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The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions. [Display omitted] • Designing a hybrid bottom-up, top-down approach for automatic building digital model creation. • Align the capabilities of AI in scene understanding with domain knowledge to overcome challenges in indoor 3D reconstruction. • Developing a knowledge-based method for 3D space parsing in largescale indoor point cloud. • Reconstruction of the parametric building's floor plan using point cloud with low sensitivity to poor data quality. • Creation of dynamic parametric building digital models (LOD200) for flexible facility management and space analysis. [ABSTRACT FROM AUTHOR]

The implementation of emerging technologies generates new possibility for the Architectural Engineering and Construction sector. Specifically, the study focuses on the applications of Digital Twins in the heritage construction sector, an area characterized by significant potentials and challenges in various research fields (e.g., energy, digital technology, citizen engagement, cultural aspects, management, etc.). Bibliographic and scientometric analyses enable the systematic categorization of the literature, analyzing a total of 133 scientific publication realized in the years 2001–2023. The bibliometric analysis outlines time evolution, geographical distribution, type, and disciplinary fields of the publications. Meanwhile, the scientometric research identifies co-occurrence networks of authors, keyworks, and research topics. Thank to these techniques, the following research topics on Digital Twins in the Built Heritage have been identified and clustered: (i) Digital Twins and 3D scanner techniques; (ii) Digital Twins and virtual reality; (iii) Digital Twins and Heritage Building Information Model (HBIM); and (iv) Digital Twins and Internet of Things (IoT). Each technique is critically discussed, highlighting applications, methods, limitations, and gaps in the literature. This analysis also paves the way for future research directions and potential solutions to enhance the automation in the heritage sector. Additionally, a glossary of essential disciplinary terms is provided to guide the understanding of individual definitions as they apply to heritage constructions. The primary challenges relate to accuracy, security, interoperability, and synchronization of data and technologies from various sources, requiring appropriate verification and validation methodologies, as well as tailored standards, protocols, interfaces, and regulations. Moreover, ethical considerations should underlie the utilization of digitally driven predictions that incorporate human-computer techniques, and human–robot interactions. This extends to the formulation of specific policies, and legislation. Only through this approach, the immense potential of digital technologies can emerge to create cross-cutting professions, interactive engagement techniques, and compatible preservation and management activities. • Bibliometric and scientometric mapping of scientific articles published between 2001 and 2023. • Digital Twins in heritage constructions booming in publications, geographic distributions, types, disciplinary fields, and researchers. • Digital Twins in heritage constructions cover 3D techniques, virtual reality, HBIM, IoT, building management, and structural analysis. • Study reveals insights on interoperability, metaheuristic algorithms, intelligent matrixes, and decision support systems. • Six levels of application from pre-DT to Adaptive Intelligent DT with the interaction of 3D scanners, HBIM, IoT, Metaverse, and Artificial Intelligence. [ABSTRACT FROM AUTHOR]

Current procedures for the rapid inspection of buildings and infrastructure are subjective, time-consuming, and cumbersome to document, necessitating new technologies to automate the process and eliminate these shortcomings. Fortunately, recent developments in imaging devices and artificial intelligence, such as computer vision, provide the necessary tools for this, though they are not yet integrated into infrastructure applications. In this paper, we propose an end-to-end pipeline that generates damage-augmented digital twins for buildings at LOD3, including geometrical information as well as data pertaining to damage condition and its characterization. Our framework incorporates multiple-view images to (1) create a level of detail model, (2) segment damage information, and (3) characterize damage. The core of the method is the structure from motion, which is used to reconstruct the building scene, and machine-learning models that segment and characterize damage. In contrast to current practices, our method does not require manual intervention, generates lightweight models, and can be applied to a wide range of assets. The results generated with our pipeline represent a significant step towards an automated infrastructure damage assessment. We intend to expand our work in the future to include real-time applications and applications to other types of infrastructure. Codes and data sets are publicly available (https://github.com/eesd-epfl/DADT%5fbuildings and https://doi.org/10.5281/zenodo.7767478). • A novel framework to generate Damage Augmented Digital Twins (DADT) of buildings. • State-of-the-art solutions for 3D models, crack segmentation and characterization. • Bench-marking data set for evaluating the automated generation of DADT of buildings. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) technology is far from being comprehensive and mature, resulting in their piecemeal implementation in practice where some functions are automated by DTs, and others are still performed by humans. This piecemeal implementation of DTs often leaves practitioners wondering what roles (or functions) to allocate to DTs in a work system, and how might it impact humans. A lack of knowledge about the roles that humans and DTs play in a work system can result in significant costs, misallocation of resources, unrealistic expectations from DTs, and strategic misalignments. To alleviate this challenge, this paper answers the research question: When humans work with DTs, what types of roles can a DT play, and to what extent can those roles be automated? Specifically, we propose a two-dimensional conceptual framework, Levels of Digital Twin (LoDT). The framework is an integration of the types of roles a DT can play, broadly categorized under (1) Observer, (2) Analyst, (3) Decision Maker, and (4) Action Executor, and the extent of automation for each of these roles, divided into five different levels ranging from completely manual to fully automated. A particular DT can play any number of roles at varying levels. The framework can help practitioners systematically plan DT deployments, clearly communicate goals and deliverables, and lay out a strategic vision. A case study illustrates the usefulness of the framework. • Digital Twin human interaction • Digital Twin can play multiple roles in a work system. • Levels of Digital Twin framework provides a common language for understanding. • Digital Twin must be evaluated for each case – a "one size fits all" does not work. [ABSTRACT FROM AUTHOR]

The emerging concept of digital twins outlines the pathway towards intelligent buildings. Although abundant building data carries an overwhelming amount of information, if not well exploited, the redundant and irrelevant data dimensions result in the overfitting problem and heavy computational load. Taking the fault detection and diagnosis process for building HVAC systems as the case, this paper adopts a symbolic artificial intelligence technique to identify informative sensory dimensions for building-specific faults by exploring the symbolic representation of labelled time-series. To preserve this ad-hoc temporal knowledge in the digital twin ecosystem, machine-readable fault tags are defined to label corresponding sensor entities. A digital twin data platform is developed to annotate the real-time data with fault tags and produce filtered low-latency data streams associated with a specified tag to automate this process. This paper describes a digital twin-based approach to automatically identify and pick up informative data to support dynamic asset management. • Adopt tagging to represent and preserve knowledge learned through AI techniques. • Use Bag-of-Words to identify informative sensory dimensions for case-specific faults. • Define machine-readable fault tags to label the sensors and annotate real-time data. • Produce low-latency data streams appended with the specified tags to feed the FDD. • Informed a way to enable asset management functionalities through digital twin. [ABSTRACT FROM AUTHOR]

[Display omitted] • Laser scanning and photogrammetric technologies are fused in the pipeline. • Semantic information in images is mapped to 3D laser-scanned point clouds. • Small objects of di_erent classes are detected in the creation of digital twins. • Text information is recognised and used to enrich digital twins. This paper addresses the challenge of enriching geometric digital twins of buildings, with a particular emphasis on capturing small but important entities from the electrical and the fire-safety domain, such as signs, sockets, switches, smoke alarms, etc. Unlike most previous research that focussed on structural elements and processed laser point clouds and images separately, we propose a novel method that fuses laser scanning and photogrammetry methods to capture the relevant objects, recognise them in 2D images and then map these to a 3D space. The considered object classes include electrical elements (light switch, light, speaker, socket, elevator button), safety elements (emergency switch, smoke alarm, fire extinguisher, escape sign), plumbing system elements (pipes), and other objects with useful information (door sign, board). Semantic information like class labels is extracted by applying AI-based image segmentation and then mapped to the 3D point cloud, segmenting the point cloud into point clusters. We subsequently fit geometric primitives to the point clusters and extract text information by AI-based text detection and recognition. The final output of our proposed method is an information-rich digital twin of buildings that contains geometric information, semantic information such as object categories and useful text information which is valuable in many aspects, like condition monitoring, facility maintenance and management. In summary, the paper presents a nearly fully-automated pipeline to enrich a geometric digital twin of buildings with details and provides a comprehensive case study. [ABSTRACT FROM AUTHOR]