Monitoring, modeling, and hybrid simulation—an integrated Bayesian-based approach to high-fidelity fragility analysis

Li, Jian

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https://hdl.handle.net/2142/44294 Copy

Description

Title

Monitoring, modeling, and hybrid simulation—an integrated Bayesian-based approach to high-fidelity fragility analysis

Author(s)

Li, Jian

Issue Date

2013-05-24T22:06:50Z

Director of Research (if dissertation) or Advisor (if thesis)

Spencer, Billie F., Jr.

Doctoral Committee Chair(s)

Spencer, Billie F., Jr.

Committee Member(s)

• Elnashai, Amr S.
• Agha, Gul A.
• Song, Junho

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Fragility analysis
• Structural Health Monitoring
• Wireless Smart Sensor
• Time Synchronization
• Hybrid simulation
• Multiple-Support Excitation
• System identification
• Output decoupling
• Seismic risk assessment

Abstract

Fragility functions are one of the key technical ingredients in seismic risk assessment. The derivation of fragility functions has been extensively studied in the past; however, large uncertainties still exist, mainly due to limited collaboration between the interdependent components involved in the course of fragility estimation. This research aims to develop a systematic Bayesian-based framework to estimate high-fidelity fragility functions by integrating monitoring, modeling, and hybrid simulation, with the final goal of improving the accuracy of seismic risk assessment to support both pre- and post-disaster decision-making. In particular, this research addresses the following five aspects of the problem: (1) monitoring with wireless smart sensor networks to facilitate efficient and accurate pre- and post-disaster data collection, (2) new modeling techniques including innovative system identification strategies and model updating to enable accurate structural modeling, (3) hybrid simulation as an advanced numerical-experimental simulation tool to generate highly realistic and accurate response data for structures subject to earthquakes, (4) Bayesian-updating as a systematic way of incorporating hybrid simulation data to generate composite fragility functions with higher fidelity, and 5) the implementation of an integrated fragility analysis approach as a part of a seismic risk assessment framework. This research not only delivers an extensible and scalable framework for high-fidelity fragility analysis and reliable seismic risk assessment, but also provides advances in wireless smart sensor networks, system identification, and pseudo-dynamic testing in civil engineering applications.

Graduation Semester

2013-05

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http://hdl.handle.net/2142/44294

Copyright and License Information

Copyright 2013 Jian Li

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