Doctoral Dissertations

Keywords and Phrases

Deep learning; earthquake damage; surrogate models

Abstract

"Seismic damage assessment is a critical step to enhance community resilience in the wake of an earthquake. This study aims to develop deep learning-based surrogate models for widely used fragility curves to achieve more accurate and rapid assessment in practice. These surrogate models are based on artificial neural networks trained from the labelled ground motions whose resulting damage classes on targeted structures are determined by nonlinear time history analyses. The development of various surrogate models is progressed in four phases. In Phase I, the multilayer perceptron (MLP) is used to develop multivariate seismic classifiers with up to 50 hand-crafted intensity measures (IMs) as inputs when trained for the simultaneous fragility estimation and (both local and global) damage classification of a code-conforming benchmark reinforced concrete (r/c) frame building. In Phase II, a MLP seismic classifier is trained with 6 IMs and 2 structural parameters as inputs to consider both the primary earthquake and structural uncertainties in the portfolio-scale seismic damage assessment of one-story, residential woodframe structures near the New Madrid Seismic Zone. In Phase III, convolutional neural networks (CNNs) with encoded ground-motion images as inputs are trained for the benchmark r/c frame building to automatically extract features (e.g., IMs) of the ground motions and thus avoid the hand-crafted IMs. In Phase IV, new one-dimensional CNNs with raw ground motions as inputs are developed for the benchmark r/c frame building to further improve the computational efficiency of existing CNN-based seismic damage classifications. Based on the above studies, future research directions are identified to mature the developed models for applications in earthquake and wind engineering"-- Abstract, p. iv

Advisor(s)

Chen, Genda

Committee Member(s)

Sneed, Lesley
Yan, Guirong Grace
Ma, Hongyan
Liu, Kelly H.

Department(s)

Civil, Architectural and Environmental Engineering

Degree Name

Ph. D. in Civil Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2022

Pagination

xv, 151 pages

Note about bibliography

Includes_bibliographical_references_(pages 46, 76, 107, 134 and 148-150)

Rights

© 2021 Xinzhe Yuan, All rights reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12182

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