Despite the growing emphasis on digital twins in construction, there is limited understanding of how to enable effective human interaction with these systems, limiting their potential to augment decision-making. This paper investigates the research question: “How can construction control rooms be utilized as digital twin interfaces to enhance the accuracy and efficiency of decision-making in the digital twin construction workflow?”. Design science research was used to develop a framework for human-digital twin interfaces, and it was evaluated in a real-world construction project. Findings reveal that control rooms can serve as dynamic interfaces within the digital twin ecosystem, improving coordination efficiency and decision-making accuracy. This finding is significant for practitioners and researchers, as it highlights the role of digital twin interfaces in augmenting decision-making. The paper opens avenues for future studies of human-digital twin interaction and machine learning in construction, such as imitation learning, codifying tacit knowledge, and new HCI paradigms.

Complex systems are not entirely decomposable; hence, interdependencies arise at the interfaces in complex projects. When changes occur, significant risks arise at these interfaces as it is hard to identify, manage and visualise the systemic consequences of changes. Particularly problematic are the interfaces in which there are multiple interdependencies, which occur where the boundaries between design components, contracts and organisation coincide, such as between design disciplines. In this paper, we propose an approach to digital twin-based interface management, through an underpinning state-of-the-art review of the existing technical literature and a research agenda to identify the characteristics of future data-driven solutions. We set out an approach to digital twin-based interface management and an agenda for research on advanced methodologies for managing change in complex projects. This agenda includes the need to integrate work on identifying systems interfaces, change propagation and visualisation, and the potential to significantly extend the limitations of existing solutions by using developments in the digital twin, such as linked data, semantic enrichment, network analyses, natural language processing (NLP)-enhanced ontology and machine learning.

In the realm of construction production control, effective communication across operational levels and the rapid influx of diverse data are essential. Yet, integrating this data faces challenges due to disparate systems and a lack of common terminology, resulting in data silos and hindered interoperability. An ontology-based solution emerges as promising for enhancing interoperability. This research paper introduces the development, implementation, and assessment of the cSite ontology, encompasses several crucial facets necessary for efficient production control such as location, activities, and documents. To evaluate its practicality, a real-case study was conducted, wherein the ontology was employed to answer competency questions through SPARQL queries. Furthermore, interactive dashboards, situated within the construction control rooms, were developed to present the information visually. This paper underscores the transformative potential of integrated and visualised production information in construction projects. Additionally, it illuminates how the cSite ontology can facilitate the development and implementation of construction digital twins.

The past few years have seen new entrants into the construction sector backed with unprecedented levels of funding from venture capital funds or other large investment firms. These entrants seek to leverage industry 4.0 principles for the digitalization and industrialization of construction. In addition to new technologies, these firms also bring new business models that depart from the project-based tendering system found in traditional construction. The first new business model is vertical integration. These firms are structured as integrated hierarchical firms, keeping control of product architecture and processes in-house. These firms control production by developing their own off-site factories. The second new business model is digital systems integration. These firms leverage an integrated cloud-based product configurator to enable mass customization. Using principles of capital-light industry 4.0 supply chains, digital system integrators can manufacture parts from periphery supply chain partners suppliers, including new sectors such as automotive, aerospace, manufacturing or industrial. The third new business model is the transformation of an existing project-based business toward industrialized construction through the creation of a spinoff factories. The chapter includes examples of the three business models and concludes with a discussion of how new business models may be catalysts for potential disruption of the established construction sector.

Extending current work on visualization in the Architecture, Engineering, and Construction (AEC) sector, this paper describes an industry-led collaborative research and innovation project to develop and use a control room on the construction site. The work is inspired by NASA mission operations, with its large-scale visual display. It addresses the challenges of visualizing realtime construction data. Working with a main contractor, technology companies, and other researchers, we first give an overview of the progress of the overall project to date and discuss our contributions on requirements, realtime simulation of construction data, and visualization. We conclude by discussing the contribution to work on visualizing construction.

Manually laying out the floor plan for buildings with highly-dense adjacency constraints at the early design stage is a labour-intensive problem. In recent decades, computer-based conventional search algorithms and evolutionary methods have been successfully developed to automatically generate various types of floor plans. However, there is relatively limited work focusing on problems with highly-dense adjacency constraints common in large scale floor plans such as hospitals and schools. This paper proposes an algorithm to generate the early-stage design of floor plans with highly-dense adjacency and non-adjacency constraints using reinforcement learning based on off-policy Monte-Carlo Tree Search. The results show the advantages of the proposed algorithm for the targeted problem of highly-dense adjacency constrained floor plan generation, which is more time-efficient, more lightweight to implement, and having a larger capacity than other approaches such as Evolution strategy and traditional on-policy search.

In the construction sector, complex constraints are not usually modeled in conventional scheduling and 4D building information modeling software, as they are highly dynamic and span multiple domains. The lack of embedded constraint relationships in such software means that, as Automated Data Collection (ADC) technologies become used, it cannot automatically deduce the effect of deviations to schedule. This paper presents a novel method, using semantic web technologies, to model and validate complex scheduling constraints. It presents a Linked-Data based Constraint-Checking (LDCC) approach, using the Shapes Constraint Language (SHACL). A prototype web application is developed using this approach and evaluated using an OpenBIM dataset. Results demonstrate the potential of LDCC to check for constraint violation in distributed construction data. This novel method (LDCC) and its first prototype is a contribution that can be extended in future research in linked-data, BIM based rule-checking, lean construction and ADC.

New forms of data science, including machine learning and data analytics, are enabled by machine-readable information but are not widely deployed in construction. A qualitative study of information flow in three projects using building information modeling (BIM) in the late design and construction phase is used to identify the challenges of codification that limit the application of data science. Despite substantial efforts to codify information with common data environment (CDE) platforms to structure and transfer digital information within and between teams, participants work across multiple media in both structured and unstructured ways. Challenges of codification identified in this paper relate to software usage (interoperability, information loss during conversion, multiple modelling techniques), information sharing (unstructured information sharing, drawing and file based sharing, document control bottlenecks, lack of process change), and construction process information (loss of constraints and low level of detail). This paper contributes to the current understanding of data science in construction by articulating the codification challenges and their implications for data quality dimensions, such as accuracy, completeness, accessibility, consistency, timeliness, and provenance. It concludes with practical implications for developing and using machine-readable information and directions for research to extract insight from data and support future automation.