인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Purpose: This study aims to propose a hybrid modular supply system for the reconstruction of Kharkiv, a city severely damaged by war. The primary objective is to establish a resilient and resource-efficient urban recovery model that integrates rapid shelter provision with long-term adaptability. By addressing both immediate housing needs and sustainable urban regeneration, the system aspires to provide a scalable framework applicable to various post-conflict contexts. Method: The research employs a qualitative methodology combining literature review, precedent analysis, and comparative case study. Three urban recovery models—Kharkiv’s hybrid modular system, Aleppo’s post-war redevelopment, and Seoul’s incremental regeneration—were systematically examined to identify operational processes, strengths, and limitations. These insights were synthesized into a framework that grounds the proposed strategy. Building on this analytical foundation, the hybrid modular approach is articulated as a design-based proposal, integrating off-site supply (factory-based prefabrication of structural cores) with on-site supply for flexible assembly and progressive expansion. This dual system also considers partially functioning infrastructure and the adaptive reuse of construction yards. Result: The hybrid system demonstrates efficiency in both speed and scalability. Off-site manufactured cores allow for rapid deployment of safe shelters, while on-site assembly supports incremental growth that accommodates diverse household needs. Furthermore, by incorporating environmentally sustainable practices such as resource-efficient construction and adaptive reuse, the system enhances long-term resilience. This framework not only addresses Kharkiv’s urgent housing crisis but also provides a transferable model for resilient urban regeneration in post-conflict environments worldwide.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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