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Identification of Structural Damage, Ground Motion Response, and the Benefits of Dense Seismic Instrumentation


Filippitzis, Filippos (2021) Identification of Structural Damage, Ground Motion Response, and the Benefits of Dense Seismic Instrumentation. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/x0sf-pq18.


This study explores the problems of identifying structural damage in steel frame buildings, through the use of dense instrumentation over the height of the building, and of characterizing the ground motion response in urban Los Angeles following the 2019 Ridgecrest earthquakes, through the use of dense instrumentation from available seismic networks, including the very dense Community Seismic Network.

First we explore the possibility of tracing possible nonlinear behavior of a structure by updating an equivalent linear system model in short time segments of the earthquake-induced excitation and response time histories, using a moving time window approach. The stiffness and damping related parameters of the equivalent linear model are estimated by minimizing a measure of fit between the measured and model predicted response time histories for each time window. We explore the effectiveness of the methodology for two example applications, a single-story and a six-story steel moment frame building. For the single-story building, the methodology is shown to be very effective in tracing the nonlinearities, while the six-story building is designed to also reveal the limitations of the methodology, mainly arising from the different types of model errors manifested in the formulation.

Next, we investigate the problem of structural damage identification through the use of sparse Bayesian learning (SBL) techniques. This is based on the premise that damage in a structure appears only in a limited number of locations. SBL methods that had been previously applied for structural damage identification used measurements related to modal properties and were thus limited to linear models. Here we present a methodology that allows for the application of SBL in non-linear models, using time history measurements recorded from a dense network of sensors installed along the building height. We develop a two-step optimization algorithm in which the most probable values of the structural model parameters and the hyper-parameters are iteratively obtained. An equivalent single-objective minimization problem that results in the most probable model parameter values is also derived. We consider the example problem of identifying damage in the form of weld fractures in a 15-story moment resisting steel frame building, using a nonlinear finite element model and simulated acceleration data. Fiber elements and a bilinear material model are used in order to account for the change of local stiffness when cracks at the welds are subjected to tension and the model parameters characterize the loss of stiffness as the crack opens under tension. The damage identification results demonstrate the effectiveness and robustness of the proposed methodology in identifying the existence, location, and severity of damage for a variety of different damage scenarios, and degrees of model and measurement errors. The results show the great promise of the SBL methodology for damage identification by integrating nonlinear finite element models and response time history measurements.

The final part of the thesis involves studying the ground motion response in urban Los Angeles during the two largest events (M7.1 and M6.4) of the 2019 Ridgecrest earthquake sequence using recordings from multiple regional seismic networks as well as a subset of 350 stations from the much denser Community Seismic Network. The response spectral (pseudo) accelerations for a selection of periods of engineering significance are calculated. Significant spectral acceleration amplification is present and reproducible between the two events. For the longer periods, coherent spectral acceleration patterns are visible throughout the Los Angeles Basin, while for the shorter periods, the motions are less spatially coherent. The dense Community Seismic Network instrumentation allows us to observe smaller-scale coherence even for these shorter periods. Examining possible correlations of the computed response spectral accelerations with basement depth and Vs30, we find the correlations to be stronger for the longer periods. Furthermore, we study the performance of two state-of-the-art methods for estimating ground motions for the largest event of the Ridgecrest earthquake sequence, namely 3D finite difference simulations and ground motion prediction equations. For the simulations, we are interested in the performance of the two Southern California Earthquake Center 3D Community Velocity Models (CVM-S and CVM-H). For the ground motion prediction equations, we consider four of the 2014 Next Generation Attenuation-West2 Project equations. For some cases, the methods match the observations reasonably well; however, neither approach is able to reproduce the specific locations of the maximum response spectral accelerations, or match the details of the observed amplification patterns.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Damage Identification; Finite Element Model updating; Bayesian Learning; Earthquake Ground Motions; Ground Motion Amplification; Ridgecrest Earthquake Sequence
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Heaton, Thomas H. (advisor)
  • Kohler, Monica D. (co-advisor)
Thesis Committee:
  • Asimaki, Domniki (chair)
  • Heaton, Thomas H.
  • Kohler, Monica D.
  • Clayton, Robert W.
Defense Date:30 September 2020
Record Number:CaltechTHESIS:11052020-043034327
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for Chapter 2. adapted for Chapter 4.
Filippitzis, Filippos0000-0001-8377-4914
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13992
Deposited By: Filippos Filippitzis
Deposited On:12 Nov 2020 18:22
Last Modified:28 Oct 2021 22:35

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