AI–BIM–Digital Twin Integration for Architectural Design and Lifecycle-Value Decision Support in Nigerian Housing Estate Management

Housing-estate performance depends on whether architectural design information remains usable through construction, occupation, maintenance, renewal and valuation. This critical structured narrative review examines how artificial intelligence (AI), Building Information Modelling (BIM) and digital twins can support architectural design quality, information continuity and lifecycle-value decision-making in Nigerian housing estates. The analytical corpus comprises 42 peer-reviewed journal articles published between 2014 and 2026, supplemented by standards, legal instruments and institutional sources. Evidence indicates complementary but distinct roles: BIM structures architectural and asset information; AI can augment prediction, classification and option evaluation; and digital-twin functions can connect maintained digital representations with operational and post-occupancy evidence. Nigerian evidence consistently identifies skills, interoperability, infrastructure, client demand, investment and organisational capacity as implementation constraints. However, most local studies remain sector-wide rather than longitudinal studies of occupied housing estates. The review therefore contributes three applied decision tools: a five-stage architecture–estate-valuation implementation framework, a Nigeria-specific data-governance and access-responsibility matrix, and an illustrative lifecycle-cost break-even test for selective sensing. The economic rule requires discounted decision benefits to equal or exceed discounted acquisition, connectivity, maintenance, replacement and governance costs before sensor expansion. Because the framework has not yet been empirically validated on Nigerian housing estates, the proposed thresholds are decision rules for pilot testing rather than demonstrated performance effects. Digital investment should be judged by measurable improvements in information quality, maintainability, operational performance and valuation-relevant evidence rather than technological novelty alone.

artificial intelligence; Building Information Modelling; digital twins

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