型包括代表塑性和不同拉伸/压缩损伤模型的KCC模型和CDP模型,以及代表塑性和弥散裂缝模型的温弗里思模型。随后,通过将这些模型与钢筋混凝土结构的实验数据进行比较来评估模型的性能,以验证整体行为预测的准确性。温弗里思模型在预测二维和三维墙的行为方面表现出令人满意的结果,包括最大强度、刚度退化和能量耗散。该方法准确预测了二维墙温弗里思混凝土模型的最大强度,误差为9.24%,对于三维墙,在X方向的误差为3.28%,在Y方向的误差为5.02%。温弗里思模型在预测三维墙X和Y方向的耗能方面表现出更高的精度,相应误差分别为6.84%和6.62%。还进行了额外的参数分析,以研究结构行为,考虑了混凝土强度、应变率、网格尺寸和单元类型的影响等变量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/68ca915ec290/materials-17-01877-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b3c0df07de7b/materials-17-01877-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b2ac6af84db2/materials-17-01877-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/3decb84eba14/materials-17-01877-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/910dab0296af/materials-17-01877-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/1641924beea3/materials-17-01877-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b2ccd29b5869/materials-17-01877-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/8e46ceccdf11/materials-17-01877-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/0b1f237cc8aa/materials-17-01877-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/e3b8d51745f6/materials-17-01877-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/7124a554f0d1/materials-17-01877-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/9ff6565e7a35/materials-17-01877-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/7a46f0211666/materials-17-01877-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/0842c9495550/materials-17-01877-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/99800befc9a5/materials-17-01877-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/e129c23646d5/materials-17-01877-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/f469d54cb53f/materials-17-01877-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/1ed3c2e531f7/materials-17-01877-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/572c1cc77435/materials-17-01877-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/dd685c94aefe/materials-17-01877-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/18f6606b1372/materials-17-01877-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b9c413e31839/materials-17-01877-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/93158a24e84d/materials-17-01877-g022a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/68ca915ec290/materials-17-01877-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b3c0df07de7b/materials-17-01877-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b2ac6af84db2/materials-17-01877-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/3decb84eba14/materials-17-01877-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/910dab0296af/materials-17-01877-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/1641924beea3/materials-17-01877-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b2ccd29b5869/materials-17-01877-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/8e46ceccdf11/materials-17-01877-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/0b1f237cc8aa/materials-17-01877-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/e3b8d51745f6/materials-17-01877-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/7124a554f0d1/materials-17-01877-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/9ff6565e7a35/materials-17-01877-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/7a46f0211666/materials-17-01877-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/0842c9495550/materials-17-01877-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/99800befc9a5/materials-17-01877-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/e129c23646d5/materials-17-01877-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/f469d54cb53f/materials-17-01877-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/1ed3c2e531f7/materials-17-01877-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/572c1cc77435/materials-17-01877-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/dd685c94aefe/materials-17-01877-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/18f6606b1372/materials-17-01877-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/b9c413e31839/materials-17-01877-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/93158a24e84d/materials-17-01877-g022a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/985f/11051482/68ca915ec290/materials-17-01877-g023.jpg

相似文献

1
Effective Prediction of Concrete Constitutive Models for Reinforced Concrete Shear Walls under Cyclic Loading.循环加载下钢筋混凝土剪力墙混凝土本构模型的有效预测
Materials (Basel). 2024 Apr 18;17(8):1877. doi: 10.3390/ma17081877.
2
Effect of Steel Fibers on the Hysteretic Performance of Concrete Beams with Steel Reinforcement-Tests and Analysis.钢纤维对配筋混凝土梁滞回性能的影响——试验与分析
Materials (Basel). 2020 Jun 29;13(13):2923. doi: 10.3390/ma13132923.
3
Cyclic Response of Steel Fiber Reinforced Concrete Slender Beams; an Experimental Study.钢纤维增强混凝土细长梁的循环响应;一项实验研究。
Materials (Basel). 2019 Apr 29;12(9):1398. doi: 10.3390/ma12091398.
4
Analysis of Residual Flexural Stiffness of Steel Fiber-Reinforced Concrete Beams with Steel Reinforcement.配有钢筋的钢纤维增强混凝土梁的残余抗弯刚度分析
Materials (Basel). 2020 Jun 13;13(12):2698. doi: 10.3390/ma13122698.
5
Seismic Analysis and Design of Composite Shear Wall with Stiffened Steel Plate and Infilled Concrete.带加劲钢板和填充混凝土组合剪力墙的抗震分析与设计
Materials (Basel). 2021 Dec 27;15(1):182. doi: 10.3390/ma15010182.
6
Modeling and Simulation of the Hysteretic Behavior of Concrete under Cyclic Tension-Compression Using the Smeared Crack Approach.基于弥散裂缝模型的混凝土在反复拉压作用下滞回性能的建模与仿真
Materials (Basel). 2023 Jun 17;16(12):4442. doi: 10.3390/ma16124442.
7
A Practical Finite Element Modeling Strategy to Capture Cracking and Crushing Behavior of Reinforced Concrete Structures.一种用于捕捉钢筋混凝土结构开裂和破碎行为的实用有限元建模策略。
Materials (Basel). 2021 Jan 21;14(3):506. doi: 10.3390/ma14030506.
8
Improved Shear Strength Prediction Model of Steel Fiber Reinforced Concrete Beams by Adopting Gene Expression Programming.采用基因表达式编程改进钢纤维增强混凝土梁抗剪强度预测模型
Materials (Basel). 2022 May 24;15(11):3758. doi: 10.3390/ma15113758.
9
Behaviour of Hybrid Steel and FRP-Reinforced Concrete-ECC Composite Columns under Reversed Cyclic Loading.反复循环荷载下混杂钢与 FRP 增强混凝土-ECC 组合柱的性能。
Sensors (Basel). 2018 Dec 2;18(12):4231. doi: 10.3390/s18124231.
10
Experimental Research and Numerical Analysis of CFRP Retrofitted Masonry Triplets under Shear Loading.CFRP加固砌体三联体在剪切荷载作用下的试验研究与数值分析
Polymers (Basel). 2022 Sep 6;14(18):3707. doi: 10.3390/polym14183707.