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Adaptive spatial reconstruction and generative design method for textile industrial heritage based on topology relationship optimization

Year: 2026

Type: article

Abstract: Textile industrial heritage regeneration is pivotal to urban renewal. To overcome limitations of conventional approaches, including reliance on subjective experience and inconsistent efficiency and quality, this study proposes a generative design method based on genetic algorithms. Quantitative analysis of Qingdao’s nine historical cotton mills identified five primary spatial typologies. Comparative evaluation against six adaptive reutilization benchmarks identified core challenges: functional mismatch, low spatial efficiency, and topological rigidity. An optimized genetic algorithm framework integrating heritage value assessment was consequently developed. This system facilitates efficient spatial reorganization while preserving cultural significance. Validation at State-owned 4th Cotton Mill demonstrates that this approach can effectively address spatial reconstruction challenges encountered during the transformation of textile industrial heritage into public spaces, providing innovative insight for the revitalization of industrial heritage.

Source: Scientific Reports

DOI: 10.1038/s41598-026-68009-4

Authors: Yan Chu, Yan Xu, Qinghua Guo, Kexing Qu

Institution: Shandong University of Science and Technology

Language: English

Topic: Cultural Heritage Management and Preservation

Subfield: Archeology

Field: Arts and Humanities

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,690