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Title:Rapid post-earthquake safety assessment system for buildings using sparse acceleration measurements
Author(s):Tsuchimoto, Koji
Director of Research:Spencer, Jr., Billie F.
Doctoral Committee Chair(s):Spencer, Jr., Billie F.
Doctoral Committee Member(s):Bergman, Lawrence A.; Fahnestock, Larry A.; Wada, Akira; Work, Daniel
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Rapid safety assessment
post-earthquake
sparse acceleration measurements
damage index
maximum inter-story drift angle
safety classification
damage-sensitive features
convolutional neural network
parametric model updating
nonparametric system identification
experimental validation
cost-benefit analysis
Abstract:After a major seismic event, building safety should be assessed by qualified experts prior to reoccupation and resumption of operations. Such inspections are generally performed by teams of two or more experts and are time consuming, labor intensive, subjective in nature, and potentially put the lives of inspectors in danger. Structural Health Monitoring (SHM) provides the potential to accelerate the required amount of evaluation. The goal of the research proposed herein is to develop a cost-effective approach for rapid safety assessment of buildings after seismic events by using sparse acceleration measurements. First, the problem definition of the rapid safety assessment system is discussed, and an appropriate performance measure is developed. Herein, the maximum inter-story drift angle (IDA) is considered as a reliable damage index (DI) to classify the safety of buildings after seismic events occur. A damage sensitive feature (DSF) is then defined by a comparison between linear and nonlinear responses that can be used to assess the condition of buildings. Condition assessment using a convolutional neural network (CNN) is employed to uncover the complex relationship between DSFs and the damage index. Issues associated with full-scale implementation are also addressed. First, validation of the proposed approach is conducted using a numerical model of a five-story steel building. A three-dimensional (3D) nonlinear analysis model is created and subjected to random ground motion. Because the use of the 3D analysis model is too computationally expensive to generate the required training data, a simplified nonlinear model is developed for this purpose, along with a corresponding linear model. The training data for the CNN incorporates uncertainties in both the analysis model and ground motion. While the numerical results confirmed the potential of the proposed approach, experimental validation on large-scale structures is required for field implementation. Moreover, an extension to the assessment of high-rise buildings, such as those used for residences in many modern cities, is desired. To this end, data for a 1/3-scale, 18-story experimental steel building that was tested on the shaking table at E-Defense in Japan is employed. The importance of online model updating of the linear analysis model to calculate corresponding DSFs during the operation is also discussed. Experimental results confirm the efficacy of the proposed approach for rapid post-earthquake safety assessment for high-rise buildings. Finally, a cost-benefit analysis with respect to the number of sensors used is presented. This research demonstrates the efficacy of the proposed approach for rapid post-earthquake safety assessment of buildings, using sparse acceleration measurements.
Issue Date:2021-12-01
Type:Thesis
URI:http://hdl.handle.net/2142/113973
Rights Information:Copyright 2021 Koji Tsuchimoto
Date Available in IDEALS:2022-04-29
Date Deposited:2021-12


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