DATA, DIGITAL & BIM | INSIGHT
LOD 400 requirements mandate fabrication-
ready models with sufficient detail for component manufacturing and installation. The evolution from LOD 300 to LOD 400 and ultimately
LOD 500 reflects regulatory agencies’ recognition that traditional construction documentation is insufficient for managing complex underground infrastructure projects. Cohesive knowledge systems provide the foundational infrastructure needed to meet these evolving BIM requirements while adding to value engineering.
GIS Integration The GIS-based integration of diverse data sources is widely recognised and preferred across North America. To illustrate this, consider the case of a tunnel
project beneath the city’s dense urban core requiring a comprehensive analyses of surface settlements and impacts to nearby structure. A GIS-based approach is adopted to generate a
unique visualisation depicting the effects of ground deformation due to tunnelling and station construction — a significant risk for project situated in an urban environment. The primary aim is to communicate project risk to the stakeholders through a single source of truth by integrating multiple sources of information and results from engineering assessments. This includes integrating information about:
(a) Level of ‘as-built’ information available for structures overlying the alignment;
(b) Expected greenfield ground deformations due to tunnelling;
(c) Potential damage category of overlying structures due to tunnelling-induced ground movement (damage category of the structure defined per Burland et al. 1977); and,
(d) Modified damage category of overlying structures accounting on stiffness of their ‘as-built’ foundations and other factors, etc.
This GIS-based workflow involves developing a Python script/code to break down the polygon edges to apply the building information. The vertices are then utilised to model the facades of the structure for a damage assessment due to tunnelling. A unique ID of the building is extracted from the building data, and the assessed damage level is input to the GIS to visualise the effects of tunnel excavation, providing a clear depiction of both settlement and structural stability. Figure 3 alongside presents an example of the
developed visualisation from a tunnel project in North America. A direct link to the ‘as-built’ conditions of the structures — typically stored as PDFs of drawings, on Bentley ProjectWise or SharePoint sites — is provided as structure metadata within the GIS environment, allowing the user to interact with multiple levels of information at once. The workflow also captures any updates to the structural stability due to change in geotechnical conditions and/or change in the planned tunnel alignment.
Predictive Modelling with Real-Time Data The Internet of Things (IoT) revolution in construction has equipped tunnelling projects with unprecedented sensor density and data generation capacity. Modern TBMs contain hundreds of sensors generating
millions of data points daily, while distributed monitoring systems track everything from ground settlement to air quality. This sensor proliferation creates both opportunities and challenges that cohesive knowledge systems are uniquely positioned to address. Rather than being inundated with data, integrated systems transform this information abundance into actionable intelligence that improves project outcomes. Artificial intelligence (AI) and machine learning (ML)
applications in tunnelling require large, integrated datasets to develop effective predictive models. Predictive maintenance algorithms can correlate equipment performance with geological conditions,
Left, figure 3:
A GIS-based integration of multi-layered information into a unified visualisation to support effective decision-making
October 2025
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