基于机器学习的高阻尼的OpenSees替代构成模型.
Feng Li1, Tianbo Peng1,2
1College of Civil Engineering, Tongji University, Shanghai 200092, China.
Polymers
|December 17, 2024
概括
为OpenSees开发了一种新的HDRGA材料,简化了高阻尼 (HDR) 地震隔离的复杂构成模型. 这种材料准确地模拟了地震期间的HDR性能.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 高阻尼 (HDR) 对于结构中的地震隔离至关重要,但具有复杂的机械性能.
- 现有的HDR构成模型对有限元分析的实施构成挑战.
- 之前的研究引入了一种新的HDR构成模型,该模型使用Gated Recurrent Unit (GRU) 和注意力机制.
研究的目的:
- 简化并将之前提出的HDR构成模型应用于OpenSees有限元分析平台.
- 在OpenSees中开发一个用户友好的HDR材料 (HDRGA),用于地震工程模拟.
- 为了验证开发的HDRGA材料的准确性和性能.
主要方法:
- 简化基于GRU注意力的HDR构成模型.
- 将简化模型转换为OpenSees的单轴材料格式.
- 为OpenSees.开发HDRGA材料代码 (标题和源文件) 的开发.
- 使用双节点模型和单节点模型进行验证,并对测试和模拟结果进行比较分析.
主要成果:
- 在OpenSees.es中成功开发和实施HDRGA材料.
- 这种HDRGA材料准确地描述了HDR的机械特性.
- 验证研究证实了该材料在地震负载条件下表现良好的能力.
- 对比分析显示,HDRGA材料模拟与实验/计算结果之间的一致性很好.
结论:
- 开发的HDRGA材料为模拟OpenSees内的地震隔离应用中的HDR行为提供了有效的工具.
- 实施方便在实际工程模拟中使用先进的构成模型.
- 该HDRGA材料在结合HDR组件的结构的地震分析方面表现出可靠的性能.
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