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Xiongwei Zhu

Bio: Xiongwei Zhu is an academic researcher from Southeast University. The author has contributed to research in topics: Urban resilience & Flooding (psychology). The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

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TL;DR: In this paper, the authors identified 12 key influencing factors through literature review and Delphi method under a 4R (i.e., robustness, rapidity, redundancy, and resourcefulness) framework, and used the DEMATEL and ISM methods to investigate their influencing mechanisms.

41 citations


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TL;DR: In this paper , the authors reviewed the most common resilience qualities (RQs), capacities, and dimensions of the built environment and their interdependencies, and concluded that integrated resilience indicators, planning, and design methodology are crucial for incorporating RQs into the framework that influences the built environments.

25 citations

Journal ArticleDOI
TL;DR: In this paper , a systematic literature review is presented, identifying main crises impacts on the built environment and their solutions, and the main interconnections between the identified crises impacts and solutions were established.

10 citations

Journal ArticleDOI
03 Sep 2022-Land
TL;DR: In this article , an evaluation indicator system for urban resilience in China on four dimensions (economy, environment, society, and infrastructure) was constructed using the entropy weight method to measure the resilience levels of 138 cities in 8 urban agglomerations from 2005 to 2018.
Abstract: This paper constructs an evaluation indicator system for urban resilience in China on four dimensions—economy, environment, society, and infrastructure. The evaluation indicator is used by the entropy weight method to measure the resilience levels of 138 cities in 8 urban agglomerations from 2005 to 2018. Using the Theil index and variance decomposition method, we explore the size and sources of urban resilience differences among the eight urban agglomerations from the dual perspectives of space and structure and employ geographic detectors to identify the driving factors behind their differences. The results show that although the overall resilience level of the eight urban agglomerations is not high, it shows an upward trend. The differences within the eight urban agglomerations are the main spatial sources of urban resilience differences and economic resilience is the main structural source of urban resilience differences. Moreover, economic resilience and social resilience have the greatest contribution and driving effect on the resilience differences of BTH, YRD, PRD, MYR, CC, GP, and HC urban agglomerations, but the difference in resilience of CP is mainly caused by the difference in infrastructure resilience. Compared with the single factor, the impact of the interaction of each factor is even greater.

9 citations

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors employed the TOPSIS method, particle swarm optimization (PSO), and extreme learning machine (ELM) to measure urban resilience from 2010 to 2020.

8 citations