...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Earthquake Engineering & Structural Dynamics >Generalized ground motion prediction model using hybrid recurrent neural network
【24h】

Generalized ground motion prediction model using hybrid recurrent neural network

机译：杂交经常性神经网络的广义地面运动预测模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Modern seismic hazard analysis utilizes various ground motion prediction equations (GMPEs) to estimate intensity measures (IMs) such as spectral acceleration (S-a), Arias intensity (I-a), significant duration (D5-95), and cumulative absolute velocity (CAV) as scalar parameters. The use of these GMPEs leads to independent estimations of IMs for the same earthquake scenario. Since the IMs belonging to the same earthquake scenario are naturally correlated, the GMPEs used for characterization of seismic hazard are expected to incorporate such correlation structure. Particularly, S-a corresponding to different periods of a single-degree-of-freedom oscillator is estimated by GMPEs that do not explicitly account for internal correlations between the S-a at various periods for the same ground motion. The current approach to incorporate such a correlation for S-a spectrum is through a post-processing technique developed by Baker and Jayaram; their method relates the spectral accelerations of different periods using functional forms describing linear correlations. However, the proposed correlation functions can be further improved and extended to more accurately estimate the spectrum of near-fault high-magnitude ground motions using high order dependencies among the spectral accelerations. This study proposes a generalized ground motion prediction model (GGMPM) using a hybrid recurrent neural network (RNN) framework that can be used to estimate a 29 x 1 correlated vector (denoted as IM) of RotD50 S-a at 26 periods and geometric means of I-a, D5-95, and CAV using a set of seismic source and site parameters as inputs. This is an improvement to the current body of knowledge because the high order dependencies between the individual components of IM are incorporated. A RNN framework is developed that estimates the median vector of IM. The discrepancy between the IM estimated using the RNN framework and the IM computed from recorded motions is further minimized using the covariance matrix adaptation evolution strategy (CMA-ES), which is a non-convex optimization method. The residuals of the RNN framework are used to construct the inter-event and the intra-event covariance matrices to account for the inter-event and intra-event variabilities of the ground motions. Hence, given the source and site parameters, the RNN-framework returns a median prediction of the IM, which is then combined with estimated inter-event and intra-event covariance matrices to obtain the probabilistic estimation of IM. Furthermore, this GGMPM framework is compared against various currently used GMPEs, and the results of these comparisons demonstrate that the proposed GGMPM leads to improved predictions while maintaining the internal dependencies of the IM components.
机译：现代地震危害分析利用各种地面运动预测方程（GMPE）来估计强度测量（IMS），例如光谱加速度（SA），ARIAS强度（IA），显着的持续时间（D5-95）和累积绝对速度（CAV）。标量参数。使用这些GMPES导致对同一地震场景的IMS的独立估计。由于属于同一地震场景的IMS自然相关，因此预期用于地震危害表征的GMPES将包括这种相关性结构。特别地，对应于单级自由度振荡器的不同时段的S-A通过GMPE估计，所述GMPE在不同的地面运动的各个时段处没有明确地解释S-A之间的内部相关性。掺入S-A频谱的这种相关方法的方法是通过Baker和Jayaram开发的后处理技术;它们的方法利用描述线性相关性的功能形式涉及不同时段的光谱加速度。然而，所提出的相关函数可以进一步改善和扩展到使用高阶依赖性在光谱加速度之间更准确地估计近故障高幅度接地运动的频谱。本研究提出了一种使用混合复发性神经网络（RNN）框架的广义地面运动预测模型（GGMPM），其可用于在26个周期和Ia的几何手段中估计Rotd50 SA的29×1相关的向量（表示为IM） ，D5-95和Cav使用一组地震源和站点参数作为输入。这是对当前知识体系的改进，因为IM的各个组件之间的高阶依赖性被纳入。开发了RNN框架，估计IM的中位数矢量。使用RNN框架估计的IM之间的差异和从记录的动作计算的IM，使用协方差矩阵自适应演化策略（CMA-ES）进一步最小化，这是非凸优化方法。 RNN框架的残差用于构建事件帧间和事件协方差矩阵，以考虑地面运动的帧间活动和事件内变形性。因此，鉴于源和站点参数，RNN框架返回IM的中值预测，然后与估计的事件间和事件协方差矩阵组合以获得IM的概率估计。此外，将该GGMPM框架与各种当前使用的GMPE进行进行比较，这些比较的结果表明，所提出的GGMPM导致改进的预测，同时保持IM组件的内部依赖性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号