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New applications that come from extending seismic networks into buildings

机译:将地震网络扩展到建筑物中的新应用

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摘要

This thesis describes engineering applications that come from extending seismic networks into building structures. The proposed applications will benefit the data from the newly developed crowd-sourced seismic networks which are composed of low-cost accelerometers. An overview of the Community Seismic Network and the earthquake detection method are addressed. In the structural array components of crowd-sourced seismic networks, there may be instances in which a single seismometer is the only data source that is available from a building. A simple prismatic Timoshenko beam model with soil-structure interaction (SSI) is developed to approximate mode shapes of buildings using natural frequency ratios. A closed form solution with complete vibration modes is derived. In addition, a new method to rapidly estimate total displacement response of a building based on limited observational data, in some cases from a single seismometer, is presented. The total response of a building is modeled by the combination of the initial vibrating motion due to an upward traveling wave, and the subsequent motion as the low-frequency resonant mode response. Furthermore, the expected shaking intensities in tall buildings will be significantly different from that on the ground during earthquakes. Examples are included to estimate the characteristics of shaking that can be expected in mid-rise to high-rise buildings. Development of engineering applications (e.g., human comfort prediction and automated elevator control) for earthquake early warning system using probabilistic framework and statistical learning technique is addressed.
机译:本文描述了将地震网络扩展到建筑结构中的工程应用。拟议的应用程序将从新开发的,由低成本加速度计组成的人群来源地震网络中受益。概述了社区地震台网和地震检测方法。在众包地震网络的结构阵列组件中,可能存在单个地震仪是可从建筑物获得的唯一数据源的情况。建立了一个简单的具有土壤-结构相互作用(SSI)的棱柱Timoshenko梁模型,以使用自然频率比近似建筑物的模态形状。推导了具有完整振动模式的闭式解。此外,提出了一种基于有限的观测数据快速估算建筑物总位移响应的新方法,在某些情况下,该数据来自单个地震仪。建筑物的总响应是通过将向上传播的波引起的初始振动运动与随后的运动作为低频共振模式响应的组合来建模的。此外,地震期间高层建筑的预期震动强度将与地面上的震动强度明显不同。包含示例以估计中高层到高层建筑可能会发生的震动特征。解决了使用概率框架和统计学习技术开发地震预警系统的工程应用(例如,人类舒适度预测和自动电梯控制)的问题。

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    Cheng Ming Hei;

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  • 年度 2014
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