Digital twin (DT) has been developed for a single function in most of previous studies. This study aims to empower
DT with a multilayered integration of multifunctional and multidisciplinary models in the built environment. It starts
with the development of a framework of three hierarchical tiers of regional, asset, and system DT modules, defines a
new concept of the degree of digital twinning (DODT) to a real world by the number of models enabled by a common
DT platform, enables spatiotemporal analysis in multiple scales to couple nonstructural with structural building
components and connect the built environment to planning constructions, enables an integrated computational and
informational modeling, and demonstrates multiple values of the DT of a university campus in asset lifecycle
management. A mechanical model is used to evaluate the structural and nonstructural behavior of buildings under
earthquake loads, allowing damage/cost scenario studies for community resilience in the wake of an extreme event.
A multitask machine learning model is used to detect the type and material of building roofs from videos, allowing
infrastructure planning for existing buildings. An informational model allows master planning for green space
development, environmental planning for flood zone susceptibility, security protocol development, and energy
harvesting and utilization. The DODT allows the value-driven digital replication of a physical twin at different levels
and thus the value proposition of structural health monitoring in broader architectural and engineering practices.
The DODT of the campus is eight, indicating that the DT has broader impacts on campus’ asset lifecycle management.