Digital Twins and Civil Engineering Phases: Reorienting Adoption Strategies
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Abstract (arXiv)
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) is 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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 190 references that are in the map. First posted on arXiv 4 Mar 2024.
Builds on
13 of 190 references.
References · 13
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
- 2016Learning Video Object Segmentation from Static Imagescited by 151
- 2017Semantic Instance Segmentation with a Discriminative Loss Functioncited by 118
- 2019Digital Twin: Values, Challenges and Enablerscited by 81
- 2020A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scalecited by 58
- 2020Digital twin, physics-based model, and machine learning applied to damage detection in structurescited by 17
- 2023Multi-fidelity Bayesian Optimization: A Reviewcited by 12
- 2024Epsilon-Greedy Thompson Sampling to Bayesian Optimizationcited by 8
- 2018Towards Automated Post-Earthquake Inspections with Deep Learning-based Condition-Aware Modelscited by 4
- 2022Artificial intelligence-driven digital twin of a modern house demonstrated in virtual realitycited by 4
- 2020Normal-bundle Bootstrapcited by 2
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