DATA, DIGITAL & BIM | INSIGHT
Left, figure 6:
Example of a digital archive of tunnel project showing the integration of geological-geotechnical conditions with TBM data
project designs and construction methods while supporting more accurate life cycle cost predictions for current projects.
RECOMMENDATIONS AND TECHNICAL CONSIDERATIONS Successful implementation of cohesive knowledge systems in tunnelling requires careful consideration of technical architecture, organisational change management, and stakeholder alignment strategies. The technical foundation must be sufficiently flexible to accommodate diverse data types while maintaining performance and security requirements that meet industry standards. Cloud-based architectures provide the scalability
and accessibility needed for modern tunnelling projects while supporting the collaborative requirements of distributed project teams. However, data sovereignty and security concerns require careful evaluation of cloud deployment strategies, particularly for projects involving critical infrastructure or sensitive geological information. Data standardisation represents a critical success
factor that requires industry-wide coordination to achieve maximum benefit. While proprietary data formats will continue to exist, the development of common exchange standards and semantic frameworks can significantly improve interoperability between different systems/software and organisations. Industry initiatives should focus on developing these standards while recognising the practical constraints of upfront investments. Change management strategies must address both
technical adoption challenges and organisational cultural shifts required for successful implementation. Traditional engineering workflows based on sequential information exchange must evolve to support real- time collaboration and shared decision-making processes. Training programmes must address devising new collaborative workflows enabled by integrated information access.
The integration of AI capabilities requires careful
consideration of algorithm transparency, bias mitigation, and human oversight requirements. While ML can provide valuable insights and
predictions, the high-stakes nature of tunnel construction requires that automated recommendations remain subject to expert review and validation. The development of explainable AI approaches, that
can provide clear rationales for their recommendations, becomes critical for gaining engineer confidence and regulatory acceptance.
FUTURE DIRECTION AND INDUSTRY TRANSFORMATION As more projects implement integrated data management approaches, the collective learning potential increases exponentially, creating positive feedback loops that accelerate innovation while reducing risk. The technical foundations for cohesive knowledge
systems exist today. Successful implementations of cohesive knowledge systems in other industries demonstrate their viability and value. The primary barriers to adoption are organisational
rather than technical. Overcoming those barriers requires coordinated effort
from clients, contractors, consultants, and technology providers to develop the standards, processes, and collaborative frameworks needed for industry-wide transformation. Projects that implement comprehensive cohesive
knowledge systems today will enjoy significant competitive advantages while contributing to the industry-wide knowledge base that will benefit future projects. The time for incremental improvements to
fragmented data management approaches has now passed; the future belongs to approaches that successfully integrate information assets into cohesive intelligence systems that transform data into wisdom and insight into action.
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