Search

Large volumes of 3D parametric datasets, such as building information modeling (BIM), are the foundation for developing and applying smart city and digital twin technologies. Those datasets are also considered valuable tools for efficiently managing rebuilt structures during the operation and maintenance stages. Nevertheless, current approaches developed for the scan-to-BIM process rely on manual or semi-automatic procedures and insufficiently leverage semantic data in point clouds. These methods struggle to accurately represent large-scale indoor complex layouts and extract details from irregular-shaped unstructured elements, causing inefficiencies in BIM model generation. To address these issues, we propose an innovative scan-to-BIM framework based on deep learning algorithms and raw point cloud data, enabling the automatic generation of 3D models for both structured and unstructured indoor elements. Initially, we propose an enhanced deep learning neural network to improve the point clouds' semantic segmentation accuracy. Subsequently, an efficient workflow is developed to reconstruct 3D building models of structured indoor scenes. The proposed workflow can reconstruct large-scale data with multiple room layouts of Manhattan or non-Manhattan structures and reconstruct 3D models automatically by using a BIM parametric algorithm implemented in Revit software. Moreover, we introduce a robust method for unstructured elements to automatically generate corresponding 3D BIM models, even when the incorporating semantic information is incomplete. The proposed approach was evaluated on synthetic and real data for different scales and complexities of indoor scenes. The results of the experiments demonstrate that the improved model significantly enhances the overall semantic segmentation accuracy compared to the baseline models. The proposed scan-to-BIM framework is efficient for indoor element 3D reconstruction, achieving precision, recall, and F-score values ranging from 96% to 99%. The generated BIM models are competitive with traditional methods regarding model completeness and geometric accuracy. • We propose an innovative scan-to-BIM framework based on deep learning algorithms: • We propose an enhanced deep-learning neural network to improve the point clouds' semantic segmentation accuracy. • A novel and efficient workflow is developed to reconstruct 3D building models of structured indoor scenes. It can reconstruct large-scale data with multiple room layouts in 3D model automatically by using a BIM parametric • For unstructured elements, we propose a robust method to automatically generate 3D BIM models, even when the semantic information is incomplete. [ABSTRACT FROM AUTHOR]

Accurate and efficient inspection of rebar dimensions has proven to be a persistent challenge for researchers and practitioners. This paper introduces a semantically enriched 3D model-based system that employs computer vision and deep learning for location-aware identification and tracking of rebar issues. The system comprises four modules: (A) digital twin generation, (B) segmentation, (C) inspection, and (D) issue identification and tracking. The generation module constructs 3D models from rebar structures. The segmentation and inspection modules analyze the 3D models, enriching them with semantic information. The issue identification and tracking module exchanges information between the semantically enriched 3D models and the building information models across time. An experiment on a column rebar cage is conducted. A precision of over 90% and a recall of over 97% are reported in 3D instance segmentation. Diameter inspection achieves an accuracy of 95.5% for large-size rebars. Spacing inspection achieves a mean relative error of 0.98%. The defective spacing is identified and tracked. • Fuses deep learning and computer vision to construct 3D semantic rebar model. • BIM- and digital-twin-based information system fulfills rebar quality control. • Autonomous rebar inspection pipeline directly generates issue reports from images. [ABSTRACT FROM AUTHOR]

BIM-to-Metaverse offers benefits to the AEC industry, but the challenge of efficiently and effectively implementing it will be formidable and require several years. The objective of this study is to identify the outsourcing forms of this new technology for construction project stakeholders and to explore its associated implementation challenges. Specifically addressed will be (a) innovations in XR technologies and how to convert BIM models to XR models for visualization in the metaverse, (b) an integrated definition function (IDEF0) model that describes how to prepare the BIM model for metaverse, (c) a use case for utilizing metaverse in the AEC industry and an evaluation of the concerns that must be resolved prior to successful implementation, and (d) an example illustrating the process. The metaverse implementation in design and construction can enhance collaboration, productivity, and quality but implementation cost, data security, and hardware and software interoperability are major challenges in its implementation. • Metaverse has potential for improving workflow and productivity in AEC industry. • This study proposed an IDEF0 model for design and construction phases. • This study explored challenges and limitations of implementing Metaverse in construction projects. • This study reviewed applications of metaverse in non-AEC industries. • This study proposed a use case for metaverse in a construction project. [ABSTRACT FROM AUTHOR]

The transformation of dynamic supply chains within the realm of offsite manufacturing presents a unique and compelling opportunity to effectively respond to shifts in market demand. However, this paradigm shift also entails novel challenges and prospects for streamlining processes, augmenting efficiency, and ultimately driving success across production workflows and logistical operations. Dynamic supply chains necessitate unimpeded access to high-quality, dependable data to facilitate real-time, data-driven decision-making. Furthermore, owing to their inherently intricate nature, these supply chains are subject to a heightened vulnerability to disruptions and delays, surpassing those encountered in traditional supply chains. Against this backdrop, this paper proposes a framework that synthesises the convergence of the Internet of Things (IoT), Building Information Modelling (BIM), and blockchain technology (BCT) into a cohesive and integrated system, thereby optimising the efficiency and transparency of supply chain management operations. The proposed framework takes cognisance of the challenges inherent in ensuring information traceability, mitigating fragmented information, enhancing interoperability, and fortifying reliability throughout the design, production, and implementation phases of assets within dynamic supply chains operating in the offsite manufacturing domain. To illustrate the efficacy of our proposition, we present a framework that exemplifies the seamless integration of a permissioned, open-source distributed ledger, which forms the bedrock of our support infrastructure for IoT and BIM integration. Through rigorous analysis, we showcase the effectiveness of the developed framework in the form of a designed Supply Chain Management Model (SCMM-PDO), which harmonises the physical, digital, and operational layers within a robust system architecture (BCT.IoT-BIM). The integration of these technologies provides a decentralised solution, reinforcing supply chains with improved security, reliability, and quality control mechanisms, thus enhancing the efficiency of the supply chain network. It is anticipated that this transformative integration will yield substantial improvements in key performance indicators (KPIs) such as operational efficiency, cost reduction, and the cultivation of trust and accountability across supply chain activities. The research proposes an IoT and BIM conceptual framework that simulates the dynamic supply chain operations, facilitating real-time analysis and prediction of supply chain participants' behaviours from resources, processes, and assets. Furthermore, the integration of IoT-BIM (digital twinning) with a blockchain-based system, such as Hyperledger Fabric, engenders a secure and transparent framework for the diligent tracking of assets and information throughout the supply chain. The findings of this study offer an all-encompassing conceptual framework for the secure and transparent tracking of asset information by integrating Blockchain-based technologies with IoT and BIM. This research endeavour thus expands the existing body of knowledge, establishing a foundation for future investigations within this proliferating field. • "Developed a real-time analysis and prediction framework for dynamic supply chain operations, integrating IoT, BIM, and blockchain technology." • "Created a supply chain management model (SCMM-PDO) integrating physical, digital, and operational layers in a robust system architecture." • "Addressed information traceability and reliability challenges in offsite manufacturing supply chain with a new framework." • "Combined IoT, BIM, and blockchain for improved efficiency and transparency in supply chain management." • "Highlight potential improvements and trust across supply chain activities through IoT, BIM, and blockchain integration." [ABSTRACT FROM AUTHOR]

New-generation intelligent construction places higher demands on digitisation and intelligence of tunnel. Digital twin (DT) effectively supports high-fidelity modelling, virtual-real mapping, and analysis-based decision-making but with research in the initial stage. To begin with, this paper delves into the complexity and uncertainty inherent in tunnel construction, highlighting DT as a promising solution compared to exisiting technologies such as Building Information Modelling. Then, a systematic literature survey is conducted, revealing growing focus on DT research topics. To provide comprehensive insights into DT-related technologies and their application in tunnel construction, this paper clusters literature from perspectives of sensor networks, Internet of Things (IoT), computer vision-based twin data acquisition, communication, natural language processing (NLP), automatic control-based connection, and geometric, semantic, analytical integrated twin modelling. These aspects shed light on potentials and limitations of existing researh in developing a functional DT. In response to the challenges of information richness, timeliness, and analytical capabilities, an improved conceptual framework tailored for tunnel is proposed to close the information and control loop. Finally, the paper discusses the prospects and gaps of DT in theory and practice to leverage DT implementation. • Digital Twin greatly enhances the development of intelligent tunnel construction. • A comprehensive literature analysis is conducted to assess status of Digital Twin technologies in tunnel construction. • A close-loop conceptual framework of Digital Twin is proposed. • Gaps and future prospects of Digital Twin for tunnel construction are discussed. [ABSTRACT FROM AUTHOR]

Digital twin, a technology bridging the physical and digital domains, has found extensive application in digitally advanced industries. However, its adoption in the construction sector remains limited, hindered by challenges related to construction, integration with real-time data capture, and visualisation platforms. This paper presents a construction industry digital twin that combines Building Information Modelling-based data visualisation with an Internet of Things-driven live data capture platform, outlining a methodology for its development. The digital twin was implemented in a university library embodying the 'Living Lab' concept to account for the nuances associated with live environments. It integrates sensors with Building Information Modelling to offer a semiotic representation of the library's internal conditions in digital twin, empowering facility managers to proactively optimize thermal, lighting, and air quality. • Digital twin (DT) presents opportunities to proactively address challenges before they occur. • Complexities with the building of DT in the construction industry pose many difficulties. • An appropriate data capture and visualisation platform is key to DT development. • Application of DT in educational buildings, specifically in 'Living Labs' is scarce. [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]

Robots are expected to play an important role in future construction work. However, they are not yet widely adopted by the industry because it is difficult and expensive to program robots to conduct a variety of construction tasks. This paper presents a method for intuitively and flexibly teaching robots to perform various construction tasks through demonstrations. Robots are first programmed with basic skill primitives and then learn the sequencing of these primitive skills to perform different types of construction work under the guidance of human supervisors. The construction workflow and the interaction processes are enabled by a process-level digital twin system. Case studies with three assembly scenarios and a wooden frame construction experiment are used to present and verify the proposed method. The proposed approach enables automatic robot motion sequencing abilities through Learning from Demonstration and has the potential to enable the widespread adoption of robots on construction sites. • Learning from demonstration module for robots to learn motion sequencing skills. • Scene distance array is used to determine the corresponding skill primitive. • Construction robot deployment based on modularized skill primitives and digital twin. • Case studies demonstrate robots learn and transfer knowledge for various tasks. • Experiments show robots made 91.77% of decisions in building a frame by learning. [ABSTRACT FROM AUTHOR]

Collaboration in indoor fire emergencies requires participants to share various information regarding individuals, fires, and resources. To facilitate response actions, this study develops a system to provide the necessary information, calculate route guidance for escape and rescue, track victim locations, and share resources with participants promptly. The system includes a pathfinding module using geometries of building information models, a positioning module fusing inertial sensor data and wireless signals via a particle filter, an information exchange module that delivers emergency-related properties, and an information sharing module that distributes information through a multi-user environment. Moreover, a two-stage pathfinding algorithm and a hybrid positioning approach are proposed to improve computational efficiency and localization accuracy. Through comparative evaluation, the system is validated to reduce the computational time by 89.29% and lessen the positioning error to 1.36 m. The study is expected to provide explicit insights into developing digital twins for collaborative emergency response. • A two-stage pathfinding algorithm is proposed to improve computational efficiency. • Particle filter-based IMU and Wi-Fi fusion is developed to reduce positioning error. • A data exchange mechanism is built to supply emergency-related IFC properties. • A multi-user system with functions specialized for information sharing is created. • System feasibility and functionality are verified by a case study and experiments. [ABSTRACT FROM AUTHOR]

Effective environmental condition monitoring provides constant surveillance of the built environment and reveals deteriorations that could impact the daily operation of facilities, especially amid COVID situations. However, the current Industry Foundation Classes (IFC) data schema for Building Information Modelling (BIM) provided limited support to represent full semantics related to environmental sensing and monitoring. How to semantically enrich the IFC schema with enhanced data description capability for informed decision-making in smart facilities management (FM) amid COVID situations remains an open question. This paper develops a semi-automatic extension and integration of IFC data schema with Sensor Model Language (SensorML) specification in order to support automated built environment sensing and monitoring. Referring to SensorML, an extended IFC model view definition for a comprehensive description of required sensor metadata and sensing entities is presented. An Internet of Things (IoT) sensor network is then established to realise continuous data collection from a variety of wireless sensing devices. The spatial-temporal data captured by the IoT sensor network are extracted by a regular expression-based data distillation algorithm and integrated with the digital twin, in which spatial interpolation algorithms further analyse, compute, and visualise the state of the environment. The proposed method is demonstrated via an experimental study which supports real-time environmental monitoring and delivers more actionable insights to facility managers to sustain the daily operation of buildings. This study contributes new methods and models to semantically enrich the digital twin from the data perspective for environmental condition monitoring during the pandemic time which fosters the development of holistic building facility management. • Present a digital twin approach to monitor the built environment for facilities management. • Conduct data integration with textual mapping and semi-automatic relationship classification. • Propose an integrated data model based on Industry Foundation Classes (IFC) and SensorML. • Develop BIM-based algorithms for integration and interpolation of spatial-temporal data into digital twins. • Establish a wireless sensor network (mounted on UGV) for automated environmental monitoring. [ABSTRACT FROM AUTHOR]

Working with heritage is complex and projects often include scientific, structural, and documentary studies. Every step requires a systematic approach in which the contribution of the diverse experts that participate in the conservation of heritage assets is analysed following the logical sequence of the project execution phase. However, there are still no scientific contributions that record the complete process. A logical sequence, called a 'systematic approach' in this paper, should start with data acquisition using remote sensing techniques, the insertion of point cloud data in a Building Information Modelling (BIM) environment, ontologies applied to HBIM (Historic BIM), data classification, and evolution towards the Digital Twin (DT) concept. This paper also analyses the modelling workflow of a case study, the construction of a column base of the Santiago church in Jerez de la Frontera (Spain). This component has been automatically modelled from a mesh in an Autodesk Revit family and through an adaptive mesh process in ArchiCAD. Furthermore, one of the objectives was the data organisation by following and extending a standard data structure of the column components. This paper presents a novel holistic approach from data capture, analysis of complex geometry, construction of the semantically enriched parametric model, and exchange of it among all involved professionals in open source IFC formats. This systematic approach therefore makes it possible to obtain a validated DT for the life-cycle management of architectural elements. • Systematic approach to a Historic BIM project. • Assessment of the building and the information model. • Classification of structural elements. • Characterisation of a pillar of a church in the evaluation of its life cycle. [ABSTRACT FROM AUTHOR]

Registering images to a building information model is an effective approach of associating as-built component status to as-designed information. However, registering a single still image to a digital twin is difficult due to pose ambiguities caused by the limited field of view in images. Several recent studies have started focusing on leveraging panoramic images to address problems, such as partial occlusions, repetitive facility components and textureless views, which could cause registration failures in existing image-based registration workflows. Even though having a bigger field of view helps in locating where the image was taken, registering a panoramic image to a building information model is still challenging due to inconsistent visual appearances, such as model vs. image visual differences caused by temporary objects in a scene, lighting condition changes, and different levels of details. In this paper, we present a novel method that registers panoramic images to a digital twin in a hierarchical way: the proposed method first performs rough registration through image retrieval using semantic segmentation, then localizes the image with a fine registration through minimizing semantic reprojection errors. Compared to existing methods, the developed method has the following contributions: 1) semantic-based image retrieval makes the rough registration process robust to lighting condition changes, texture differences, and temporary objects, 2) semantic-based image retrieval allows for bi-directional queries between a model and images, 3) reprojection-based fine registration further reduces the localization error due to the dimension reduction of features during image retrieval. Though the proposed method is developed for panoramic images, it can be generalized to monocular images at the cost of localization accuracy. The developed method was evaluated on a real-world academic building and a synthetic dataset, and the results showed that the proposed method can localize a panorama in less than a second and achieve sub-meter level localization error. • A new panorama-to-BIM registration method is developed. • A new image retrieval approach is developed for hierarchical registration. • Semantic features help address visual differences between BIM and images. • Accuracy and robustness of image-to-BIM registration are improved. [ABSTRACT FROM AUTHOR]