Document Type

Theses, Ph.D

Abstract

The development of high-quality digital twins of built assets is essential for resolving defects and integrating off-site fabricated components in construction and renovation applications. A key component of digital twins is the three-dimensional (3D) digital models. Building Information Modelling (BIM) and Geographic Information Science (GIS) models serve as valuable geospatial sources for these models. The integration of BIM and GIS data enables the creation of comprehensive digital twins within a georeferenced national mapping framework. Since digital twins are developed to reflect the existing conditions of the assets in the real world, geospatial surveying instruments and techniques are typically used to produce the as built BIM and GIS models. However, their integration is hindered by several challenges. One of the main challenges is the varying geospatial data quality of the models, which significantly impacts the integration process and the success of digital twin development. Despite recognition of this shortcoming, the existing literature lacks a comprehensive framework that addresses this challenge.

Therefore, this research proposes the Level of Integration (LoInt) framework, which provides a mechanism for managing the geospatial data quality of integrated BIM-GIS models through a hierarchical integration approach. The LoInt framework consists of four levels of integration (LoInt0, LoInt100, LoInt200, and LoInt300), each level addresses a specific aspect of integration. LoInt0 represents the initiation phase that involves tasks related to preparation, production, and geometric validation of the as-built BIM model. At LoInt100, the as-built GIS model is created and geometrically validated. LoInt200 addresses the semantic enrichment and semantic validation of the as-built BIM and GIS models. LoInt300 represents the highest level of integration, which addresses data storage, querying, and visualisation of the models.

The proposed framework was evaluated throughout different phases of this research. A case study was conducted to assess its applicability, while semi-structured interviews with industry experts were used to validate the framework and confirm its reliability. The findings demonstrated that the framework is both applicable and reliable, providing a systematic approach for predicting, controlling, and validating the geospatial data quality of integrated BIM-GIS models throughout the digital twin development process.

DOI

https://doi.org/10.21427/v3rg-rh47

Creative Commons License

Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.

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