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1.华东交通大学土木建筑学院,江西 南昌 330013
2.天津大学建筑工程学院,天津 300354
Received:14 November 2024,
Revised:2025-02-18,
Published:28 April 2025
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蒋认,师燕超,黄宏等.爆炸荷载下基于子结构的RC框架抗连续倒塌分析[J].防灾减灾工程学报,2025,45(02):271-281.
JIANG Ren,SHI Yanchao,HUANG Hong,et al.A Substructure Based Analysis of Progressive Collapse Resistance for RC Frame Structures Under Blast Loading[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(02):271-281.
蒋认,师燕超,黄宏等.爆炸荷载下基于子结构的RC框架抗连续倒塌分析[J].防灾减灾工程学报,2025,45(02):271-281. DOI: 10.13409/j.cnki.jdpme.20241114002.
JIANG Ren,SHI Yanchao,HUANG Hong,et al.A Substructure Based Analysis of Progressive Collapse Resistance for RC Frame Structures Under Blast Loading[J].Journal of Disaster Prevention and Mitigation Engineering,2025,45(02):271-281. DOI: 10.13409/j.cnki.jdpme.20241114002.
结合建筑结构爆炸灾害调查和精细化数值仿真手段,对钢筋混凝土框架结构爆炸损伤范围和连续倒塌分析计算效率进行研究。基于LS‑DYNA建立RC框架结构数值模型的正确性,提出了爆炸荷载下基于子结构的钢筋混凝土框架抗倒塌分析方法,包括从爆炸损伤区域中选取子结构和计算子结构的边界条件。通过对典型的5层钢筋混凝土框架结构与提出的子结构方法在爆炸荷载进行抗倒塌分析,比较提出的子结构方法的有效性和高效性。研究结果表明:提出的子结构方法能准确地预测给定爆炸荷载下RC框架结构的损伤范围和倒塌性能,同时子结构方法的计算时间相比较钢筋混凝土框架结构减少了72.7%。
This study investigated the blast damage range and computational efficiency of progressive collapse analysis for reinforced concrete (RC) frame structures by combining investigations of blast damage to building structures with refined numerical simulations. The validity of the RC frame structure model based on LS-DYNA was verified
and a substructure-based method for collapse resistance analysis of RC frame structure under blast loading was proposed
including selecting substructures from blast-damaged regions and properly calculating their boundary conditions. By conducting collapse resistance analysis on a typical 5-story RC frame structure under blast loading using full-structure and the proposed substructure methods
the effectiveness and efficiency of the proposed substructure method were examined. The results showed that the proposed substructure method could accurately predict the damage range and collapse behavior of RC frame structures under given blast loading
while reducing computational time by 72.7% compared with the full-structure method.
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