Digital twin (DT) technology has received immense attention over the years
due to the promises it presents to various stakeholders in science and
engineering. As a result, different thematic areas of DT have been explored.
This is no different in specific fields such as manufacturing, automation, oil
and gas, and civil engineering, leading to fragmented approaches for
field-specific applications. The civil engineering industry is further
disadvantaged in this regard as it relies on external techniques by other
engineering fields for its DT adoption. A rising consequence of these
extensions is a concentrated application of DT to the operations and
maintenance phase. On another spectrum, Building Information Modeling (BIM) are
pervasively utilized in the planning/design phase, and the transient nature of
the construction phase remains a challenge for its DT adoption. In this paper,
we present a phase-based development of DT in the Architecture, Engineering,
and Construction industry. We commence by presenting succinct expositions on DT
as a concept and as a service and establish a five-level scale system.
Furthermore, we present separately a systematic literature review of the
conventional techniques employed at each civil engineering phase. In this
regard, we identified enabling technologies such as computer vision for
extended sensing and the Internet of Things for reliable integration.
Ultimately, we attempt to reveal DT as an important tool across the entire life
cycle of civil engineering projects and nudge researchers to think more
holistically in their quest for the integration of DT for civil engineering
applications.

我们提出了在建筑、工程和建筑行业中基于阶段的数字孪生发展，通过将数字孪生作为一个被广泛应用的工具跨越建筑工程项目的整个生命周期以及鼓励研究人员在整合数字孪生技术的过程中以更全面的方式进行思考。

数字孪生与土木工程阶段：重新调整采用策略

Digital Twins and Civil Engineering Phases: Reorienting Adoption  Strategies

The digital twin concept represents an appealing opportunity to advance
condition-based and predictive maintenance paradigms for civil engineering
systems, thus allowing reduced lifecycle costs, increased system safety, and
increased system availability. This work proposes a predictive digital twin
approach to the health monitoring, maintenance, and management planning of
civil engineering structures. The asset-twin coupled dynamical system is
encoded employing a probabilistic graphical model, which allows all relevant
sources of uncertainty to be taken into account. In particular, the
time-repeating observations-to-decisions flow is modeled using a dynamic
Bayesian network. Real-time structural health diagnostics are provided by
assimilating sensed data with deep learning models. The digital twin state is
continually updated in a sequential Bayesian inference fashion. This is then
exploited to inform the optimal planning of maintenance and management actions
within a dynamic decision-making framework. A preliminary offline phase
involves the population of training datasets through a reduced-order numerical
model and the computation of a health-dependent control policy. The strategy is
assessed on two synthetic case studies, involving a cantilever beam and a
railway bridge, demonstrating the dynamic decision-making capabilities of
health-aware digital twins.

本研究提出了一种基于预测性数字孪生的方法，用于土木工程结构的健康监测、维护和管理规划，通过动态贝叶斯网络建模实现了实时的结构健康诊断和故障预测，并利用此信息用于动态决策框架中的维护和管理行动的优化规划。通过两个综合案例研究，验证了该方法对健康感知数字孪生的动态决策能力。