Resilience-informed optimization of post-typhoon recovery strategy for residential community

Severe typhoon hazards frequently strike China’s coastal regions, causing widespread damage to residential communities. Post-typhoon recovery is often constrained by limited physical resources, such as repair crews and materials. These resource shortages cause queuing delays and significantly impact functional recovery. It necessitates optimal recovery strategies that align with practical demands. To address this challenge, this study develops a resilience-informed optimisation method for post-typhoon recovery strategies for residential communities. The proposed framework integrates typhoon hazard analysis, building physical damage assessment, and post-hazard recovery modelling. A building delay time parameter is introduced to explicitly model the restrictions imposed by limited resources. A bi-objective optimisation model is then formulated and solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimise the overall community recovery time and maximise the community resilience measure. A case study of a typical coastal residential community demonstrates that resource limitations severely flatten the functional recovery curve. The results reveal a clear trade-off between minimising the overall community recovery time and maximising community resilience. The optimisation method provides a superior set of Pareto optimal solutions, equipping decision-makers with diverse optimal alternatives to balance competing recovery objectives.

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Publication Details

Journal
Urban Resilience and Earthquake Engineering
Published
2026-08-25
DOI
https://doi.org/10.1080/30656680.2026.2710641
Primary Topic
Tropical and Extratropical Cyclones Research
Type
article
Field-Weighted Citation Impact
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article

Resilience-informed optimization of post-typhoon recovery strategy for residential community

Hongliang Miao, D. Li, Xiaowen Ji, Yan‐Gang Zhao
Urban Resilience and Earthquake Engineering
Tropical and Extratropical Cyclones Research
article

Resilience-informed optimization of post-typhoon recovery strategy for residential community

Hongliang Miao, D. Li, Xiaowen Ji, Yan‐Gang Zhao
article en

Abstract

Severe typhoon hazards frequently strike China’s coastal regions, causing widespread damage to residential communities. Post-typhoon recovery is often constrained by limited physical resources, such as repair crews and materials. These resource shortages cause queuing delays and significantly impact functional recovery. It necessitates optimal recovery strategies that align with practical demands. To address this challenge, this study develops a resilience-informed optimisation method for post-typhoon recovery strategies for residential communities. The proposed framework integrates typhoon hazard analysis, building physical damage assessment, and post-hazard recovery modelling. A building delay time parameter is introduced to explicitly model the restrictions imposed by limited resources. A bi-objective optimisation model is then formulated and solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimise the overall community recovery time and maximise the community resilience measure. A case study of a typical coastal residential community demonstrates that resource limitations severely flatten the functional recovery curve. The results reveal a clear trade-off between minimising the overall community recovery time and maximising community resilience. The optimisation method provides a superior set of Pareto optimal solutions, equipping decision-makers with diverse optimal alternatives to balance competing recovery objectives.

Urban Resilience and Earthquake Engineering
Sinopec (China) (CN), Beijing University of Technology (CN)
Openalex Percentile: Top 14%
Tropical and Extratropical Cyclones Research
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Resilience-informed optimization of post-typhoon recovery strategy for residential community — Hongliang Miao, D. Li, et al. · Urban Resilience and Earthquake Engineering (2026) | TGRS Research Map | TGRS