To better understand how DT can be more uniformly applied in civil engineering, a research team led by Ruda Zhang, assistant professor of civil and environmental engineering at the University of Houston, conducted an in-depth literature review to develop a phase-based approach for using DT in the architecture, engineering, and construction (AEC) industry. The team also established a five-level rating system for defining the complexity and depth of the DT being used within these phases.
“Our systematic literature review studied the conventional techniques employed at each civil engineering phase,” Zhang said. “We then identified enabling technologies such as computer vision for extended sensing and the Internet of Things for reliable integration. Our goal is to show how DT is an important tool across the entire lifecycle of civil engineering projects and hopefully nudge researchers to think more holistically in their quest to integrate DT in civil engineering applications.”
Taiwo A. Adebiyi and Nafeezat A. Ajenifuja, both at the University of Houston, authored the paper with Zhang. The study, “Digital Twins and Civil Engineering: Reorienting Adoption Strategies,” appeared in the Journal of Computing and Information Science in Engineering in October 2024.
Three phases
One reason for the slow pace of DT adoption in civil engineering is the profession’s resistance to change and the complexity of its projects. Every civil engineering project must go through three distinct and sequential phases: planning/design, construction, and operations and maintenance. Each of these phases utilizes AI in different ways, making it difficult to integrated the flow of DT across phases. The team researched how each phase uses DT and explored the different concepts/frameworks for phase-based DT applications.
Planning/Design Phase: Five Levels of BIM Growth
The integration of DT with BIM systems has been transformative for planning and design. The long-established practice of BIM has gradually evolved into a powerful digital tool.
“BIM has evolved from its inherent static configuration to an integrated dynamic powerhouse as an enabler for DT development in the built environment,” Zhang said. BIM has grown from a rudimentary virtual reality program to include simulation, sensor technologies, and artificial intelligence (AI), all of which enable an advanced DT in the built environment.
Construction: Sensing, Internet of Things
How DT in deployed in the construction phase depends the other digital technologies being used, including sensing and the Internet of Things.
“Sensing technologies paved the way for real-time construction monitoring, enabling improved productivity, hazard identification, construction waste management, and clash detection,” Zhang said. “Laser scanners, RGB cameras, and depth cameras are used for mapping sensors; however, laser scanners are limited by the lack of color information—hence their combination with digital cameras to obtain colorized 3D point clouds.”
Operations and Maintenance: Structural Health
Structural health monitoring (SHM) is an important application that relies on advanced DT technologies. SHM is segmented into two categories: diagnosis and prognosis. Most of the DT work done in this phase is diagnostic (damage detection, localization, and evaluation) with fewer advances in the prognosis category (for example, residual life prediction). Nonetheless, “the advent of several miniaturized or sensor-based SHM systems directly attached to modern structures has paved the way for real-time monitoring of the structures via model updating and parameter estimations, otherwise known as system identification,” Zhang said.
When these and other DT tools are in place, the prognosis of aspect of SHM will become more attainable, “including prediction, learning, management, optimization, and other context-specific functionalities—all of which are encompassed in a virtual duplicate of a physical infrastructure tagged DT,” Zhang added.
A competitive tool
Zhang’s research shows how BIM, sensing, and IoT are essential technologies/processes for developing highly sensitive digital twin technology for civil engineering applications. DT capabilities include data acquisition and processing through sensing technologies, data processing such as feature extraction via machine-learning methods, and data interpretation from extracted features or raw data using computer vision.
Two other functions needed for effective DT development are uncertainty quantification and model updating.
“Model updating is a must for an accurate depiction of the digital states of the corresponding physical states of an asset,” Zhang said. “However, the need for continuous model updates can be computationally expensive due to the repetitive need for evaluations.”
More research is needed to determine the most efficient approach for model updates for the DT development of structural systems. For both uncertainty quantification and model updates, randomization of parameters for probabilistic evaluations is critical.
If DT can be as advanced in civil engineering as it is in aviation and manufacturing, especially for mission planning optimization, “it will become a competitive tool for sustainable and resilient civil engineering amidst the mounting environmental and social challenges in the AEC profession,” Zhang concluded.
Mark Crawford is a technology writer in Corrales, N.M.
