Enhancing resilience through environmental data integration in early construction planning: a cloud-based framework

Environmental constraints relevant to construction planning—such as protected areas, hazard zones, and regulatory requirements—are typically fragmented across geospatial services, regulatory documents, and expert reports, and are rarely integrated systematically into BIM-based workflows during early design stages. Existing BIM–environmental data integrations and ICDD-based solutions primarily rely on static model exchanges or manually maintained links, which become outdated as designs and environmental inputs evolve. This limits traceability, increases rework, and undermines proactive, regulation-compliant planning. This paper presents a cloud-based dynamic multimodel framework that addresses this gap by enabling version-aware semantic linking between BIM models, environmental datasets, and regulatory documents within an ISO 21597-compliant information container. The fundamental contribution lies not in combining BIM and environmental data per se , but in automating the maintenance, validation, and propagation of semantic links across document versions, without manual reconstruction of linksets as planning iterations occur. This is achieved through an ontology-driven extension of the ICDD container that explicitly models document versions, identifiers, and link semantics. The framework is validated through an illustrative environmental planning use case implemented within the iECO project, involving multiple BIM versions, regulatory planning documents, and externally referenced geospatial datasets. Evaluation results show that semantic links were preserved or correctly invalidated with 100% link update accuracy across three BIM revisions, while automated link propagation reduced update time from approximately 40.66 min (manual linking) to 5.5 s for the same set of links. Query results remained consistent across versions, demonstrating stable traceability under iterative planning conditions. These findings demonstrate that the proposed framework moves beyond static BIM–GIS and conventional ICDD workflows by providing a technically robust mechanism for managing data evolution in early construction planning. The approach supports transparent, auditable, and regulation-oriented integration of environmental information and establishes a foundation for future extensions toward automated reasoning, real-time sensor integration, AI-assisted validation, and digital twin applications.