超越海脆弱性函数:对建筑损坏估计的实验评估
Ruben Vescovo1, Bruno Adriano2, Erick Mas2
1Department of Civil and Environmental Engineering, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai, 980-8572, Japan.
Scientific reports
|August 31, 2023
概括
海脆弱性函数 (TFF) 显示了对二进制损害估计的潜力,但与多个类进行斗争. 一种新的机器学习方法证明了海建筑损害评估的更好概括性.
科学领域:
- 灾难科学 灾难科学
- 计算统计的计算统计.
- 机器学习是机器学习.
背景情况:
- 海脆弱性函数 (TFF) 是统计模型,将海强度与建筑损坏概率相关联.
- 研究正在从统计估计器转向灾难科学中的深度学习.
- 对于灾难信号来说,TFF很有价值,但很少用于直接损害估计.
研究的目的:
- 调查TFF适用于建筑损坏估计的适用性.
- 将TFF应用方法与一种新的机器学习方法进行比较.
- 评估对域外数据集的模型概括.
主要方法:
- 选择了三个TFF和两个应用方法来进行基线损害估计.
- 开发了一种机器学习方法,用于训练TFF强度测量之外的物理参数.
- 在二进制和多类场景中对2011年伊希诺玛基数据集 (东日本大地震和海) 进行了测试.
主要成果:
- 无论是TFF方法还是拟议的机器学习模型,都取得了良好的二进制损害估计结果.
- 在多类场景和域外概括方面,TFF方法表现出局限性.
- 这种新的机器学习方法展示了优越的概括能力.
结论:
- TFF为二进制海损害评估提供了一个基线.
- 在扩展的物理参数上训练的简单机器学习模型显示了海引起的建筑损坏的改进概括.
- 进一步的研究应侧重于加强对各种海事件的损害估计模型的概括性.
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