Application of multi-hybrid metaheuristic algorithm on prediction of split-tensile strength of shear connectors

IF 2.1 3区 工程技术 Q2 ENGINEERING, CIVIL
Chao Liu, Y. Zandi, Abouzar Rahimi, Yongli Peng, G. Ge, M. Khadimallah, A. Issakhov, Subbotina Tatyana Yu
{"title":"Application of multi-hybrid metaheuristic algorithm on prediction of split-tensile strength of shear connectors","authors":"Chao Liu, Y. Zandi, Abouzar Rahimi, Yongli Peng, G. Ge, M. Khadimallah, A. Issakhov, Subbotina Tatyana Yu","doi":"10.12989/SSS.2021.28.2.167","DOIUrl":null,"url":null,"abstract":"Shear connectors play a major role in the development of composite steel concrete systems. The behavior of shear connectors is usually calculated by push-out measurements. These experiments are expensive and take a lot of time. Soft Computation (SC) may be applied as an additional solution to remove the need for push-out testing. The objective of the research is to explore the implementation, as sub-branches of the SC approaches, of artificial intelligence (AI) techniques for the prediction of advanced C-shaped shear connectors. To this end, multiple push-out tests on these connectors will be carried out and the requisite data is obtained for the AI models. The Grey Wolf Optimizer algorithm (GWO) is built to define the parameters that influence the shear strength of angle connectors. Two regression metrics as determination coefficient (R2) and root mean square (RMSE) were used to measure the results of model. Furthermore, only four parameters in the predictive models are sufficient to provide an extremely precise prediction. It was found that GWO is a faster method and is able to achieve marginally higher output indices than in experiments.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Structures and Systems","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.12989/SSS.2021.28.2.167","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 2

Abstract

Shear connectors play a major role in the development of composite steel concrete systems. The behavior of shear connectors is usually calculated by push-out measurements. These experiments are expensive and take a lot of time. Soft Computation (SC) may be applied as an additional solution to remove the need for push-out testing. The objective of the research is to explore the implementation, as sub-branches of the SC approaches, of artificial intelligence (AI) techniques for the prediction of advanced C-shaped shear connectors. To this end, multiple push-out tests on these connectors will be carried out and the requisite data is obtained for the AI models. The Grey Wolf Optimizer algorithm (GWO) is built to define the parameters that influence the shear strength of angle connectors. Two regression metrics as determination coefficient (R2) and root mean square (RMSE) were used to measure the results of model. Furthermore, only four parameters in the predictive models are sufficient to provide an extremely precise prediction. It was found that GWO is a faster method and is able to achieve marginally higher output indices than in experiments.
多混合元启发式算法在抗剪连接件劈拉强度预测中的应用
抗剪连接件在钢-混凝土复合体系的发展中发挥着重要作用。抗剪连接件的性能通常通过推出测量来计算。这些实验既昂贵又耗时。软计算(SC)可以作为一种额外的解决方案来应用,以消除推出测试的需要。本研究的目的是探索人工智能(AI)技术作为SC方法的分支,用于预测先进的C形抗剪连接件。为此,将对这些连接器进行多次推出测试,并获得人工智能模型所需的数据。Grey-Wolf Optimizer算法(GWO)用于定义影响角连接件抗剪强度的参数。采用确定系数(R2)和均方根(RMSE)两个回归指标来衡量模型的结果。此外,预测模型中只有四个参数就足以提供极其精确的预测。研究发现,GWO是一种更快的方法,并且能够实现比实验略高的输出指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Smart Structures and Systems
Smart Structures and Systems 工程技术-工程:机械
CiteScore
6.50
自引率
8.60%
发文量
0
审稿时长
9 months
期刊介绍: An International Journal of Mechatronics, Sensors, Monitoring, Control, Diagnosis, and Management airns at providing a major publication channel for researchers in the general area of smart structures and systems. Typical subjects considered by the journal include: Sensors/Actuators(Materials/devices/ informatics/networking) Structural Health Monitoring and Control Diagnosis/Prognosis Life Cycle Engineering(planning/design/ maintenance/renewal) and related areas.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信