Literature Review of Digital Twins Applications in Construction Workforce Safety

Integrating FRAM and BN for enhanced resilience evaluation in construction emergency response: A scaffold collapse case study

The construction system's complexity can generate substantial uncertainties during emergencies. Resilience, as a new perspective on emergency response, can significantly mitigate these challenges. This paper introduces an innovative model to assess the resilience of construction emergency response processes utilizing a scaffold collapse scenario as a demonstrative case study. Grounded in resilience engineering, our model integrates the merits of the Functional Resonance Analysis Method (FRAM) with the probabilistic strengths of Bayesian Networks (BNs). The process commences with FRAM, mapping out the emergency response in qualitative terms by identifying functions, variabilities, and couplings. This culminates in a topological network which serves as a foundational structure for the directed Complex Network (CN) and the BN model. Thereafter, the Delphi method and the modified K-shell (MKS) decomposition algorithm guide the computation of prior probabilities for root nodes and the conditional probability table within the BN model. Subsequently, the BN model is subjected to a simulation using the AgenaRisk software, executing both forward and backward propagation as well as sensitivity analyses. Our findings pinpoint "Intersectoral Coordination and Linkage" as the most crucial function, with rapidity being the most sensitive aspect influencing resilience during a scaffold collapse emergency response process.

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers' unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers' behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers' behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers' unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers' behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers' behavior monitoring and management.

A systematic review of the application of immersive technologies for safety and health management in the construction sector

INTRODUCTION

The construction industry employs about 7% of global manpower and contributes about 6% to the global economy. However, statistics have depicted that the construction industry contributes significantly to workplace fatalities and injuries despite multiple interventions (including technological applications) implemented by governments and construction companies. Recently, immersive technologies as part of a suite of industry 4.0 technologies, have also strongly emerged as a viable pathway to help address poor construction occupational safety and health (OSH) performance.

METHOD

With the aim of gaining a broad view of different construction OSH issues addressed using immersive technologies, a review on the application of immersive technologies for construction OSH management is conducted using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) approach and bibliometric analysis of literature. This resulted in the evaluation of 117 relevant papers collected from three online databases (Scopus, Web of Science, and Engineering Village).

RESULTS

The review revealed that literature have focused on the application of various immersive technologies for hazard identification and visualization, safety training, design for safety, risk perception, and assessment in various construction works. The review identified several limitations regarding the use of immersive technologies, which include the low level of adoption of the developed immersive technologies for OSH management by the construction industry, very limited research on the application of immersive technologies for health hazards, and limited focus on the comparison of the effectiveness of various immersive technologies for construction OSH management.

CONCLUSIONS AND PRACTICAL APPLICATIONS

For future research, it is recommended to identify possible reasons for the low transition level from research to industry practice and proffer solutions to the identified issues. Another recommendation is the study of the effectiveness of the use of immersive technologies for addressing health hazards in comparison to the conventional methods.

Construction and Maintenance of Building Geometric Digital Twins: State of the Art Review

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.

Lightweight Neural Networks-Based Safety Evaluation for Smart Construction Devices

Based on the theory of lightweight neural networks, this paper presents a safety evaluation model for smart construction devices. The model index system includes the internal logical relationship between the input and output indexes, and the input indexes are specifically refined. According to the safety evaluation results, the article observes what type of accidents will occur at the construction site. According to the detailed and specific output index system, the six input factor layer indicators correspond to the indicators of several multiple network index layers, respectively. In the simulation process, MATLAB software was used to write the multiple neural network model program for the safety evaluation of the construction site, and the appropriate multiple network structure and related parameters were selected. The experimental results show that the multiple neural networks are trained by collecting 10 expert evaluation samples, and the trained multiple neural networks are applied to real construction cases. Comparing the two sets of data, it can be seen that the gap is relatively small, and the sample training is better. The multiple neural networks have relatively good evaluation performance. The method has a fast calculation speed and effectively improves the efficiency and practical value of safety evaluation.

Using a Digital Twin to Study the Influence of Climatic Changes on High Ozone Levels in Bulgaria and Europe

High concentration levels of air pollutants may cause damage to plants, animals, and the health of some groups of human beings. Therefore, it is important to investigate different topics related to the high air pollution levels and to find reliable answers to the questions about the possible damages, which might take place when these levels exceed some limits. A few of the numerous questions, the answers of which are highly desirable, are listed below: (a) When are the air pollution levels dangerous? (b) What is the reason for the increased air pollution levels? (c) How can the air pollution levels be decreased? (d) Will the future climate changes result in higher and more dangerous air pollution levels? It is necessary to study carefully many issues connected with the distribution of air pollutants in a given region and with the reasons for the increases of the concentrations to high levels, which might be damaging. In order to do this, it is necessary to develop a Digital Twin of all relevant physical processes in the atmosphere and to use after that this tool in different applications. Such a tool, its name is DIGITAL AIR, has been created. Digital Twins are becoming more and more popular). Many complex problems, arising taking place in very complicated surroundings, can be handled and resolved successfully by applying Digital Twins. The preparation of such a digital tool as well as its practical implementation in the treatment of a special problem, the increase of some potentially dangerous ozone levels, will be discussed and tested in this paper. The Unified Danish Eulerian Model (UNI-DEM) is a very important part of DIGITAL AIR. This mathematical model, UNI-DEM, can be applied in many different studies related to damaging effects caused by high air pollution levels. We shall use it in this paper to get a reliable answer to a very special but extremely important question: will the future climatic changes lead to an increase in the ozone pollution levels in Bulgaria and Europe, which can potentially become dangerous for human health?

An Off-Site Construction Digital Twin Assessment Framework Using Wood Panelized Construction as a Case Study

Off-site construction is an innovative type of construction with the philosophy of standardizing the process and deploying the latest technological enablers. Many technologies, such as the Building Information Model (BIM), Internet of Things (IoT), etc., are concerned with virtual representation and manipulation of the physical site. However, a holistic view of the off-site construction processes is lacking in the exploration of the technological advances, resulting in inconsistency when applying these advances in practice. The concept of Digital Twin is useful for addressing this challenge. Digital Twin is a philosophy and a collection of technologies aimed toward seamless physical and virtual connections. Therefore, a holistic Off-site Construction Digital Twin model is necessary for any research concerning this topic, and an assessment framework is useful in helping off-site construction industry companies in approaching systematic Digital Twin. This research first proposes a model for Off-site Construction Digital Twin. To quantify this model, an assessment tool named Off-site Construction Digital Twin Maturity Level is proposed. The validation and evaluation of this assessment framework are conducted through a case study with ACQBuilt, an off-site construction company in Edmonton, Canada. The resulting assessment framework contributes to the body of knowledge in two ways: Firstly, it sets the foundation for an Off-site Construction Digital Twin, which is anticipated to significantly reduce waste and to improve efficiency. Secondly, it enables easier technology application in practice by offering a holistic Digital Twin framework.

Digital Twin-Based Intelligent Safety Risks Prediction of Prefabricated Construction Hoisting

Prefabricated construction hoisting has one of the highest rates of fatalities and injuries compared to other construction processes, despite technological advancements and implementations of safety initiatives. Current safety risk management frameworks lack tools that are able to process in-situ data efficiently and predict risk in advance, which makes it difficult to guarantee the safety of hoisting. Thus, this article proposed an intelligent safety risk prediction framework of prefabricated construction hoisting. It can predict the hoisting risk in real-time and investigate the spatial-temporal evolution law of the risk. Firstly, the multi-dimensional and multi-scale Digital Twin model is built by collecting the hoisting information. Secondly, a Digital Twin-Support Vector Machine (DT-SVM) algorithm is proposed to process the data stored in the virtual model and collected on the site. A case study of a prefabricated construction project reveals its prediction function and deduces the spatial-temporal evolution law of hoisting risk. The proposed method has made advancements in improving the safety management level of prefabricated hoisting. Moreover, the proposed method is able to identify the deficiencies regarding digital-twin-level control methods, which can be improved towards automatic controls in future studies.

Mechanical Performance of 3D Printed Concrete in Steam Curing Conditions

Three-dimensional (3D) concrete printing (3DCP) technology attracts significant attention from research and industry. Moreover, adequate mechanical performance is one of the primary properties for materials, meeting the demand of structural safety using 3DCP technology. However, research on curing conditions as the significant influence factor of mechanical capacity is required to accelerate the practical application of 3DCP technology. This study aims to explore the impact of various steam curing conditions (heating rate, constant temperature time, and constant temperature) on the mechanical performance of printed concrete containing solid wastes. Moreover, the optimal steam curing conditions are obtained for compressive, tensile, and flexural properties in different directions. Subsequently, anisotropies in the mechanical properties of printed composites and interlayer bonding behaviors are investigated when various curing conditions are employed. The result shows that steam curing conditions and solid waste incorporation improves the interlayer bond for 3D printed cement-based composites.

Printable and Mechanical Performance of 3D Printed Concrete Employing Multiple Industrial Wastes

Three-dimensional concrete printing is a promising technology and attracts the significant attention of research and industry. However, printable and mechanical capacities are required for 3D printable cementitious materials. Moreover, the quantitative analysis methods of printable performance are limited and have low sensitivity. In this study, the orthogonal experiment through samples combining 3D concrete printing method with fly ash, silica fume, and ground granulated blast furnace slag was designed to obtain the printable and mechanical property influence of various mix proportions. Furthermore, multiple industrial wastes were utilized to improve material sustainability. Meanwhile, the static and dynamic extrusion pressure measured by the original 3D printing extrudability tester were verified to achieve a high-sensitivity evaluating indicator. Thereby, a novel high-sensitivity quantitative analysis method of printable capacity was established to explore the influence of industrial wastes usage on the printability of 3D printable mortars. The optimum dosage of fly ash, silica fume, and ground granulated blast furnace slag was 20 wt.%, 15 wt.%, and 10 wt.%, respectively, based on printable and mechanical property experiments. Furthermore, the optimum dosage was employed to print the sample and achieved a higher compressive strength (56.3 MPa) than the control cast.

Combined Utilization of Construction and Demolition Waste and Propylene Fiber in Cement-Stabilized Soil

Construction and demolition (C&D) waste has become a research hotspot due to the need for environmental sustainability and strength enhancement of cementitious materials. However, wider applications of C&D waste are limited, as its non-homogeneous surface nature limits its workability. This research evaluated the feasible utilization of C&D waste as aggregates in polypropylene-fiber-reinforced cement-stabilized soil (CSS) under sulfate-alkali activation. CSS specimens incorporated Portland cement and C&D waste in 10%, 20%, and 30% proportions. Also, polypropylene fiber after alkali activation by sodium sulfate (at 0.2%, 0.4%, and 0.8% dosing level) was defined as 1%, 2%, and 4%. Strength enhancement was examined through unconfined compressive strength (UCS) and flexural strength tests at 7, 14 and 28 days. Test results indicated that mechanical properties showed significant improvement with increasing levels of Portland cement and sodium sulfate, while the improvement dropped after excessive addition of C&D waste and polypropylene fiber. Optimal proportioning was determined as 30%, 4%, 20%, and 0.8% for Portland cement, polypropylene fiber, C&D waste, and sodium sulfate, respectively. Scanning electron microscope (SEM) analysis attributed the enhancement to hydration product (ettringite) formation, bridging effect and increased particle friction. Additionally, the decrease in amplification was ascribed to the destruction of interface transition-zone (ITZ) strength, resulting in premature failure.

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

Intelligent Prediction of Prestressed Steel Structure Construction Safety Based on BP Neural Network

In the construction process of a prestressed steel structure, it is a point of research interest to obtain the safety state of the structure according to the design parameters and working conditions of the structure. The intelligent prediction of structural construction safety provides the basis for safety control. This study proposes an intelligent prediction method of structural construction safety based on a back propagation (BP) neural network. Firstly, the correlation mechanism of structural construction safety performance parameters is established, which involves structural design parameters and mechanical parameters. According to the basic principle of a BP neural network, the relationship between design parameters and mechanical parameters is captured. The virtual model of a structure construction process is established based on digital twins (DTs). The DTs and BP neural network are combined to form a structural safety intelligent prediction framework and theoretical method, setting working conditions in a twin model to obtain mechanical parameters. Mechanical parameters are intelligently predicted by design parameters in neural networks. The safety performance of structure construction is evaluated according to mechanical parameters. Finally, the intelligent prediction method is applied to the construction process of string beam. Based on DTs and BP neural network, the intelligent analysis of structural construction safety is carried out. This provides a reliable basis for safety control. The feasibility of this research method is verified by comparing the predicted results of the theoretical method with the measured data on site.

Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review

Digital twin (DT) is gaining increasing attention due to its ability to present digital replicas of existing assets, processes and systems. DT can integrate artificial intelligence, machine learning, and data analytics to create real-time simulation models. These models learn and update from multiple data sources to predict their physical counterparts’ current and future conditions. This has promoted its relevance in various industries, including the construction industry (CI). However, recognising the existence of a distinct set of factors driving its adoption has not been established. Therefore, this study aims to identify the drivers and integrate them into a classification framework to enhance its understanding. Utilising popular databases, including Scopus, Web of Science, and ScienceDirect, a systematic literature review of 58 relevant DT adoptions in the CI research was conducted. From the review, the drivers for DT adoption in the CI were identified and classified. The results show that developed countries such as the UK, US, Australia, and Italy have been the top countries in advancing DT adoption in the CI, while developing countries have made commendable contributions. A conceptual framework has been developed to enhance the successful adoption of DT in the CI based on 50 identified drivers. The major categories of the framework include concept-oriented drivers, production-driven drivers, operational success drivers, and preservation-driven drivers. The developed framework serves as a guide to propel DT adoption in the CI. Furthermore, this study contributes to the body of knowledge about DT adoption drivers, which is essential for DT promotion in the CI.

A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics

Digital twins offer a unique opportunity to design, test, deploy, monitor, and control real-world robotic processes. In this paper we present a novel, modular digital twinning framework developed for the investigation of safety within collaborative robotic manufacturing processes. The modular architecture supports scalable representations of user-defined cyber-physical environments, and tools for safety analysis and control. This versatile research tool facilitates the creation of mixed environments of Digital Models, Digital Shadows, and Digital Twins, whilst standardising communication and physical system representation across different hardware platforms. The framework is demonstrated as applied to an industrial case-study focused on the safety assurance of a collaborative robotic manufacturing process. We describe the creation of a digital twin scenario, consisting of individual digital twins of entities in the manufacturing case study, and the application of a synthesised safety controller from our wider work. We show how the framework is able to provide adequate evidence to virtually assess safety claims made against the safety controller using a supporting validation module and testing strategy. The implementation, evidence and safety investigation is presented and discussed, raising exciting possibilities for the use of digital twins in robotic safety assurance.

A Parametric Scan-to-FEM Framework for the Digital Twin Generation of Historic Masonry Structures

Historic masonry buildings are characterised by uniqueness, which is intrinsically present in their building techniques, morphological features, architectural decorations, artworks, etc. From the modelling point of view, the degree of detail reached on transforming discrete digital representations of historic buildings, e.g., point clouds, into 3D objects and elements strongly depends on the final purpose of the project. For instance, structural engineers involved in the conservation process of built heritage aim to represent the structural system rigorously, neglecting architectural decorations and other details. Following this principle, the software industry is focusing on the definition of a parametric modelling approach, which allows performing the transition from half-raw survey data (point clouds) to geometrical entities in nearly no time. In this paper, a novel parametric Scan-to-FEM approach suitable for architectural heritage is presented. The proposed strategy uses the Generative Programming paradigm implementing a modelling framework into a visual programming environment. Such an approach starts from the 3D survey of the case-study structure and culminates with the definition of a detailed finite element model that can be exploited to predict future scenarios. This approach is appropriate for architectural heritage characterised by symmetries, repetition of modules and architectural orders, making the Scan-to-FEM transition fast and efficient. A Portuguese monument is adopted as a pilot case to validate the proposed procedure. In order to obtain a proper digital twin of this structure, the generated parametric model is imported into an FE environment and then calibrated via an inverse dynamic problem, using as reference metrics the modal properties identified from field acceleration data recorded before and after a retrofitting intervention. After assessing the effectiveness of the strengthening measures, the digital twin ability of reproducing past and future damage scenarios of the church is validated through nonlinear static analyses.

A Research Framework of Mitigating Construction Accidents in High-Rise Building Projects via Integrating Building Information Modeling with Emerging Digital Technologies

The construction of high-rise building projects is a dangerous vocation due to the uniqueness and nature of the activities, as well as the complexity of the working environment, yet safety issues remain crucial in the construction industry. Digital technologies, such as building information modeling (BIM), have been identified as valuable tools for increasing construction productivity, efficiency, and safety. This research aimed to mitigate the accident safety factors in high-rise building projects via integrating BIM with emerging digital technologies in the construction industry, such as photogrammetry, GPS, RFID, augmented reality, (AR), virtual reality (VR), and drone technology. Qualitative research was conceived in the ground theory approach. Forty-five online interviews with construction stakeholders and qualitative data analysis were carried out using the NVivo 11 software package. According to the findings, interviewees were more motivated to use photogrammetry and drone technologies in high-rise building projects in order to increase construction safety. Positive, negative, and neutral attitudes about BIM integration with emerging digital technologies were discovered. Furthermore, a research framework was developed by consolidating research findings that articulate the measures and future needs of BIM integration with other digital technologies to mitigate construction accidents in high-rise building projects. The framework also renders practical references for industry practitioners towards effective and safer construction.

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies.

How Digital Twin Concept Supports Internal Transport Systems?—Literature Review

In the Industry 4.0 era, the Digital Twin has become one of the most promising enabling technologies supporting material flow. Although the literature on the Digital Twin is becoming relatively well explored, including a certain number of review papers, the context of the Digital Twins application in internal transport systems has not been investigated so far. This paper thoroughly reviews the research on the Digital Twins applied in internal transport systems concerning major research trends within this research area and identification of future research directions. It provides clarification of various definitions related to the Digital Twin concept, including misconceptions such as a digital shadow, a digital model, and a digital mirror. Additionally, the relationships between terms such as material handling, material flow, and intralogistics in the context of internal transport systems coupled with the Digital Twin are explained. This paper’s contribution to the current state of the art of the Digital Twins is three-fold: (1) recognition of the most influential and high-impact journals, papers, and researchers; (2) identification of the major research trends related to the Digital Twins applications in internal transport systems, and (3) presentation of future research agendas in investigating Digital Twins applied for internal transport systems.

An Adapted Model of Cognitive Digital Twins for Building Lifecycle Management

In the digital transformation era in the Architecture, Engineering, and Construction (AEC) industry, Cognitive Digital Twins (CDT) are introduced as part of the next level of process automation and control towards Construction 4.0. CDT incorporates cognitive abilities to detect complex and unpredictable actions and reason about dynamic process optimization strategies to support decision-making in building lifecycle management (BLM). Nevertheless, there is a lack of understanding of the real impact of CDT integration, Machine Learning (ML), Cyber-Physical Systems (CPS), Big Data, Artificial Intelligence (AI), and Internet of Things (IoT), all connected to self-learning hybrid models with proactive cognitive capabilities for different phases of the building asset lifecycle. This study investigates the applicability, interoperability, and integrability of an adapted model of CDT for BLM to identify and close this gap. Surveys of industry experts were performed focusing on life cycle-centric applicability, interoperability, and the CDT model’s integration in practice besides decision support capabilities and AEC industry insights. The evaluation of the adapted model of CDT model support approaching the development of CDT for process optimization and decision-making purposes, as well as integrability enablers confirms progression towards Construction 4.0.

Building Information Modelling and Internet of Things Integration for Facility Management—Literature Review and Future Needs

Digitisation of the built environment is seen as a significant factor for innovation in the Architecture, Engineering, Construction and Operation sector. However, lack of data and information in as-built digital models considerably limits the potential of Building Information Modelling in Facility Management. Therefore, optimisation of data collection and management is needed, all the more so now that Industry 4.0 has widened the use of sensors into buildings and infrastructures. A literature review on the two main pillars of digitalisation in construction, Building Information Modelling and Internet of Things, is presented, along with a bibliographic analysis of two citations and abstracts databases focusing on the operations stage. The bibliographic research has been carried out using Web of Science and Scopus databases. The article is aimed at providing a detailed analysis of BIM–IoT integration for Facility Management (FM) process improvements. Issues, opportunities and areas where further research efforts are required are outlined. Finally, four key areas of further research development in FM management have been proposed, focusing on optimising data collection and management.

Digitization, Digital Twins, Blockchain, and Industry 4.0 as Elements of Management Process in Enterprises in the Energy Sector

In the 21st century, it is becoming increasingly clear that human activities and the activities of enterprises affect the environment. Therefore, it is important to learn about the methods in which companies minimize the negative effects of their activities. The article presents the steps taken and innovative actions carried out by enterprises in the energy sector. The article analyzes innovative activities undertaken and implemented by enterprises from the energy sector. The relationships between innovative strategies, including, inter alia, digitization, and Industry 4.0 solutions, in the development of companies and the achieved results concerning sustainable development and environmental impact. Digitization has far exceeded traditional productivity improvement ranges of 3–5% per year, with a clear cost improvement potential of well above 25%. Enterprises on a large scale make attempts to increase energy efficiency by implementing the state-of-the-art innovative technical and technological solutions, which increase reliability and durability (material and mechanical engineering). Digitization of energy companies allows them to reduce operating costs and increases efficiency. With digital advances, the useful life of an energy plant can be increased up to 30%. Advanced technologies, blockchain, and the use of intelligent networks enables the activation of prosumers in the electricity market. Reducing energy consumption in industry and at the same time increasing energy efficiency for which the European Union is fighting in the clean air package for all Europeans have a positive impact on environmental protection, sustainable development, and the implementation of the decarbonization program.

Integration of BIM and Immersive Technologies for AEC: A Scientometric-SWOT Analysis and Critical Content Review

With the outset of Industrial Revolution 4.0 (IR 4.0), every sector is escalating to get enrichment out of it, whether they are research- or industry-oriented. The Architecture Engineering and Construction (AEC) industry lags a bit in adopting it because of its multi-faceted dependencies and unique nature of work. Despite this, a trend has been seen recently to hone the IR 4.0 multitudes in the AEC industry. The upsurge has been seen in the usage of Immersive Technologies (ImTs) as one of the disruptive techniques. This paper studies the literature based on ImTs, which are Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) integrating with Building Information Modelling (BIM) in the AEC sector. A total number of 444 articles were selected from Scopus following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) protocol of reviewing the literature. Among the selected database, 64 papers are identified as the result of following the protocol, and the articles are divided into eight domains relevant to the AEC industry, namely client/stakeholder, design exploration, design analysis, construction planning, construction monitoring, construction health/safety, facility/management, and education/training. This study adopts both a scientometric analysis for bibliometrics visualization and a critical review using Strength Weakness Opportunity Threat (SWOT) analysis for finding gaps and state of play. The novelty of this paper lies in the analysis techniques used in the literature to provide an insight into the literature, and it provides directions for the future with an emphasis on developing sustainable development goals (SDGs). In addition, research directions for the future growth on the adoption of ImTs are identified and presented based on categorization in immersive devices, graphical/non-graphical data and, responsive/integrative processes. In addition, five subcategories for each direction are listed, citing the limitations and future/needs. This study presents the roadmap for the successful adoption of ImTs for industry practitioners and stakeholders in the AEC industry for various domains. The paper shows that there are studies on ImTs with or without BIM; however, future studies should focus on the usage of ImTs in various sectors such as modular integrated construction (MiC) or emerging needs such as SDGs.

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.