INSIGHT | DATA, DIGITAL & BIM
The days of compartmentalised information flows and
reactive problem-solving are rapidly becoming obsolete as project stakeholders are increasingly expecting proactive risk mitigation through continuous data integration and predictive analytics.
COHESIVE KNOWLEDGE SYSTEMS: DEFINING A ‘cohesive knowledge system’ in tunnelling represents an integrated framework that seamlessly connects diverse data sources, formats, and storage locations into a unified and accessible information ecosystem, that is relatively easy to navigate. Unlike simple data warehouses or basic integration platforms, these systems employ advanced analytics, machine learning and semantic technologies to create meaningful relationships between seemingly disparate information types. The architecture of such systems extends beyond
technical integration to encompass organisational workflows, stakeholder communication protocols, and decision-making processes. Figure 1 shows unique domains of knowledge within tunnelling projects that exist exclusively and warrant an integration. As an example, the data from geological investigations becomes dynamically linked with real-time TBM performance metrics, while GIS databases provide spatial context for as-built documentation. Historical project data informs predictive models that guide current construction decisions, and feedback loops ensure continuous system refinement. The sophistication of cohesive knowledge systems lie
in their ability to maintain data integrity while enabling flexible access patterns. In the planning and design phase, engineers can query
the boreholes at specific chainage locations to access the geological conditions, investigate into impacts to utilities and structures, summarise design outputs to study the variation in the results, and store real-time data from field visits.
During construction, TBM performance data,
construction photographs, and surveying outputs can be queried. Project managers can visualise risk distributions across the tunnel alignment while simultaneously accessing mitigation strategies employed on similar projects. This level of integration transforms data from static and isolated resources into dynamic intelligence that actively supports decision-making processes.
COHESIVE KNOWLEDGE SYSTEMS: APPLICATIONS Advances in BIM The tunnelling and underground construction industry is experiencing an unprecedented shift toward stringent digital delivery requirements, with clients increasingly mandating comprehensive Building Information Modelling (BIM) specifications extending beyond traditional documentation practices. This transformation reflects broader industry demands for enhanced project transparency, operational efficiency, and long-term asset management capabilities. BIM has matured beyond simple 3D visualisation to
become a comprehensive project delivery methodology, requiring integrated data management throughout the asset life cycle. Level of Development (LOD) 500 has become the gold
standard for tunnel projects, representing field-verified ‘as-built’ models that capture precise geometric and non-geometric information about completed assets. Unlike traditional ‘as-built’ drawings, LOD 500 models prove comprehensive digital representations that include detailed component specifications, maintenance schedules, warranty information, and operational parameters essential for life cycle asset management. LOD 350 specifications are becoming standard
for construction documentation, requiring detailed representation of component interfaces and connections with other building systems.
Above, figure 2: Example of integrating mechanical, electrical, plumbing (MEP), and other utility networks within a 3D digital model of an underground facility
32 | October 2025
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45
