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There has been a plethora of research on the performance of schedule and cost in construction projects. However, current research falls short in investigating the combined contribution of different construction technologies on project performance metrics. Specifically, none of the previous efforts have used integrated earned value management (EVM) parameters and utilized construction technologies to assess project schedule and cost performance. This paper fills this knowledge gap. To this end, a multistep research methodology was employed to (1) explore the utilization ratings of 13 construction technologies based on data collected from 52 industry experts; (2) rank these technologies in terms of their contribution to schedule and cost savings; (3) map the utilization ratings of construction technologies to schedule and cost variances by leveraging data from 48 construction projects with varying levels and scopes of technology integration to gauge the impact of using these technologies; and (4) develop an associated decision-support model that underwent rigorous verification under extreme conditions and sensitivity analysis. This study makes a noteworthy contribution to the existing body knowledge, revealing that the major drivers for schedule savings are building information modeling (BIM), unmanned aerial vehicles (UAVs), and augmented/virtual reality (AR/VR), while additive manufacturing (AM), digital twin (DT), and Internet of Things (IoT) are the most significant contributors to cost savings. Moreover, the study reveals strong correlations between various technologies, such as UAVs and laser scanning, geographic information system (GIS) and GPS, AR/VR and digital twins, as well as robotics/automation and AR/VR. Furthermore, the study introduces a novel model that enhances the prediction of project performance, specifically in terms of schedule and cost variations resulting from the adoption of various construction technologies. Eventually, it improves the decision-making process related to the deployment of the investigated construction technologies. [ABSTRACT FROM AUTHOR]

Historic Building Information Modelling (HBIM) plays a pivotal role in heritage conservation endeavours, offering a robust framework for digitally documenting existing structures and supporting conservation practices. However, HBIM's efficacy hinges upon the implementation of case-specific approaches to address the requirements and resources of each individual asset and context. This paper defines a flexible and generalisable workflow that encompasses various aspects (i.e., documentation, surveying, vulnerability assessment) to support risk-informed decision making in heritage management tailored to the peculiar conservation needs of the structure. This methodology includes an initial investigation covering historical data collection, metric and condition surveys and non-destructive testing. The second stage includes Finite Element Method (FEM) modelling and structural analysis. All data generated and processed are managed in a multi-purpose HBIM model. The methodology is tested on a relevant case study, namely, the church of Santa Ana in Seville, chosen for its historical significance, intricacy and susceptibility to seismic action. The defined level of detail of the HBIM model is sufficient to inform the structural analysis, being balanced by a more accurate representation of the alterations, through linked orthophotos and a comprehensive list of alphanumerical parameters. This ensures an adequate level of information, optimising the trade-off between model complexity, investigation time requirements, computational burden and reliability in the decision-making process. Field testing and FEM analysis provide valuable insight into the main sources of vulnerability in the building, including the connection between the tower and nave and the slenderness of the columns. [ABSTRACT FROM AUTHOR]

The growing interest in building data management, especially the building information model (BIM), has significantly influenced urban management, materials supply chain analysis, documentation, and storage. However, the integration of BIM into 3D GIS tools is becoming more common, showing progress beyond the traditional problem. To address this, this study proposes data transformation methods involving mapping between three domains: industry foundation classes (IFC), city geometry markup language (CityGML), and web ontology framework (OWL)/resource description framework (RDF). Initially, IFC data are converted to CityGML format using the feature manipulation engine (FME) at CityGML standard's levels of detail 4 (LOD4) to enhance BIM data interoperability. Subsequently, CityGML is converted to the OWL/RDF diagram format to validate the proposed BIM conversion process. To ensure integration between BIM and GIS, geometric data and information are visualized through Cesium Ion web services and Unreal Engine. Additionally, an RDF graph is applied to analyze the association between the semantic mapping of the CityGML standard, with Neo4j (a graph database management system) utilized for visualization. The study's results demonstrate that the proposed data transformation methods significantly improve the interoperability and visualization of 3D city models, facilitating better urban management and planning. [ABSTRACT FROM AUTHOR]

In the dynamic realm of digital twin modeling, where advancements are swiftly unfolding, users now possess the unprecedented ability to capture and generate geospatial data in real time. This article delves into a critical exploration of this landscape by presenting a meticulously devised workflow tailored for the creation of Level of Detail 3 (LOD3) models. Our research methodology capitalizes on the integration of Apple LiDAR technology alongside photogrammetric point clouds acquired from Unmanned Aerial Vehicles (UAVs). The proposed process unfolds with the transformation of point cloud data into Industry Foundation Classes (IFC) models, which are subsequently refined into LOD3 Geographic Information System (GIS) models leveraging the Feature Manipulation Engine (FME) workbench 2022.1.2. This orchestrated synergy among Apple LiDAR, UAV-derived photogrammetric point clouds, and the transformative capabilities of the FME culminates in the development of precise LOD3 GIS models. Our proposed workflow revolutionizes this landscape by integrating multi-source point clouds, imbuing them with accurate semantics derived from IFC models, and culminating in the creation of valid CityGML LOD3 buildings through sophisticated 3D geometric operations. The implications of this technical innovation are profound. Firstly, it elevates the capacity to produce intricate infrastructure models, unlocking new vistas for modeling digital twins. Secondly, it extends the horizons of GIS applications by seamlessly integrating enriched Building Information Modeling (BIM) components, thereby enhancing decision-making processes and facilitating more comprehensive spatial analyses. [ABSTRACT FROM AUTHOR]

The metaverse is seen as a future generation of the Internet in which the virtual and the real merge into a common world. Technologies such as IoT, cloud computing, artificial intelligence or the reality–virtuality continuum underpin the metaverse and condition its evolution. This paper presents a bibliometric study on the WoS database (from 1995 to 2022) to obtain a comprehensive and non-subjective understanding of the metaverse. The study identifies the main subject areas and sources, the leading countries and authors. It also analyzes the evolution over time and the core and future research themes. Extended reality, blockchain, artificial intelligence and sensors are identified as core themes, while building information modeling, digital twins and governance emerge as future themes. Based on the bibliometric study, a general layered metaverse architecture is proposed that streamlines open challenges in the metaverse to assist researchers and companies in introducing innovative and disruptive improvements. [ABSTRACT FROM AUTHOR]

The emergence of Digital Twin (DT) technology presents unique opportunities for society by facilitating real-time data transfer from the physical environment to its digital counterpart. Although progress has been made in various industry sectors such as aerospace, the Architecture, Engineering, Construction, and Operation (AECO) sector still requires further advancements, like the adoption of these technologies over traditional approaches. The use of these technologies should become standard practice rather than an advanced operation. This paper aims to address the existing gap by presenting a comprehensive framework that integrates technologies and concepts derived from purpose-driven case studies and research studies across different industries. The framework is designed to provide best practices for the AECO sector. Moreover, it aims to underscores the potential of DT for optimization through overseeing and digital management of the built environment across the entire life cycle of facilities, encompassing design, construction, operation, and maintenance. It is based on an extensive literature review and presents a holistic approach to outlining the roles of Building Information Modelling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and other key enablers within the DT environment. These digital tools facilitating the simultaneous evaluation of associated benefits, such as resource savings and future prospects, like monitoring project sustainability objectives. [ABSTRACT FROM AUTHOR]

Complex demands in the field of cultural heritage preservation often require a multidisciplinary approach and substantial volumes of multimodal data integration and management. The conventional approach to tackling these issues revolves around using different H-BIM (historical building information model) solutions. This paper presents a prototype for a web platform that moves closer to the idea of a digital twin for physical cultural assets. Based on a light development framework, it is designed for online open access and features a versatile custom 3D viewer for intuitive interaction with the presented data. The concept requires a workflow similar to the video-game industry's 3D asset optimization to generate highly detailed 3D models and to facilitate the display of multilayered imaging data. The technological stack features a minimal MVC architecture framework and front-end stylesheets. It is designed to be independent of specific databases, enhancing portability for potential future open-source releases. Moreover, the platform employs WebGL libraries to create a dynamic 3D environment interaction. The capabilities of the web platform were tested in a case study regarding the documentation of an important 17th-century church in Romania. Further developments and current limitations of the platform are also discussed. [ABSTRACT FROM AUTHOR]

Confronted with the climate crisis, the world is making tremendous efforts in energy transition, such as expanding renewable energy that does not emit carbon. The importance of virtual power plant (VPP) operation technology has emerged to secure grid flexibility in response to the expanding renewable energy implemented due to these efforts. Accordingly, VPPs, which include photovoltaics, wind turbines, heating, ventilation, and air conditioning (HVAC), load, and EV, have been constructed. HVAC, one of the component resources, is a system that controls and regulates temperature, humidity, and airflow. Since it responds sensitively to the building's heat capacity and changes in the external environment, it requires continuous and stable control. In this paper, we used data-based modeling to implement the HVAC required for the optimal operation of VPP. Since accurately creating an equation-based HVAC model was difficult considering building information modeling and external environment variables, we used historical HVAC operation data to perform data-based modeling. The model was implemented using nonlinear regression and machine learning, such as a support vector machine and artificial neural network. Then, the data-based HVAC and the actual HVAC operation results were comparatively analyzed based on a case study, and the model's goodness-of-fit was evaluated based on performance metrics. Model performance indicators confirmed that the ANN-based HVAC model was most similar to the actual HVAC system. [ABSTRACT FROM AUTHOR]

Roads and highways are often managed by using Geographic Information Systems (GIS). However, these systems lack the level of detail that Building Information Modelling (BIM) can bring to an infrastructure management system. BMGIS integration allows the management of information from both infrastructure and environmental points of view. This provides an overview of the infrastructure, facilitating decision-making process throughout its complete life cycle. This article shows a semi-automated process to generate the hybrid BIM-GIS model of Madrid Calle30. The model together with an external database was uploaded to an intelligent management platform that allows visualising the available documentation, assisting management, and bringing the ring-road closer to a digital twin of the infrastructure. [ABSTRACT FROM AUTHOR]

With the development and popularization of the Internet of Things, big data, cloud computing, and other technologies, Digital twin technology (DTT) is increasingly applied to building operation and maintenance (O&M). However, most of the research focuses on building energy consumption, safety, and other management, and relatively little research on the monitoring of building terminal energy using information. The information is closely related to occupants' behavior, such as air conditioning, lighting, shading, window status information, as well as personnel number and location, and it has a great impact on building energy consumption. Due to different occupants' behaviors, the level of building energy consumption varies several times or even more. Take an office building as an example. Based on digital twin technology, the framework of building energy using intelligent monitoring is constructed. It mainly includes four parts, namely building physical space, virtual twin space, predictive control simulation engine, and twin big data. For each part, functions are realized through building Information Modelling (BIM), smart sensors, and Internet of Things (IoT) technologies. Based on the standard framework and every function realization method, the DTT can used for building O&M effectually. The application of building an intelligent control system based on the occupants' characteristics is simulated and analyzed in Designbuilder software 6.1.2. The results show that the digital technology application in building intelligent control systems can realize maximum energy saving for 30%. However, the DTT in building O&M is not widely used now. There is a lot of research to be completed in the future. [ABSTRACT FROM AUTHOR]

Urban excavation is an indispensable process for many construction activities such as road paving, house building, and pipe rehabilitation. However, the everincreasing complexity of underground utilities (e.g., water mains, gas lines, and sewage pipes) in urban environments challenges the safety of urban excavation, posing tremendous risks of potential collision and damage accidents. By obtaining real-time excavation information and high-fidelity simulation to evaluate safety risks, digital twin (DT) has an unexplored potential to improve urban excavation safety (UES). This research aims to investigate how a DT for urban excavation can be developed and used to improve UES. First, a multisensory solution is proposed to equip the physical excavators with the capability to precisely estimate their three-dimensional (3D) poses based on the kinematic model and social spider algorithm (SSA). Second, a building information model (BIM) of buried utilities and a 3D model of the excavators are integrated to form a dynamic virtual model that mirrors the actual excavation process. Third, based on the physical-virtual coupling DT, a real-time safety control method is proposed to proactively monitor urban excavation, dynamically assess collision risk, and timely warn against unsafe behaviors. A system prototype was developed and applied in a case study in Shandong, China. Results show that the system can precisely twin the pose of the excavator, increasing the estimation accuracy of the translation by at least 4.0 cm. The system can display the dynamic spatial position of the excavator and the buried pipes in 3D and automatically guide the excavator to operate safely in real-time, thereby avoiding potential collision accidents. [ABSTRACT FROM AUTHOR]

The paper sheds light on the process of creating and validating the digital twin of bridges, emphasizing the crucial role of load testing, BIM models, and FEM models. At first, the paper presents a comprehensive definition of the digital twin concept, outlining its core principles and features. Then, the framework for implementing the digital twin concept in bridge facilities is discussed, highlighting its potential applications and benefits. One of the crucial components highlighted is the role of load testing in the validation and updating of the FEM model for further use in the digital twin framework. Load testing is emphasized as a key step in ensuring the accuracy and reliability of the digital twin, as it allows the validation and refinement of its models. To illustrate the practical application and issues during tuning and validating the FEM model, the paper provides an example of a real bridge. It shows how a BIM model is utilized to generate a computational FEM model. The results of the load tests carried out on the bridge are discussed, demonstrating the importance of the data obtained from these tests in calibrating the FEM model, which forms a critical part of the digital twin framework. [ABSTRACT FROM AUTHOR]

Although novel construction tech systems have been developed to monitor construction sites, each collects data in isolation. We present an event processing model (EPM) that applies multiple monitoring technologies in parallel and a set of algorithms to interpret data from the multiple streams for operations control. In a full-scale lab experiment, multiple monitoring systems were applied to monitor workers as they built drywall partitions with a variety of systems and finishes. Data from the different streams were merged to establish what work was done, by whom, where, and for how long. The results suggest that collecting, merging, and interpreting diverse data streams using a multiple monitoring technologies system on-site can provide more complete and more accurate project status information than can be obtained from the use of any individual technology. This is essential for the automation of project progress monitoring, which is important in automating production planning and control tasks. Modern construction management approaches need more accurate, more reliable, and automated construction-progress monitoring. Manual monitoring is complicated and expensive in large, complex projects. It is difficult to perform the production control process successfully for whole projects with many related trades and resources. Automating data monitoring, data collection, and computing can reduce errors and increase efficiency compared to manual approaches. This paper presents a fully integrated system to manage production information through the construction life cycle. Different types of construction technologies could be integrated, along with a building information modeling (BIM) environment, into one common data-management system to report actual work progress and events completed on-site. The proposed system would support the decision-making process as part of construction planning in large and complex projects by providing accurate real-time information defining what work was done, by whom, in which location, and for how long. The information can be tailored to suit planners' needs at four different levels: (1) work package, (2) task, (3) daily recording, and (4) building element. Broadly speaking, this paper presents a digital twin construction (DTC) framework to integrate data transfer and monitoring from field-to-BIM and from BIM-to-field for automated, accurate, and intelligent production control. This system can help planners improve look-ahead planning (LAP) and decision-making processes. [ABSTRACT FROM AUTHOR]

As facility owners and operators, public sector organizations (such as government agencies) that provide public facilities and services not only maintain the existing capital assets but also plan for future developments on an ongoing basis through a capital improvement program (CIP). The CIP identifies capital projects and is typically funded through several distinct funding sources with specific usage, timing, and reporting restrictions. The continuous nature of capital improvement planning compounded by the potential for scope or funding availability changes further makes it a challenge to retain the flexibility to add new and different funding sources to an existing funding allocation plan. Existing literature about this problem is sparse, and a systemic solution is yet to be proposed that enables an owner organization to meet its goals while maintaining fiscal responsibility toward its funding agencies. The financial digital twin (FinDT) framework developed in this study integrates fund allocation and accounting with project acquisition in real time and enables the owner to automate funding allocation tasks and to synchronize change management while ensuring compliance with funding restrictions. The goal of this framework is to leverage rule-based knowledge management to consolidate and continually optimize the efficient use of accurate financial information at all stages of the capital asset lifecycle. This is accomplished through automation and digital twin (DT) integration. A use case was developed to demonstrate and evaluate the FinDT, which was found to hold potential for more efficient information management and support for decision making through enterprise-level data integration. The framework developed in this study can be used to display other nongeometric attribute information in a building information model for the purposes of visualization and interactive, efficient decision making. [ABSTRACT FROM AUTHOR]

Most of the buildings that exist today were built based on 2D drawings. Building information models that represent design-stage product information have become prevalent in the second decade of the 21st century. Still, it will take many decades before such models become the norm for all existing buildings. In the meantime, the building industry lacks the tools to leverage the benefits of digital information management for construction, operation, and renovation. To this end, this paper reviews the state-of-the-art practice and research for constructing (generating) and maintaining (updating) geometric digital twins. This paper also highlights the key limitations preventing current research from being adopted in practice and derives a new geometry-based object class hierarchy that mainly focuses on the geometric properties of building objects, in contrast to widely used existing object categorisations that are mainly function-oriented. We argue that this new class hierarchy can serve as the main building block for prioritising the automation of the most frequently used object classes for geometric digital twin construction and maintenance. We also draw novel insights into the limitations of current methods and uncover further research directions to tackle these problems. Specifically, we believe that adapting deep learning methods can increase the robustness of object detection and segmentation of various types; involving design intents can achieve a high resolution of model construction and maintenance; using images as a complementary input can help to detect transparent and specular objects; and combining synthetic data for algorithm training can overcome the lack of real labelled datasets. [ABSTRACT FROM AUTHOR]

The construction industry has been criticized for low productivity, lack of collaboration and information sharing, poor contract administration, and the like due to its decentralized and fragmented structure as well as sequential and chain-resembling nature. Recently, blockchain technology and its benefits have received wide attention and interest. This research synthesizes the research trends and needs of this growing area by means of a bibliometric-qualitative review method. Scopus and Web of Science were selected as the literature databases to retrieve relevant academic publications. Through a systematic literature search and screening, 181 related articles were identified for bibliometric analysis, and 149 publications were critically discussed in a qualitative review. The bibliometric results indicated the recent research regarding blockchain in construction is primarily directed into several clusters, such as "smart contract," "Building Information Modeling (BIM)," "supply chain management," "construction contract," "construction and project management," "digital twin," and "smart city." These clusters were further synthesized for a qualitative review revealing deep insight into research challenges and gaps. Both quantitative and qualitative review results were then mapped to the future directions. It was noted that future research needs to focus on (1) quantifying the cost-benefits of the blockchain applications in construction, e.g., return on investment, practitioners training, and improving industry readiness, (2) integration of blockchain with different project delivery systems, and (3) technology fusion with blockchain for construction management. [ABSTRACT FROM AUTHOR]

Application of emerging digital technology (DT) is an attractive subject of research and is indispensable to the current goal of intelligent construction in many countries. Relevant studies have either focused on the status of a specific DT or proposed preliminary/qualitative comparisons among DTs. This paper provides a systematic understanding of research on applying DTs to the architecture, engineering, construction, and operation (AECO) industry using a mixed-review method. Ten emerging DTs are determined, and publications related to them in 2011–2020 are collected from the Scopus database. The overall status and comparisons of research on 10 emerging DTs by region, journal, and popularity are then presented. The content of research on them is examined through in-depth literature analysis (including research scenario, project stage, and literature evolution in a discipline) and theme classification. Future research directions including technology knowledge, development, application and management, and some practical implications for the country, industry, project, and enterprise, are finally proposed. This research will be informative for academics with regard to the emerging DT's opportunities in this discipline and provide support for work on the digitization of the AECO industry. Some practical implications are proposed from the perspectives of the country, industry, project, and enterprise. For the government, the holistic planning and scientific coordination of emerging DTs need to be highlighted, such as vigorously promoting Building Information Modelling (BIM) and artificial intelligence (AI) and utilizing them to further support the progress of blockchain and digital twin. Establishing demonstration projects to play their radiating roles and accelerate the inter-regional promotion of emerging DTs is also effective. For the industry, it is suggested to build technology alliances to jointly develop integrated platforms covering multiple emerging DTs through in-depth synergy; industry associations are advised to strengthen knowledge diffusion and accumulation for emerging DTs through sharing and training. For projects, applying emerging DTs to the entire life of projects and making them be deeply integrated with off-site, lean, and green construction are advisable. For enterprises, improving the workflow and management model adapted to the application of emerging DTs is necessary. Through adjusting organizational structure and transforming corporate culture, the competitiveness of enterprises could be continuously improved. [ABSTRACT FROM AUTHOR]

Building information modelling (BIM) is the first step towards implementing Building 4.0, where virtual reality and digital twins are key elements. The use of unmanned aircraft systems (UAS/drones) to capture data from buildings is nowadays a very popular method, so a methodology was developed to digitally integrate the photogrammetric surveys of a building into BIM, exclusively with the use of drones. Currently, buildings are responsible for 40% of energy consumption in Europe; therefore, the interconnection between BIM and building energy modelling (BEM) is essential to digitalize the construction sector, increasing competitiveness through cost reduction. In this context, the BlueWoodenHouse Project aims, among other activities, to characterize the solutions/systems of building materials and monitor the temperature, relative humidity and CO2, as well as energy consumption, of a single-family modular wooden house located in the north of Portugal, with 190 m2 and three users. Thus, the experimental monitoring results, of this case study, were used to validate the numerical model developed in the DesignBuilder simulator, which includes the building envelope's 3D geometrical data obtained by one of those aircraft, in order to demonstrate the usefulness of drones for the optimization of solutions, from the energy point of view. [ABSTRACT FROM AUTHOR]

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks. [ABSTRACT FROM AUTHOR]

Reverse engineering and the creation of digital twins are advantageous for documenting, cataloging, and maintenance control tracking in the cultural heritage field. Digital copies of the objects into Building Information Models (BIM) add cultural interest to every artistic work. Low-cost 3D sensors, particularly structured-light scanners, have evolved towards multiple uses in the entertainment market but also as data acquisition and processing techniques for research purposes. Nowadays, with the development of structured-light data capture technologies, the geometry of objects can be recorded in high-resolution 3D datasets at a very low cost. On this basis, this research addresses a small artifact with geometric singularities that is representative of small museum objects. For this, the precision of two structured-light scanners is compared with that of the photogrammetric technique based on short-range image capture: a high-cost Artec Spider 3D scanner, and the low-cost Revopoint POP 3D scanner. Data capture accuracy is evaluated through a mathematical algorithm and point set segmentation to verify the spatial resolution. In addition, the precision of the 3D model is studied through a vector analysis in a BIM environment, an unprecedented analysis until now. The work evaluates the accuracy of the devices through algorithms and the study of point density at the submillimeter scale. Although the results of the 3D geometry may vary in a morphometric analysis depending on the device records, the results demonstrate similar accuracies in that submillimeter range. Photogrammetry achieved an accuracy of 0.70 mm versus the Artec Spider and 0.57 mm against the Revopoint POP 3D scanner. [ABSTRACT FROM AUTHOR]

Railway track maintenance plays an important role in enabling safe, reliable, and seamless train operations and passenger comfort. Due to the increasing rail transportation, rolling stocks tend to run faster and the load tends to increase continuously. As a result, the track deteriorates quicker, and maintenance needs to be performed more frequently. However, more frequent maintenance activities do not guarantee a better overall performance of the railway system. It is crucial for rail infrastructure managers to optimize predictive and preventative maintenance. This study is the world's first to develop deep machine learning models using three-dimensional recurrent neural network-based co-simulation models to predict track geometry parameters in the next year. Different recurrent neural network-based techniques are used to develop predictive models. In addition, a building information modeling (BIM) model is developed to integrate and cross-functionally co-simulate the track geometry measurement with the prediction for predictive and preventative maintenance purposes. From the study, the developed BIM models can be used to exchange information for predictive maintenance. Machine learning models provide the average R2 of 0.95 and the average mean absolute error of 0.56 mm. The insightful breakthrough demonstrates the potential of machine learning and BIM for predictive maintenance, which can promote the safety and cost effectiveness of railway maintenance. [ABSTRACT FROM AUTHOR]

A geometric digital twin (gDT) model capable of leveraging acquired 3D geometric data plays a vital role in digitizing the process of structural health monitoring. This study presents a framework for generating and updating digital twins of existing buildings by inferring semantic information from as-is point clouds (gDT's data) acquired regularly from laser scanners (gDT's connection). The information is stored in updatable Building Information Models (BIMs) as gDT's virtual model, and dimensional outputs are extracted for structural health monitoring (gDT's service) of different structural members and shapes (gDT's physical part). First, geometric information, including position and section shape, is obtained from the acquired point cloud using domain-specific contextual knowledge and supervised classification. Then, structural members' function and section family type is inferred from geometric information. Finally, a BIM is automatically generated or updated as the virtual model of an existing facility and incorporated within the gDT for structural health monitoring. Experiments on real-world construction data are performed to illustrate the efficiency and precision of the proposed model for creating as-is gDT of building structural members. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS) is a leading technology in data acquisition for building information modeling (BIM) applications due to its rapid, direct, and accurate scanning of different objects with high point density. Three-dimensional point cloud classification is essential step for Scan-to-BIM applications that requires high accuracy classification methods, running at reasonable processing time. The classification process is divided into three main steps: neighborhood definition, LiDAR-derived features extraction, and machine learning algorithms being applied to label each LiDAR point. However, the extraction of LiDAR-derived features and training data are time consuming. This research aims to minimize the training data, assess the relevance of sixteen LiDAR-derived geometric features, and select the most contributing features to the classification process. A pointwise classification method based on random forests is applied on the 3D point cloud of a university campus building collected by a TLS system. The results demonstrated that the normalized height feature, which represented the absolute height above ground, was the most significant feature in the classification process with overall accuracy more than 99%. The training data were minimized to about 10% of the whole dataset with achieving the same level of accuracy. The findings of this paper open doors for BIM-related applications such as city digital twins, operation and maintenance of existing structures, and structural health monitoring. [ABSTRACT FROM AUTHOR]

As the scale of foundation pit projects of subway stations in Shenzhen becomes larger, and the construction constraints become more and more complex, there is an urgent need for intelligent monitoring and safety management of foundation pits. In this study, an integrated intelligent approach for monitoring and management of a deep foundation pit in a subway station was proposed and a case study based on the Waterlands Resort East Station Project of Shenzhen Metro Line 12 was used for validation. The present study first proposed the path of intelligent foundation pit engineering. Based on geotechnical survey and building information modeling, a three-dimensional transparent geological model of foundation pit was constructed. Multi-source sensing technologies were integrated, including micro electromechanical system sensing technology, Brillouin optical frequency domain analysis sensing technology, an unmanned aerial vehicle and machine vision for real-time high-precision wireless monitoring of the foundation pit. Moreover, machine learning models were developed for predicting key parameters of foundation pits. Finally, a digital twin integrated platform was developed for the management of the subway foundation pit in both construction and maintenance phases. This typical case study is expected to improve the construction, maintenance and management level of foundation pits in subway stations. [ABSTRACT FROM AUTHOR]

Passage of overweighted commercial vehicles is one of the significant causes of damage to the pavement and structural components of bridges. Weigh-in-motion (WIM) systems can currently detect real-time traffic data; however, these data are stored in standalone databases. Building information modeling (BIM) has transformed the construction industry by injecting "information" into the building model and integrating different databases. BIM capabilities for bilateral exchange of data led to the inception of digital twin. This research investigates the feasibility of developing a digital twin of a mock-up bridge by integrating WIM data into a bridge information model (BrIM). The system was validated by first creating a mock-up bridge with affixed weight sensors attached to microcomputers and then developing a BrIM model and passing scaled vehicles over in real time with varying weight capacities. This study showed the feasibility of creating digital twins, ultimately enabling future research. [ABSTRACT FROM AUTHOR]

The digital twin is considered the central component of modern industry. It has been adopted in many industrial fields. However, its application in nuclear engineering is very rare, especially for the radioactive waste deposits winch is an urgent and tricky issue. Motivated by this demand and considering China's research & development guidelines for geological disposal of high-level radioactive waste (a three-step strategy by 2050 to construct the radioactive waste repository), a framework of the radioactive waste repository digital twin is proposed. The digital twin uses the framework edge + cloud with a multi-layer structure. It can be adopted in the construction of the radioactive waste repository. It can significantly strengthen the management capability, reduce the operating cost, improve the safety level and deal with accidents more efficiently. The first step for the achievement for the digital twin development of radioactive waste repository based on the framework is also introduced in the paper. The proposed digital twin framework of the radioactive waste repository in this work could be widely used as a reference and easily extended to support management in other industrial fields. [ABSTRACT FROM AUTHOR]

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]

Digital twins (DTs) and building information modelling (BIM) are proving to be valuable tools for managing the entire life cycle of a building (LCB), from the early design stages to management and maintenance over time. On the other hand, BIM platforms cannot manage the geometric complexities of existing buildings and the large amount of information that sensors can collect. For this reason, this research proposes a scan-to-BIM process capable of managing high levels of detail (LODs) and information (LOIs) during the design, construction site management, and construction phases. Specific grades of generation (GOGs) were applied to create as-found, as-designed, and as-built models that interact with and support the rehabilitation project of a multi-level residential building. Furthermore, thanks to the sharing of specific APIs (Revit and Autodesk Forge APIs), it was possible to switch from static representations to novel levels of interoperability and interactivity for the user and more advanced forms of building management such as a DT, a BIM cloud, and an extended reality (XR) web platform. Finally, the development of a live app shows how different types of users (professionals and non-expert) can interact with the DT, in order to know the characteristics with which the environments have been designed, as well as the environmental parameters, increasing their degree of control, from the point of view of improving comfort, use, costs, behaviour, and good practices. Finally, the overall approach was verified through a real case study where the BIM-XR platform was built for energy improvements to existing buildings and façade renovations. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management. [ABSTRACT FROM AUTHOR]

Core domains to establish digital twins of the built environment are Building Information Modeling (BIM), Geographic Information System (GIS) and Internet of Things (IoT). RESTful Application Programming Interfaces (APIs) of these domains allow easy access to the digital twin. While multiple information models are reasonable since the focus of these domains is distinct, interoperability becomes a challenge. Therefore, in this paper we propose a hypermedia-driven RESTful API for digital twins of the built environment. While the GIS and IoT interfaces are covered by established standards, we define a RESTful interface for Industry Foundation Classes (IFC) models of the BIM domain. The APIs are interconnected with a federated approach though hypermedia navigation links. An evaluation by two use cases – a planning model and a structural health monitoring application – shows that the API can be used to navigate through the models crossing seamlessly the scope of the domains and overcoming barriers of interoperability. • Exposing IFC models and their items by a hypermedia-driven RESTful API. • Interacting with IFC models and their items using HTTP verbs (e.g., POST, DELETE). • Provisioning of GIM layers from IFC files through OGC API compliant endpoints. • Modeling sensors in IFC and applying a linked model approach to their observations. • Implementation of the API4BE server and client to access BIM, GIM and IoT endpoints. [ABSTRACT FROM AUTHOR]

This paper analyzes the evolution of Artificial Intelligence (AI) in the construction industry from 2014 to 2023, focusing on enhancing security, communication, and maintenance. It combines in-depth analysis of 121 papers with visualizations of 507 articles from major databases such as SCOPUS, IEEE, ACM, Science Direct, and Google Scholar to map AI advancements in construction. The study found that security is established as a mature research domain, whereas communication and maintenance are at comparatively earlier stages of development. Specifically, the analysis reveals a shift from Radio Frequency Identification (RFID) to more sophisticated technologies such as Internet of Things (IoT), Virtual Reality (VR), blockchain, Building Information Modeling (BIM), and digital twins, which significantly improve security. Communication and maintenance have also evolved towards greater digital integration and predictive analytics. The integration of AI innovations with human expertise is emphasized as a strategic direction to enhance decision-making and operational efficiency in construction. [Display omitted] • Review the development of AI-driven studies in the construction sector. • AI evolution in construction security, communication and maintenance (2014–2023). • AI shifts to IoT, VR, blockchain, BIM, digital twins. • Challenges in data management, AI integration noted. • Predictive analytics, ethical AI use in future directions. [ABSTRACT FROM AUTHOR]

In the fight against climate change, and to improve its sustainability standards, the building sector is trying to reduce its energy consumption and environmental footprint. Leveraging digitalization, in the wake of Industry 4.0 and 5.0, holds immense potential in revolutionizing the buildings and construction sector. Digitalization is transforming buildings throughout their life cycle encompassing design, construction, operation, and retrofitting/decommissioning stages. This study aims to analyze the role of digital technologies in sustainable buildings. In doing so, a systematic literature review is conducted to examine the current landscape of the application of 13 key digital technologies - sensors, Building information modeling (BIM), Building Management Systems (BMS), smart meters, 3D printing/additive manufacturing, robotics, big data analytics, Internet of Things (IoT), machine learning, artificial intelligence, digital twin, blockchain, and cyber security - in terms of their scope and prospects in the building sector. These technologies have been discussed considering their functions, applications in buildings, adoption and market trends, challenges, and the way forward. The findings of the study reveal that digital technologies are making buildings more integrated, flexible, energy-efficient, smart, and sustainable by optimizing resource utilization, enhancing operational efficiency, and minimizing environmental impact. For instance, sensors enable real-time data collection, BIM facilitates sustainability assessments, and AI enhances energy management. Further, digital technologies can lead to a 30%–50% improvement in energy intensity in the building sector by 2040. While digital technologies offer promising solutions in terms of improving sustainability standards in buildings, they also face several challenges such as high initial cost, privacy and security risks associated with data breaches and cyber-attacks, and lack of customer awareness and a skilled workforce. Conducive policies and regulations are critical for creating an enabling environment that supports the adoption of digital technologies. Application domains of different digital technologies across a building's lifecycle. [Display omitted] • Explores the contribution of digitalization to sustainable buildings. • Discusses 13 key digital technologies for buildings in terms of functions, applications, and market trends. • Examines the prospects of digital technologies in buildings in terms of opportunities and challenges. • Makes recommendations to improve digitalization in buildings. [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]

Many infrastructure assets in transportation such as roads and bridges represent challenges for inspection and maintenance due to advanced age, structural deficiencies and modifications. Concepts such as Building Information Modelling (BIM) aim to alleviate the problem of health monitoring and asset management by providing digital building models constructed from survey data to all stakeholders. Ageing and oftentimes poorly-documented infrastructure objects such as bridges in particular benefit from a continuous integration of changes to form a digital twin which reflects the asset's as-is state. However, the process of reconstructing geometric–semantic models from survey data is a manual and labour-intensive process and makes continuously updating the models a difficult task. To automate this process, a cross-domain approach using an artificial neural network is presented which performs semantic segmentation in the image domain and transfers the results over to the point cloud. For the following fine segmentation, geometric knowledge in the 3D domain is used for post-processing and filtering via geometric reasoning. Using this method, a 3D semantic segmentation is achieved which does not require any 3D point cloud training data and only a low amount of image training data. • Combination of image-based and point cloud-based segmentation techniques. • Novel method for image-based semantic segmentation of point clouds for scenarios without 3D training data. • Robust transfer of labels from image to point cloud domain respecting the underlying 3D geometry. • Point Cloud fine segmentation based on algorithmic geometry and reasoning for dealing with segmentation errors, outliers and occluded regions. [ABSTRACT FROM AUTHOR]

Digitalization of buildings and the use of IoT sensing and metering devices are steadily increasing, offering new opportunities for more autonomous, efficient, and flexible buildings. As part of this transformation and inspired by the added value demonstrated in other domains, the concept of a building digital twin that can monitor, simulate, manage, and optimize building operation has received increased interest. To aid such digital twin implementations, accurate and adaptable simulation models are required, which can effectively integrate and utilize the available data. However, traditional building modeling and digital practices, such as Building Information Modeling and white-box modeling tools, are not easily compatible with these requirements. This work presents an innovative and flexible energy modeling framework based on the SAREF ontology. With a basis in the SAREF4BLDG extension for buildings and the defined classes, different models are presented for a selection of typical systems and devices such as spaces, space heaters, dampers, coils, etc. Using the generic semantics and relations of the SAREF4SYST extension, a method for linking and simulating component models is then presented. A proof-of-concept of the modeling framework is provided, showing its application and feasibility to provide a dynamic simulation of the different systems and devices included in a demonstration case. Finally, a future line of work is identified considering the implementation of the modeling framework in an actual building case study, including integration with actual sensing equipment to demonstrate different digital twin services such as performance monitoring, strategy planning, and operational optimization. • An innovative energy modeling framework for building digital twins is presented. • The framework is built directly upon the SAREF ontology to improve interoperability. • Dynamic component models are identified for a selection SAREF4BLDG classes. • Algorithms are presented to link models and enable system simulation. • A proof-of-concept is provided through implementation on a demonstration case. [ABSTRACT FROM AUTHOR]

The classification of the energy efficiency of buildings with the use of the energy performance certificate, constitutes a common practice for the building energy assessment performance community. The main stream in the field is the so-called asset rating, that is the calculation of the energy performance of buildings, whereas another option for the definition of the energy class of a building relies on its measured performance, named operational rating. Despite the fact that significant progress has been achieved in the field of smart sensors and smart buildings, effective solutions related to the employment of Industry 4.0 practices, such as Building Information Modelling (BIM) or digital twins are not employed for the operational rating of buildings energy performance. This study aspires to introduce a comprehensive framework for the operational assessment of buildings energy performance, with the use of advanced tools and practices for building digitization. For the purpose of this study, a relevant set of operational indicators was employed, with the purpose to provide information, both to the landlords and the tenants, related to the actual energy performance of the building. The novelty of this study relies on the fact that for the extraction of the operational rating, a digital twin environment was used, employing smart sensors, real time measurements and a BIM environment. Within this study, the proposed concepts are demonstrated in a case study building of mixed used. This study aims to shed light to the development of practices for the remote and smart real time energy assessment of building units. [ABSTRACT FROM AUTHOR]

• A novel Digital Twin framework is presented to detect occupant discomfort. • The Digital Twin approach integrates BIM with sensor data & occupant feedback. • Bayesian network used to evaluate occupant comfort in non-residential buildings. • Nine ML algorithms are used for predictive maintenance. • XGB is up to 5% more accurate than other algorithms while RF is 96% faster than XGB. This study introduces a Bayesian network model to evaluate the comfort levels of occupants of two non-residential Norwegian buildings based on data collected from satisfaction surveys and building performance parameters. A Digital Twin approach is proposed to integrate building information modeling (BIM) with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort. The study also uses 200000 points as historical data of various sensors to understand the previous building systems' behavior. The study also presents new methods for using BIM as a visualization platform and for predictive maintenance to identify and address problems in the HVAC system. For predictive maintenance, nine machine learning algorithms were evaluated using metrics such as ROC, accuracy, F1-score, precision, and recall, where Extreme Gradient Boosting (XGB) was the best algorithm for prediction. XGB is on average 2.5% more accurate than Multi-Layer Perceptron (MLP), and up to 5% more accurate than the other models. Random Forest is around 96% faster than XGBoost while being relatively easier to implement. The paper introduces a novel method that utilizes several standards to determine the remaining useful life of HVAC, leading to a potential increase in its lifetime by at least 10% and resulting in significant cost savings. The result shows that the most important factors that affect occupant comfort are poor air quality, lack of natural light, and uncomfortable temperature. To address the challenge of applying these methods to a wide range of buildings, the study proposes a framework using ontology graphs to integrate data from different systems, including FM, CMMS, BMS, and BIM. This study's results provide insight into the factors that influence occupant comfort, help to expedite identifying equipment malfunctions and point towards potential solutions, leading to more sustainable and energy-efficient buildings. [ABSTRACT FROM AUTHOR]

Increasing reliance on automation and robotization presents great opportunities to improve the management of construction sites as well as existing buildings. Crucial in the use of robots in a built environment is their capacity to locate themselves and navigate as autonomously as possible. Robots often rely on planar and 3D laser scanners for that purpose, and building information models (BIM) are seldom used, for a number of reasons, namely their unreliability, unavailability, and mismatch with localization algorithms used in robots. However, while BIM models are becoming increasingly reliable and more commonly available in more standard data formats (JSON, XML, RDF), they become more promising and reliable resources for localization and indoor navigation, in particular in the more static types of existing infrastructure (existing buildings). In this article, we specifically investigate to what extent and how such building data can be used for such robot navigation. Data flows are built from BIM model to local repository and further to the robot, making use of graph data models (RDF) and JSON data formats. The local repository can hereby be considered to be a digital twin of the real-world building. Navigation on the basis of a BIM model is tested in a real world environment (university building) using a standard robot navigation technology stack. We conclude that it is possible to rely on BIM data and we outline different data flows from BIM model to digital twin and to robot. Future work can focus on (1) making building data models more reliable and standard (modelling guidelines and robot world model), (2) improving the ways in which building features in the digital building model can be recognized in 3D point clouds observed by the robots, and (3) investigating possibilities to update the BIM model based on robot feedback. [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]

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth analysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants' feedback, a probabilistic model of occupants' comfort, and HVAC faults detection and prediction that may affect occupants' comfort. New methods for using BIM as a visualization platform, as well as a predictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants' comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integration among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunctions, and pointing toward possible solutions. [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]

The road construction industry is undergoing a continuous digital transformation due to higher construction quality control and management requirements to improve efficiency and reduce cost consumption. IC (Intelligent compaction) is an emerging and developing technology that can overcome the limitations of current quality control and quality assurance approaches, such as detection hysteresis and incompleteness. However, IC is mainly used in a vehicle system to realize real-time quality inspection, while its further potential is not fully exerted in advanced construction control and management as a comprehensive, continuous and nondestructive testing tool. This paper proposes a BIM (Building Information Model)-IoT (Internet of things) and IC integrated framework to provide a fundamental platform for advanced compaction quality monitoring and management. Specifically, BIM provides high-fidelity virtual assets of road infrastructure for data integration, visualization and construction organization management. IC is used to provide comprehensive compaction quality monitoring data and quality control methods. IoT (Internet of things) is adopted to connect physical and cyber assets by capturing real-time data from the actual operations in construction. Finally, a prototype system is developed as an implementation of the framework. The case demonstration of the system shows that the proposed framework can effectively integrate BIM and IC to realize the monitoring of road compaction quality and data in combination with IoT data, and the construction schedule can be rearranged and optimized based on the monitoring results. This study can facilitate to realize road compaction quality real-time monitoring and management, as well to offer a basic BIM-IoT platform that can be utilized as a prototype of road infrastructure digital twin to achieve more intelligent and sustainable management of the entire construction process in the future. [ABSTRACT FROM AUTHOR]