Digital Twins in Construction and the Built Environment

There has been increasing interest in the term digital twin (DT) in recent years. In built environments, DT is still a new concept, although it is becoming more appealing to the academic scholars and practitioners in the architecture, engineering, construction, and operation industries since the perceived applications of DT address long-term challenges such as the lack of efficiency and productivity. This chapter aims to navigate the key aspects of DT adoption and implementation (e.g., DT's components, stakeholders, and enabling technologies) to provide more holistic and systematic insights into DT deployment for the built environments. It defines the two concepts of DT characteristics and components. The chapter makes a comparison between Building Information Modeling and DT technologies in terms of their data requirements, ontological structures of their databases, use cases, implementation approaches, and scales of deployment.

This chapter focuses on the discovery of invariant signatures of architecture, engineering, and construction (AEC) objects, how it helps with Building Information Modeling (BIM) interoperability and in turn may benefit the development of digital twins. It couples the demonstration of invariant signatures with the industry foundation classes (IFC) standard as many BIM software platforms support IFC import/export. Three main types of invariant signatures include geometric signatures, locational signatures, and materials signatures, whereas other types of signatures (such as metadata and higher order) can always be added per the task at hand. Proven successes were introduced in the use of invariant signatures in supporting BIM interoperability between architectural design and construction management, architectural design and structural analysis, architectural design and building code compliance checking, and architectural/MEP design and energy modeling and simulation.

Construction robots are increasingly being used on job sites to assist workers with repetitive and labor-intensive construction tasks. There is a lack of effective methods to evaluate workers' health and safety during human-robot interactions to take necessary preventive actions owing to the potential hazards to humans posed by construction robots. In this regard, digital twin (DT) technology holds promise for effectively characterizing the human-robot partnership and pertinent worker health information in real-time. In the context of human-robot interaction, DT technology can be effectively employed to simulate the interactions between workers and robots. This chapter proposes a framework for the construction of a DT-based platform that will support the health and safety monitoring of workers during human-robot teaming operations. It examines a virtual reality-based DT model for worker health monitoring using a combination of physiological signals, machine learning techniques, and immersive technologies.

Ergonomic risks that a worker experiences in the workplace are triggered by hazards and the level of exposure to those hazards. This chapter introduces three near real-time and comprehensive methods for ergonomic risk assessment among electric system maintenance technicians. It presents a digital twin-based ergonomic risk assessment framework including one optical method and two non-optical methods. The first and second methods are based on the REBA tool, and ergonomic risk is evaluated in a near real-time setting considering the external load. The last method is non-optical, and the ergonomic risk assessment is based on a novel approach instead of the REBA tool. Both non-optical and optical methods use the REBA tools for generating ergonomic risk, which is a well-known tool for ergonomic risk evaluation in job sites. This digital twin-based facilitates accurate and continuous monitoring of the worker's movements and posture.

The digital twin (DT) paradigm promises to take permanent, bidirectional data exchange between the physical asset and its digital representation into account at its core. Nevertheless, as with any digital model, a DT is always an abstraction of reality. This chapter introduces a guide to creating purpose-driven, geometric-semantic DTs. It proposes a DT implementation framework with use-case-derived requirements and an integrity metric for evaluation. Potentially involved technologies for implementation are reviewed in three phases: raw data acquisition and processing, data enrichment, and geometry provision. The chapter elaborates requirement-dependent implementations for the use case of “Efficient HVAC maintenance.” A compact variety of further DT use cases across several domains is provided, each along with specific requirements and implementation suggestions to fulfill them. The chapter presents a guide that allows a structured design of successful DT implementations with a sound selection of technologies and processing approaches.

Facilities management (FM) is a critical postconstruction discipline that ensures that facilities operate safely and efficiently. As a more recent development, digital twins (DT) have emerged as a highly customizable and scalable digital innovation that can be used in FM. To respond to the need for domain knowledge-driven use cases for DT, this chapter defines a generic system architecture for the application of DT to the operational activity of deferred maintenance decision-making based on a thorough examination of existing academic and technical knowledge on DT and maintenance management. The proposed system architecture provides a generic application framework on how to develop, implement, and evaluate operational Building Information Modeling-based DT for predictive maintenance, with deferred maintenance as an exemplar use case. The analytical capabilities of operational DT and the promise of advanced and autonomous monitoring and control by cognitive DT hold significant value for FM and more specifically maintenance management.

The initial application of digital twins (DT) in the construction industry was to upgrade construction and design methods by integrating the Building Information Modeling-based platforms and cooperation model. This chapter intends to investigate the use of DT technologies in intelligent building management to provide insights into the transformative potential of DT. It establishes a bottom-up conceptual framework for DT deployment in building projects, along with illustrative case studies. The chapter discusses the connection between facility management demands and technological advancements. A campus building project is illustrated to compare the life-cycle energy and carbon performance of a conventional building and a smart building with Internet of Things (IoT) in the United Kingdom. The chapter also presents an illustrative case study approach to examine DT adoption in the architectural, engineering, and construction industries while focusing on how DT were utilized in building maintenance and operations.

In recent years, the predictive maintenance (PdM) approach has emerged as a cost-cutting balance between the action after failure (reactive maintenance) and routine maintenance before failure (preventive maintenance). The goal of digital twins (DT) in facility management is to constantly monitor the performance of a system and aid in intelligent decision-making for the optimal operation and maintenance of the facility. Integrating DT and PdM concept facilitates real-time monitoring and predicting the building facility's status. This chapter proposes a standard data-driven DT framework for the PdM of an air handling unit (AHU). It develops a framework for data-driven failure predictive DT of the AHU using LSTM and LSTM encode-decode models. The chapter tests the performance of the developed framework using real-time operational data of an AHU. The Earle Hall Building at University Park, Pennsylvania State University is used as the test platform.

The emergence of the digital twins (DT) concept presents new opportunities for port stakeholders to enhance automation, digitalization, and intelligence levels in their pursuit of becoming smart ports. This chapter utilizes a case study of Weihai Port in China to provide insights into DT implementation. It highlights key aspects, including the state-of-the-art and challenges in DT development for civil infrastructure and port sectors, business analysis processes for defining port DT requirements, data availability and data requirements, technical architecture, and data environments, and finally, outcomes and lessons learned. Emphasis is placed on business analysis, data availability and requirements, and the data environment. In future work, the smart port DT for Weihai Port will add datasets (including more real-time monitoring sensors) and update functions in each module to enhance comprehensiveness and user interaction.

For historical Building Information Modeling (HBIM), surveying and modeling efforts generally concentrate on the accurate representation of spaces and constructive elements, but with the techniques employed, result in static representations that do not make use of real-time data. This chapter explores HBIM and digital twins (DT) to develop an innovative workflow to parametrically model historical buildings. It reviews existing workflows, modeling methods, data acquisition, and processing techniques to integrate geometric, historical, constructive, and real-time monitoring data in HBIM models to develop a DT that could be used in simulations and studies. These support the management, preventive maintenance and publicizing of historic buildings. The chapter uses the Museu Republicano “Convenção de Itu,” a building listed by the Brazilian National Historical and Artistic Heritage Institute, as a pilot case study to refine and validate the developed workflow.

Central to the concept of digital twins is its requirement to mirror the physical object in real-time. This necessitates developing a 3D model in a virtual environment equipped with sensors that capture and monitor the current state of the physical structure or element. Integrating sensor data with the virtual environment facilitate the creation of a virtual reality experience, offering an immersive exploration of bridge analytics. Superimposing the virtual model over the actual bridge structure generates an augmented reality view. The Internet of Things, structural health monitoring, and intelligent transportation systems rely on embedding sensors into the physical object. This chapter discusses a framework for the integration of current transportation technologies to produce a bridge digital twin. As a cyber-physical system, the digital twin contains complex computing and controls that produce a virtual-physical feedback loop which enhances the intelligence of the system and behavior of the end users.