Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163040
Title: System vulnerability modeling and assessment in seismic-prone areas using machine learning
Authors: Chen, Weiyi
Keywords: Engineering::Civil engineering::Construction management
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chen, W. (2022). System vulnerability modeling and assessment in seismic-prone areas using machine learning. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163040
Abstract: Earthquakes have caused great social and economic losses to human societies in recent years. Vulnerability modeling and assessment are important measurements for seismic risk mitigation. Conventional seismic vulnerability analysis frameworks have limitations such as subjectivity, low capacity of index information, or high computation cost. The motivation of this thesis is to overcome these drawbacks by employing machine learning techniques. In this thesis, a machine learning-based seismic vulnerability management framework is proposed. The proposed framework is composed of four objectives: Objective 1 aims to assess the regional seismic vulnerability with the information fusion approach. Objective 2 aims to assess the building vulnerability by XGBoost models learned from earthquake building damage data. Objective 3 aims to predict the casualty rate and economic loss of earthquake disaster areas with AutoML models. Objective 4 aims to explore the effective emergency response plans of hospital networks by Multi-objective optimization.
URI: https://hdl.handle.net/10356/163040
DOI: 10.32657/10356/163040
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Theses

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