Bio-based laminated truss structures with bolted steel connections: Experiment, modeling, and model-updating

被引:4
|
作者
Shi, Da [1 ,2 ]
Xu, Yongjia [2 ,3 ]
Demartino, Cristoforo [2 ,3 ]
Xiao, Yan [2 ,3 ]
Spencer, Billie F. [2 ,3 ]
机构
[1] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou, Zhejiang, Peoples R China
[2] Zhejiang Univ, Univ Illinois Urbana Champaign Inst, Haining 314400, Zhejiang, Peoples R China
[3] Univ Illinois, Dept Civil & Environm Engn, Urbana, IL USA
来源
关键词
bio-based materials; bolted connections; Glubam; hybrid planar truss; Laminated Veneer Lumber (LVL); model updating; FINITE-ELEMENT MODEL; TIMBER; IDENTIFICATION; BAMBOO; JOINT; SHEAR;
D O I
10.1002/eqe.4043
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Bio-based laminated structures offer a timely solution to meet the pressing demand for resource-efficient and environmentally responsible construction practices in civil engineering. Bolted connections are commonly used in truss joints and significantly impact their global mechanical behaviors. However, existing studies on the complex behavior of joints for integrated bio-based structures are lacking. Based on previous work by the authors about joint behavior, this study seeks to experimentally investigate the axial behavior of bolted steel to laminated timber and bamboo (glubam) connections with slotted-in steel plates in planar truss structures. First, cyclic tests were conducted on Bio-based laminated planar truss structures to evaluate their global and local hysteretic behavior. The test matrix encompasses eight distinct connection configurations, carefully selected to assess the connection behavior of Laminated Veneer Lumber (LVL) and Glued Laminated Bamboo (glubam). Subsequently, through the incorporation of the hysteretic model for truss joints, an advanced hybrid truss model was developed within the OpenSees framework and successfully validated using experimental data. Finally, this study proposes and demonstrates the effectiveness of an efficient model-updating framework, which combines the power of the genetic algorithm (GA) and neural network, resulting in a significantly enhanced accuracy of the model.
引用
收藏
页码:739 / 755
页数:17
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