Optimizing urban health assessment for effective community center regeneration via simulation
As urban renewal in China enters a high-quality development stage, urban health examination has evolved into a multi-scalar framework covering the “urban – block – community – housing” hierarchy. Within this framework, community centers are key carriers of community public services and important objects for community-scale assessment. This study establishes an improved evaluation index system for natural ventilation and daylighting based on international standards and existing literature, and develops a full-process optimization model integrating “data extraction – problem diagnosis – optimization and renovation.” Parametric simulation is used to assess environmental performance and identify deficiencies. Taking the Ziyunnanli Community Center in Beijing as a case study, the proposed model is applied to simulate daylighting and ventilation performance. Results show that targeted renovation strategies improved the physical environment: extreme near-window DF values were reduced from 17.60% and 15.63% to 5.65% and 4.83%, while a deep-plan area previously failing daylighting standards improved from 1.86% to 2.55%. Natural ventilation was also optimized by mitigating winter wind eddies above 2.0 m/s and summer stagnant zones below 0.5 m/s. This study provides a systematic and quantitative approach for community center regeneration within the urban health examination framework.
Authors
- Wei Gong (ORCID: https://orcid.org/0000-0001-8496-5288)
- Huihui Liang
- Qian Yilin
- Zhao Dapeng
- Wu Yingshi
Institutions
- Tangshan College (CN)
- Beijing Forestry University (CN)
- Beijing Institute of Architectural Design (China) (CN)
Publication Details
- Journal
- Journal of Asian Architecture and Building Engineering
- Published
- 2026-07-26
- DOI
- https://doi.org/10.1080/13467581.2026.2706277
- Primary Topic
- Urban Transport and Accessibility
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Beijing Social Science Fund