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New Applications That Come From Exte...
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Cheng, Ming Hei.
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New Applications That Come From Extending Seismic Networks Into Buildings.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
New Applications That Come From Extending Seismic Networks Into Buildings./
作者:
Cheng, Ming Hei.
面頁冊數:
135 p.
附註:
Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.
Contained By:
Dissertation Abstracts International75-08B(E).
標題:
Civil engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3615109
ISBN:
9781303808920
New Applications That Come From Extending Seismic Networks Into Buildings.
Cheng, Ming Hei.
New Applications That Come From Extending Seismic Networks Into Buildings.
- 135 p.
Source: Dissertation Abstracts International, Volume: 75-08(E), Section: B.
Thesis (Ph.D.)--California Institute of Technology, 2014.
This item must not be sold to any third party vendors.
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.
ISBN: 9781303808920Subjects--Topical Terms:
860360
Civil engineering.
New Applications That Come From Extending Seismic Networks Into Buildings.
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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.
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