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Experimental testing and machine learning to predict the load-slip behavior of stud connectors in steel-UHPC composite structures

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成果类型:
期刊论文
作者:
Zhou, Yong-jian;Yang, Xia;Zou, Wu-wu;Han, Cheng-shuo
通讯作者:
Yang, X
作者机构:
[Yang, Xia; Zhou, Yong-jian; Yang, X; Han, Cheng-shuo; Zou, Wu-wu] Yangtze Univ, Sch Urban Construct, Jingzhou 434023, Peoples R China.
[Yang, Xia] Wuhan Polytech Univ, Sch Civil Engn & Architecture, Wuhan 430023, Peoples R China.
通讯机构:
[Yang, X ] Y
Yangtze Univ, Sch Urban Construct, Jingzhou 434023, Peoples R China.
语种:
英文
关键词:
Steel-UHPC composite structures;Stud connectors;Machine learning;Load-slip curve prediction;Push-out test
期刊:
Engineering Structures
ISSN:
0141-0296
年:
2025
卷:
335
页码:
120418
基金类别:
Natural Science Foundation of Hubei Province, China [2023AFB391]
机构署名:
本校为其他机构
院系归属:
土木工程与建筑学院
摘要:
Machine learning techniques have demonstrated significant potential in predicting the shear mechanical performance of stud connectors. However, predicting the load-slip curves of stud connectors in steel-ultrahigh-performance concrete (UHPC) composite structures remains a challenge. While some empirical models have been developed to describe the load-slip behavior of stud connectors, most are fitted to limited databases, leading to inadequate generalizability. This study presents a series of push-out tests on stud connectors encased in steel-UH...

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