模拟地震危险安全评估的地面加速峰值
Fatima Khalid1, Milad Razbin2,3
1Department of Civil Engineering, NED University of Engineering and Technology, Karachi, Pakistan. fatimak@neduet.edu.pk.
Scientific reports
|December 27, 2024
概括
这项研究引入了一个人工神经网络 (ANN) 模型,增强了基因算法 (GA),用于预测地震地面运动 (GMP). 该ANN-GA模型准确预测地震事件,改善基础设施的地震危险安全评估.
科学领域:
- 地质物理学 地质物理学
- 地震学 地震学
- 计算科学 计算科学
背景情况:
- 准确的地面运动预测 (GMP) 对于地震危险评估至关重要.
- 现有的模型需要加强,以改善地震预测.
- 浅地震对基础设施和城市规划构成重大风险.
研究的目的:
- 开发和验证一个人工神经网络 (ANN) 模型,用于预测顶峰地面加速 (PGA).
- 将基因算法 (GA) 与ANN集成,以提高预测准确度.
- 通过先进的地震预测,加强对地震危险的安全评估.
主要方法:
- 使用一种人工神经网络 (ANN) 架构,具有特定的输入 (时刻大小,故障类型,距离,土壤类型) 和输出 (PGA) 变量.
- 在太平洋工程研究中心使用885对数据对进行了ANN模型的训练和验证.
- 集成了一种遗传算法 (GA) 来优化ANN并预测20个潜在的地震场景.
主要成果:
- 该ANN模型架构包括4个输入节点,2个隐藏层,每层25个节点,1个输出节点.
- 在20个测试场景中,ANN-GA模型成功预测了15个场景中的地震发生情况.
- 该模型在准确预测地震事件方面显示出显著的潜力.
结论:
- 开发的ANN-GA模型显示了准确地预测地震事件的巨大潜力.
- 这种预测能力可以为弹性基础设施的发展做出重大贡献.
- 研究结果支持对地震易发地区制定更为明智的城市规划策略.
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