ABSTRACT

The velocity pulses produced by forward-directivity effects in the near-fault regions can have destructive effects on structures. Proper estimation of the duration of such velocity pulses is an essential step in the near-fault seismic hazard analysis and mitigating potential damage. In this study, the effects of different source, path, source-to-site geometry, and local site parameters on the duration of directivity pulse (T_p) are investigated based on the mutual information (MI) concept. A dataset of near-fault pulse-like ground motions from the NGA-West2 database including 135 observations from 17 strike-slip events and 14 non-strike-slip events is utilized for the purpose of this study. The selected ground motion variables are the magnitude, hypocentral distance, depth, D and V_S30 that are further applied in an artificial neural network (ANN) to predict T_p. The ANN estimates are verified by support vector regression (SVR) as one of the most efficient machine learning algorithms. High correlation between observations and predictions and low error functions reveals the good predictive ability of both ANN and SVR for estimating directivity pulse period. The predictions made by ANN and SVR are further compared with those provided by the empirical and physical models.

摘要

近断层区域的前向性效应产生的速度脉冲会对结构产生破坏性影响。正确估计这种速度脉冲的持续时间是近断层地震危险分析和减轻潜在损害的重要步骤。在这项研究中，基于互信息 (MI) 概念_，研究了不同源、路径、源到站点几何形状和局部站点参数对方向性脉冲 (Tp) 持续时间的影响。来自 NGA-West2 数据库的近断层脉冲状地面运动数据集包括来自 17 个走滑事件和 14 个非走滑事件的 135 个观测值，用于本研究的目的。选定的地震动变量是震级、震源距离、深度、D 和 V _S30进一步应用于人工神经网络 (ANN) 以预测T _p。作为最有效的机器学习算法之一，支持向量回归 (SVR) 验证了 ANN 估计。观测和预测之间的高相关性和低误差函数表明 ANN 和 SVR 在估计方向性脉冲周期方面具有良好的预测能力。ANN 和 SVR 的预测进一步与经验和物理模型提供的预测进行比较。