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

Linzhi Huang; Tiantian Liu; Zhiqiang Deng; Chenfeng Zhang; Shaofeng Chen

doi:10.54691/ymda4x81

Authors

Linzhi Huang School of Civil Engineering and Geomatics, Southwest Petroleum University, Nanchong, Sichuan 637000, China
Tiantian Liu School of Civil Engineering and Geomatics, Southwest Petroleum University, Nanchong, Sichuan 637000, China
Zhiqiang Deng School of Civil Engineering and Geomatics, Southwest Petroleum University, Nanchong, Sichuan 637000, China
Chenfeng Zhang School of Civil Engineering and Geomatics, Southwest Petroleum University, Nanchong, Sichuan 637000, China
Shaofeng Chen School of Civil Engineering and Geomatics, Southwest Petroleum University, Nanchong, Sichuan 637000, China

DOI:

https://doi.org/10.54691/ymda4x81

Keywords:

AI–BIM Integration; Renewal of Aging Urban Neighborhoods; Urban Renewal; Digital Twin; Full Life Cycle Governance.

Abstract

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.”

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