Road Digital Twin Design Automation

National Highways (NH) has set up a strategic vision to modernise the maintenance, development, and operation of the Strategic Road Network (SRN). This goal, outlined in the SRN Initial Report has specific targets for leveraging digital technologies to improve (1) safety in the SRN, (2) environmental sustainability of the SRN, and (3) SRN operation and maintenance.

The studentship will (a) derive local and national stakeholders' user and information requirements, and DT scoring metrics; and (b) create a framework for automatically generating a pareto-optimal front of trustworthy information models, libraries, and cloud architectures consistent with CDBB's Information Management Framework, (i) at micro, meso and macro scales and resolutions; (i1) for geometry, condition, and other property groups; and (iii) for product, process, and resource information. This is to enable i) productisation; (ii) interoperability with the National DT programme; (ini) information security; (iv) futureproofing; and (v) information curation.

Handcrafted digital twin (DT) representations of assets and of their associated defects, processes and resources are appearing in the market at an increasing rate. However, designing such DTs in an automated fashion is an under constrained problem with millions of correct solutions. This raises several key research questions: What is a good framework for automating DT design?

Which one of the solutions generated by the framework is the best one, and what does 'best' mean in this setting? How might we auto-generate such design possibilities? How should we evaluate the resulting solutions, and what would be the baseline for such an evaluation?