Research on the Renovation and Transformation Strategies of Aging Urban Neighborhoods under the Coupling of AI Assistance and BIM Technology

Vollständiger Abstract

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To address fragmented information, coarse-grained decision-making methods, inefficient coordination, and absence of closed-loop feedback in the renewal of aging urban neighborhoods, this paper proposes an AI–BIM integrated renewal strategy. Aging urban neighborhoods are decomposed into five domains: buildings, municipal infrastructure, streets, public spaces, and community governance. A five-layer integration framework is then constructed, vertically spanning the full life cycle-data, design, implementation, and operation and maintenance-and horizontally covering all domains. The framework establishes a shared spatial data foundation through multi-source data governance, performs existing-condition assessment and needs assessment via AI analysis, drives parametric modeling, performance simulation, and carbon emission estimation through BIM–AI collaborative design, enables multi-party online collaboration on a BIM+GIS base with 4D/5D collaborative control, and realizes adaptive operation and maintenance and model iteration through digital twins and feedback of measured data. This framework establishes a closed loop of “AI-enabled perception and reasoning, BIM-based information representation, and application-driven feedback and iteration.”