通过DNN方法更新预制高层建筑的高效模型
Chunqing Liu1, Fengliang Zhang1, Yanchun Ni2,3
1School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
一种新的深度神经网络 (DNN) 模型更新方法通过直接使用模拟分析和模拟数据来简化结构分析. 这种高效的方法实现了高精度 (MAC>0.99) 并揭示了预制隔壁.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 在数值模型中,对精度的需求日益增加,需要先进的模型更新技术.
- 新兴的基于深度学习的方法通常涉及复杂的架构和数学过程.
- 预制隔墙对结构影响的研究有限.
研究的目的:
- 通过使用深度神经网络 (DNN) 引入一种创新和简化的模型更新方法.
- 为了研究预制隔墙对建筑物刚性的影响.
- 以极简的DNN架构实现准确和高效的模型更新.
主要方法:
- 开发了一种使用深度神经网络 (DNN) 的新型模型更新方法.
- 对于DNN的输入数据直接包括模态分析和数值模型模拟的结果.
- 使用简约的DNN架构,绕过复杂的数学计算.
- 研究了预制隔墙相对于剪切墙的硬度贡献.
主要成果:
- 与实验测量相比,DNN模型更新方法在第一和第二顺序模式中实现了超过0.99的模式保证标准 (MAC) 值.
- 预制隔墙的硬度比率大约为0.2-0.3,相对于相同材料和厚度的剪切墙.
结论:
- 提出的基于DNN的模型更新方法为传统方法提供了一个高度准确和高效的替代方案.
- 该研究强调了预制隔墙对建筑物整体刚性的重要,但经常被忽视的贡献.
- 这项研究验证了DNN在结构模型更新和分析中的有效性.
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