Adaptability assessment of the Enning road heritage district in China based on GA-BP neural network

Abstract

In complex urban systems, heritage districts must adapt beyond their original functions, requiring innovative reuse strategies to meet urban development demands. This study integrates socio-cultural and qualitative factors to enhance adaptive reuse predictions while fostering inclusive, community-driven renewal. Using a GA-BP neural network model, stakeholder mapping, and focus group interviews, the findings highlight two key aspects: (1) cultural significance, per capita consumption, and functional type serve as critical indicators for sustainable adaptive reuse, and (2) participatory governance and transparent decision-making are essential for effective implementation, with professional mediators and targeted support aiding conflict resolution. The governance transformation of Enning Road exemplifies a multi-network approach that integrates government leadership, enterprise-driven operations, multi-stakeholder participation, and benefit-sharing mechanisms. These insights contribute to the sustainable revitalization of heritage districts, offering a replicable model for balancing heritage preservation with urban development.

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