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Smart construction is gaining attention as a solution to improve the construction industry's productivity and safety problems. Although there have been many actions to activate the utilization of smart construction technologies, their effectiveness remains unclear. Diagnosing the current status of the smart construction industry is fundamental, eventually establishing a diagnosis-prescription-improvement virtuous cycle. However, previous attempts for such diagnosis were limited to the confined scope (e.g., major contractors only) and the subjective judgment of technological capacity, primarily caused by the lack of comprehensive background data. Therefore, this study aims to investigate the current status of smart construction in Korea. Specifically, the scope of smart construction technologies was defined. This study then investigated diverse aspects of smart construction, including market trends, technological maturities, research and developments, and site applications. Based on the results, the smart construction technologies were classified into three categories: 1) similar to the global trends; 2) lacking; and 3) leading in the domestic markets. The directions for future studies were also suggested to develop a performance evaluation indicator for smart construction to understand trends and capacities in time. The study results will provide policymakers and practitioners with background information to establish strategies for advancing smart construction in Korea. [ABSTRACT FROM AUTHOR]

The application of smart construction technologies is becoming a crucial approach to improving safety performance in the construction industry. Existing research often focuses on the qualitative analysis of specific technologies, neglecting a comprehensive empirical examination of the characteristics of smart construction technologies and their impact on project safety performance. This study identifies five principal characteristics of smart construction technology: integration, automation, initiative, shareability, and sustainability. By integrating the technology acceptance model (TAM) and the task–technology fit (TTF) model, we propose a theoretical path of "technology characteristics–perceived behavior–behavioral intention–safety performance" and develop a structural equation model to assess the effect of these characteristics on safety performance. The results indicate that these characteristics have a significant direct impact on safety performance, with the estimated direct effect reaching as high as 0.61. Additionally, these characteristics positively influence safety performance indirectly through mediating variables such as perceived ease of use, perceived usefulness, usage intention, and usage behavior. This study empirically verifies the pathways through which smart construction technology characteristics affect safety performance, providing theoretical support for the widespread application of these technologies in construction engineering and enhancing safety management. [ABSTRACT FROM AUTHOR]

Under the influence of pervasive digital revolution, the accessibility and analysis of 'big data' can provide useful insights and help various industries evolve. Despite the popularity of big data, the construction industry is lagging behind other industries in adopting big data technologies. This paper fills the knowledge gap by examining the status quo of big data adoption in companies with different sizes and roles, as well as that in projects with different types, and ascertaining the drivers for and challenges in adopting big data. This paper employed a structured questionnaire survey and statistical analyses to investigate the significance of factors influencing the drivers, challenges, and enhancement strategies of big data adoption, and validated the results with post-study interviews with construction professionals. The results show that big data adoption in the construction industry is affected by the size of companies and the work experience of their employees. Technology advancement, competitiveness, and government plan and policy initiatives are identified as the top three drivers of big data adoption in the construction sector. Moreover, a lack of appropriate supporting systems, difficulties in data collection, and the shortage of knowledge and experience are found to be the major challenges in big data adoption. Finally, the identified top three strategies for overcoming these challenges and promoting big data adoption are 'clear organization structure', 'government incentives', and 'the training of information technology (IT) personnel'. The paper suggests the necessity of creating differentiated strategies for big data adoption for companies with different scales and roles, and helps provide useful insights for policy-makers in promoting big data applications. [ABSTRACT FROM AUTHOR]

Ensuring the safety of workers and machinery during operations is a critical task in the construction sites. However, an inevitable circumstance in construction sites is the complex and dynamic environment, which often leads to occlusions. When detecting occluded objects in construction sites, general vision-based approaches tend to exhibit lower accuracy and may even miss detections, resulting in potential safety hazards. To handle this issue, this paper proposes a vision-based approach for detecting occluded objects in construction sites. Firstly, the proposed detection algorithm adopts the state-of-the-art YOLOv7 as its backbone. To enhance its capability in capturing contextual information of occluded objects, a novel channel attention mechanism is employed. Then, a design scheme for the detector head is provided by integrating a novel loss function Scylla-Intersection over Union (SIoU) and the non-maximum suppression (NMS) strategy. With the help of the loss function SIoU, the network can compute the loss values of occluded objects more accurately. To ensure that the network can select the right predicted box which closely aligns with the ground truth, the Euclidean distance is utilized as spatial penalty factor during the NMS stage. By implementing these two strategies, the proposed method can preserve both the category information and bounding boxes of occluded objects, which makes them possible to be detected. Finally, detailed experiments are done to verify the proposed method. Experimental results demonstrate that the proposed method has the potential for improving the detection accuracy. Moreover, it shows a better performance in detecting occluded objects in the dynamic construction sites compared to the existing baselines. [ABSTRACT FROM AUTHOR]

Urbanization is one of the biggest challenges in the world today. Sixty-eight percent of the world's population will reside in cities and urban areas by 2050. Transitioning to a smart city is the key to overcoming the urbanization challenges. This study aimed to develop a smart city rating system that determines the overall smartness score of Riyadh City. A mixed method approach was used: qualitative research involved interviews, whereas quantitative analysis utilized the fuzzy analytic hierarchy process (AHP) and multiple-criteria decision analysis (MCDA) to determine city smartness scores using main pillars and subfactors. The findings showed that Riyadh's smart city development is influenced by several factors. The smart economy carries a weight of 17.8% and a smartness score of 74.15%, while smart people holds a weight of 8.1% and a score of 59.79%. Culture diversity, education, and the Human Development Index emerge as the most dominant subfactors. Smart living carries a weight of 8.2% and a smartness score of 51.86%, with health and security being crucial aspects of human life. Smart governance carries a weight of 17.8% and a smartness score of 81.451%, with smart business services, transparency, open communication, and collaboration being the most important factors. Smart urban services hold a weight of 17.9% and a score of 43%, with transport, logistics, urban planning, infrastructure, smart parking, digital monitoring, and safety being the most dominant subfactors. Smart construction carries a weight of 3.9% and a smartness score of 43.83%, with sustainable houses and smart buildings being the most dominant subfactors. Smart mobility holds a weight of 18% and a smartness score of 52.34%, with congestion, accidents, transport efficiency, and a sustainable transportation system being the most dominant subfactors. Understanding the strengths and weaknesses of Riyadh smart city from the current research will help developers improve and maintain areas aligned with Saudi Vision 2030 while also providing a foundation for future research on similar cities or varying factors. [ABSTRACT FROM AUTHOR]

Smart buildings play a key role in the complex ecosystem of cities and are often subject to barriers that limit their development. Although identifying these barriers is fundamental to creating an enabling environment for this segment's expansion, few works aim to identify these challenges. This work has two main objectives: (1) to research the main barriers limiting the development of new smart building projects and (2) to prioritize these barriers from the perspective of professionals with experience in the field. We adopted an exploratory approach common in research that focuses on identifying and prioritizing variables related to a phenomenon, which is based on two main actions: obtaining information through a careful literature review and consulting professionals who work in the concerned field. The results showed that professionals assessed the 23 barriers identified through bibliographic research as important, with the most important being related to lack of qualified professionals, shortage of government policies, higher initial and construction costs, macroeconomic barriers and access to financing, high cost of intelligent systems and technologies, regulatory barriers, lack of knowledge about the current and potential benefits of smart buildings, and more complex design and construction. [ABSTRACT FROM AUTHOR]

China's architecture, engineering, and construction (AEC) industry needs a clear and sustainable development roadmap. Drawing inspiration from speeches delivered at a seminar hosted by the Chinese Academy of Engineering and extensive literature research on Chinese policies, this article presents a summary of the current trends in the AEC industry in four dimensions: industrialization as the foundation, intelligence as the enabler, lean management as the strategy, and green development as the goal. These four dimensions are intricately interconnected and rooted in multiple disciplines. The article provides a detailed review of the current practices, challenges, and future directions associated with each dimension. Additionally, ten grand challenges were proposed to stimulate discussions on the future of the AEC industry. This article offers an overarching understanding of the AEC industry and presents a four-dimension framework for sustainable development, which can be valuable for AEC practitioners. [ABSTRACT FROM AUTHOR]

In construction, the accurate measurements are important in ensuring the quality of work delivered. Different measuring tools have been developed to help workers conduct accurate measuring. However, they may be subject to manipulation difficulties, such as the need for tap/gesture interaction. This paper proposes a novel eye gaze-aided virtual tape measure framework that provides a hands-free manner for conducting the measurements in construction. This framework consists of three components: data collection for point of interest, sensor calibration, and distance calculation. Its effectiveness is tested by measuring the dimensions of 15 common objects in laboratory and on-site environments and achieves the average absolute and relative errors of 2.4 cm and 4.8%. The absolute errors range from 0.3 to 7.3 cm. A comparison study is conducted to demonstrate its superior performance over iPhone's Measure application. The results illustrate the feasibility and potential of using the framework to enable measures for smart construction. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) technology can organically combine data modeling and virtual reality, and compare them with actual construction objects to realize the smart collaboration of entity and the model in the construction processes, greatly reducing early stage mistakes and improve construction efficiency. This paper proposed an automation system framework of smart construction based on BIM technology and collaboration platform, and discussed the element configuration of smart construction based on BIM technology and collaboration platform, and finally elaborated the working mechanism of automated system based on BIM technology and collaboration platform. The study results show that the automation system based on the BIM technology and collaboration platform supports the rapid construction of large-scale three-dimensional scenes and the precise integration of multi-source data, and can bring together construction information and asset information from different sources to form an open, safe, and accessible digital environment. The application of smart construction technology in construction cost management has brought huge economic benefits, construction efficiency has been increased by 65%, construction period has been shortened by 30%, labor intensity has been reduced by 27%, and labor productivity has been increased by 39%, which has brought considerable indirect benefits to construction projects.

This paper presents an IoT-based solution for detecting grouting voids in tunnel construction using the Raspberry Pi microcomputer. Voids between the primary and secondary tunnel linings can compromise structural integrity, and traditional methods like GPR lack continuous feedback. The proposed system uses embedded electrical wires in the secondary lining to measure conductivity, with disruptions indicating unfilled voids. The Raspberry Pi monitors this in real time, uploading data to a cloud platform for engineer access via smartphone. Field tests were conducted in a full-scale, 600 m long tunnel to evaluate the system’s effectiveness. The tests demonstrated the system’s accuracy in detecting voids in various tunnel geometries, including straight sections, curves, and intersections. Using only the proposed void detection system, the largest void detected post-grouting was 1.8 cm, which is within acceptable limits and does not compromise the tunnel’s structural integrity or safety. The system proved to be a cost-effective and scalable solution for real-time monitoring during the grouting process, eliminating the need for continuous manual inspections. This study highlights the potential of IoT-based solutions in smart construction, providing a reliable and practical method for improving tunnel safety and operational efficiency during grouting operations.

With the fact that the main operational parameters of the construction process in mechanized tunneling are currently selected based on monitoring data and engineering experience without exploiting the advantages of computer methods, the focus of this work is to develop a simulation-based real-time assistant system to support the selection of operational parameters. The choice of an appropriate set of these parameters (i.e., the face support pressure, the grouting pressure, and the advance speed) during the operation of tunnel boring machines (TBM) is determined by evaluating different tunneling-induced soil-structure interactions such as the surface settlement, the associated risks on existing structures and the tunnel lining behavior. To evaluate soil-structure behavior, an advanced process-oriented numerical simulation model based on the finite cell method is utilized. To enable the real-time prediction capability of the simulation model for a practical application during the advancement of TBMs, surrogate models based on the Proper Orthogonal Decomposition and Radial Basis Functions (POD-RBF) are adopted. The proposed approach is demonstrated through several synthetic numerical examples inspired by the data of real tunnel projects. The developed methods are integrated into a user-friendly application called SMART to serve as a support platform for tunnel engineers at construction sites. Corresponding to each user adjustment of the input parameters, i.e., each TBM driving scenario, approximately two million outputs of soil-structure interactions are quickly predicted and visualized in seconds, which can provide the site engineers with a rough estimation of the impacts of the chosen scenario on structural responses of the tunnel and above ground structures. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) technology can organically combine data modeling and virtual reality, and compare them with actual construction objects to realize the smart collaboration of entity and the model in the construction processes, greatly reducing early stage mistakes and improve construction efficiency. This paper proposed an automation system framework of smart construction based on BIM technology and collaboration platform, and discussed the element configuration of smart construction based on BIM technology and collaboration platform, and finally elaborated the working mechanism of automated system based on BIM technology and collaboration platform. The study results show that the automation system based on the BIM technology and collaboration platform supports the rapid construction of large-scale three-dimensional scenes and the precise integration of multi-source data, and can bring together construction information and asset information from different sources to form an open, safe, and accessible digital environment. The application of smart construction technology in construction cost management has brought huge economic benefits, construction efficiency has been increased by 65%, construction period has been shortened by 30%, labor intensity has been reduced by 27%, and labor productivity has been increased by 39%, which has brought considerable indirect benefits to construction projects. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The construction industry is continuously evolving, seeking innovative solutions to enhance efficiency, reduce costs, and improve safety. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a transformative technology in the construction sector, offering numerous advantages in data collection and site management. This paper presents a novel approach for utilizing UAVs to automate the path generation of rollers, a crucial element in the construction of roads and other large-scale infrastructure projects. A UAV was used to scan the target area to create a model; the next step was to generate the path for the rollers. Traditionally, the process of determining optimal roller paths is labor-intensive and reliant on manual surveys and engineering expertise. This study proposes a streamlined workflow that harnesses UAVs equipped with computer vision technology to capture high-resolution topographical data of construction sites. This data is then processed through an algorithm created by the authors that automatically generates optimized roller paths based on several factors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of smart construction technologies is becoming a crucial approach to improving safety performance in the construction industry. Existing research often focuses on the qualitative analysis of specific technologies, neglecting a comprehensive empirical examination of the characteristics of smart construction technologies and their impact on project safety performance. This study identifies five principal characteristics of smart construction technology: integration, automation, initiative, shareability, and sustainability. By integrating the technology acceptance model (TAM) and the task–technology fit (TTF) model, we propose a theoretical path of “technology characteristics–perceived behavior–behavioral intention–safety performance” and develop a structural equation model to assess the effect of these characteristics on safety performance. The results indicate that these characteristics have a significant direct impact on safety performance, with the estimated direct effect reaching as high as 0.61. Additionally, these characteristics positively influence safety performance indirectly through mediating variables such as perceived ease of use, perceived usefulness, usage intention, and usage behavior. This study empirically verifies the pathways through which smart construction technology characteristics affect safety performance, providing theoretical support for the widespread application of these technologies in construction engineering and enhancing safety management.

Under the influence of pervasive digital revolution, the accessibility and analysis of ‘big data’ can provide useful insights and help various industries evolve. Despite the popularity of big data, the construction industry is lagging behind other industries in adopting big data technologies. This paper fills the knowledge gap by examining the status quo of big data adoption in companies with different sizes and roles, as well as that in projects with different types, and ascertaining the drivers for and challenges in adopting big data. This paper employed a structured questionnaire survey and statistical analyses to investigate the significance of factors influencing the drivers, challenges, and enhancement strategies of big data adoption, and validated the results with post-study interviews with construction professionals. The results show that big data adoption in the construction industry is affected by the size of companies and the work experience of their employees. Technology advancement, competitiveness, and government plan and policy initiatives are identified as the top three drivers of big data adoption in the construction sector. Moreover, a lack of appropriate supporting systems, difficulties in data collection, and the shortage of knowledge and experience are found to be the major challenges in big data adoption. Finally, the identified top three strategies for overcoming these challenges and promoting big data adoption are ‘clear organization structure’, ‘government incentives’, and ‘the training of information technology (IT) personnel’. The paper suggests the necessity of creating differentiated strategies for big data adoption for companies with different scales and roles, and helps provide useful insights for policy-makers in promoting big data applications.

Smart buildings play a key role in the complex ecosystem of cities and are often subject to barriers that limit their development. Although identifying these barriers is fundamental to creating an enabling environment for this segment’s expansion, few works aim to identify these challenges. This work has two main objectives: (1) to research the main barriers limiting the development of new smart building projects and (2) to prioritize these barriers from the perspective of professionals with experience in the field. We adopted an exploratory approach common in research that focuses on identifying and prioritizing variables related to a phenomenon, which is based on two main actions: obtaining information through a careful literature review and consulting professionals who work in the concerned field. The results showed that professionals assessed the 23 barriers identified through bibliographic research as important, with the most important being related to lack of qualified professionals, shortage of government policies, higher initial and construction costs, macroeconomic barriers and access to financing, high cost of intelligent systems and technologies, regulatory barriers, lack of knowledge about the current and potential benefits of smart buildings, and more complex design and construction.

Traditional teaching has become a thing of the past, the integration of technology and the classroom has brought about significant changes in education, and the role of information entropy in teaching has gradually manifested itself. In this paper, the behavioral time occupancy of the teaching process of university courses is viewed as a probability distribution event combined with the Lagrange multiplier method to solve the maximum entropy value in the distribution of practical teaching behavior. The practice depth value of classroom behavior can be calculated using the interaction behavior coefficient and interaction behavior entropy. Apply the information entropy to the practical teaching analysis of university courses, verify the practical effect of university courses from different sides, and carry out intelligent course construction according to the current situation of students’ learning behavior. The results show that the average score of class 3 in the practical analysis ability assessment is 4.021, which indicates that the practical analysis ability of students in the class meets the standard, and the students’ practical execution should be mainly cultivated in the later practical teaching. 67.1% of students believe that the intelligent classroom has improved their performance over the traditional classroom according to the intelligent course model.

The integrated application of building information modeling (BIM) and big data (BD) has received widespread attention, and has been involved in smart construction sites, construction project management, and construction project budgeting. Nevertheless, research on the integrated implementation of BIM and BD in China mainly concentrates on a project stage or profession, the exploration of technology integration mostly focuses on the theoretical level, and the research distribution is scattered. As such, the intention of this paper is to reveal the research history of BIM and BD in China, as well as to study the research methodologies and research fields for a more thorough knowledge of the BIM and BD research and development status in the Chinese construction sector, which adopts a mixed research method that uses a quantitative research via two analytical software tools, i.e., CiteSpace version of 6.1.R6 and the Statistical Analysis Toolkit on line edition for Informetrics software packages, to conduct a macro bibliometric analysis on BIM and BD research in the China National Knowledge Infrastructure database, and provides a follow-up micro qualitative research with content analysis. To ensure the comprehensiveness of the research, the core articles of the topic in the web of science database have been sorted out and analyzed for fully understanding the development of BIM and BD in the field of Chinese construction, resulting in identifying the current research hotspots and trends in China. The results suggest that the popular research keywords on BIM and BD in China since the year 2015 have mainly been focused on informatization, the internet of things, and rail transportation. Three fruitful research themes in BIM and BD research in China have been identified, including smart construction, smart operation, and bridge informatization. [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]

Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring of these factors are vital. This review traces the evolution of the Eco-concept of stored grain in China, focusing on micro- and macro-structural composition, the Multi-field/Re-coupling structure, and Smart Construction of SGEs, while introducing the four development lines and Wisdom Methodology of SGEs. The current status and challenges of SGEs in China are also discussed. The Eco-concept of stored grain in China has progressed through the initial exploration period, formation and practice periods, and has now entered its fourth stage, marked by a shift to include interactions of multiple biological fields. This evolution extends beyond the traditional binary relationship and offers emerging technologies greater scope for scientific and intelligent theoretical analysis of grain storage practices. The Wisdom Methodology employs a multifaceted, Mechanism and Data-driven approach, incorporating four driving methods, and is now widely recognized as a leading strategy for researching Smart Grain Systems. Digital Twin technology enables precise simulations and mappings of real-world SGEs in a virtual environment, supporting accurate assessments and early warnings for issues concerning grain conditions. Driven by Mechanism and Data, Digital Twin solutions are a pioneering trend and emerging hotspot with vast potential for enhancing the intelligence and wisdom of future grain storage processes. Overall, this review provides valuable guidance to practitioners for advancing high-quality Smart Grain Systems, enhancing sustainable and intelligent grain storage practices. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Project manager competencies have been widely discussed in the literature. Although many works present lists of competencies for project managers, specific competencies for smart buildings were not found. This work aimed to identify the main competencies of project managers for the management of smart building projects. The methodology consisted of a two-step approach using the guidelines of preferred reporting items for systematic reviews and meta-analysis (PRISMA) to perform broad and detailed bibliographic research. Initially, a literature review was carried out to identify the main research fields related to the management of smart building projects and the specificities of these fields that can influence the management of this type of project. Next, these specificities were related to the competencies of project managers mentioned in the literature, to identify a set of more relevant competencies for project managers of smart building projects. The results showed that the main research fields are related to scope, risks, costs, schedule, technologies and systems, sustainability, and general characterization, which consider objectives, barriers, challenges, and regulations. They also showed that the most relevant competencies for managing smart buildings projects are technical competencies, leadership, communication, budgeting, attitudes toward risk, strategic management, organization, and specifying real requirements. These findings show the importance of hard and soft skills for managing smart building projects. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, five Smart Construction Objects (SCOs) are developed and demonstrated. The usefulness and characteristics of the developed SCOs were evaluated using a tri-axial diagram analysis method. In addition, a smart construction management system associated with the developed SCOs is proposed. The efficacy of the management system is demonstrated by applying it to various types of construction site, including tunnel construction, railway construction, and underground water tank construction. The results of the tri-axial diagram analysis showed that the developed SCOs have improved awareness and autonomy compared to previously available ones. Multiple on-site applications of the developed smart construction management system resulted in a significant reduction in the time and cost required for construction by more than 40% and 70%, respectively, compared to conventional methods. This is attributable to the simplification of construction resource management procedures, JSA (Job Security Analysis), TBM (Tool Box Meeting), PTW (Permit to Work), and nonconformity management. [ABSTRACT FROM AUTHOR]

There has been a significant increase in studies related to Industry 4.0 alongside the development of new technologies, devices and software, becoming one of the most relevant topics for years within the so-called Fourth Industrial Revolution (4IR). The Architecture, Engineering and Construction sector (AEC) sector is one step behind other engineering fields in productivity, and digitalisation can help reduce this gap. Building Information Modelling (BIM) implementation in various project phases with other technologies such as the Internet of Things, Big Data, Blockchain or Geographic Information System (GIS) are the main drivers of Smart Construction. This paper provides an updated state-of-art of the BIM applications through different civil engineering projects and towards the use of new Information and Communication Technologies (ICTs). Hence, the integration of BIM in the Facility Management through ICTs allows decision making based on data analysis and the optimization of available resources. [ABSTRACT FROM AUTHOR]

Construction is a major source of carbon emissions. Moreover, it faces various other sustainability challenges, such as construction waste, construction noise, vehicular traffic near construction sites, dust and other air and water pollutants, and safety and well-being of construction workers. Poorly designed and constructed buildings will continue to affect the well-being of their occupants and overall energy efficiency throughout the building lifecycle. Hence, accelerating the transformation of the construction industry towards smart construction or Construction 4.0 is an important topic. The ways that technology can help to achieve smart construction, especially with the adoption of construction methods with increasing construction modularity, should be further explored. Focusing on modular integrated construction (MiC), this paper examines the following questions: (1) How has technology been applied to support MiC development and smart construction in Hong Kong? (2) What are the lessons learned? A case study approach of a building information model (BIM)-enabled multifunctional blockchain-based digital platform is adopted to allow us to systematically consider (1) the main objectives and scope, (2) the stakeholders involved, (3) the key outcomes and processes, (4) the applications of blockchain technology, and (5) the integration with other digital software and management platforms in practice. Drawing upon the experience, we propose a generic four-stage approach in understanding and facilitating the adoption of relevant technology towards smart construction. At Stage One, the technologies of BIM, RFID, and blockchain are applied to support the core elements of MiC production: just-in-time transportation and on-site installation. At Stage Two, the digital platform is extended to serve as an interface for third parties, notably government; monitoring, authentication, and certifications for information sharing; visualization; and real-time monitoring and updating of MiC projects. At Stage Three, the system focuses on people in the construction process, aiming to enhance the safety and well-being of workers and drivers throughout the construction process. Different Internet-of-Thing devices and sensors, construction robotics, closed-circuit television, dashboards, and cloud-based monitoring are deployed. At Stage Four, the full construction lifecycle is the focus, whereby a centralized smart command theatre is set up with multiple sources of data in a city information model. [ABSTRACT FROM AUTHOR]

The integrated application of building information modeling (BIM) and big data (BD) has received widespread attention, and has been involved in smart construction sites, construction project management, and construction project budgeting. Nevertheless, research on the integrated implementation of BIM and BD in China mainly concentrates on a project stage or profession, the exploration of technology integration mostly focuses on the theoretical level, and the research distribution is scattered. As such, the intention of this paper is to reveal the research history of BIM and BD in China, as well as to study the research methodologies and research fields for a more thorough knowledge of the BIM and BD research and development status in the Chinese construction sector, which adopts a mixed research method that uses a quantitative research via two analytical software tools, i.e., CiteSpace version of 6.1.R6 and the Statistical Analysis Toolkit on line edition for Informetrics software packages, to conduct a macro bibliometric analysis on BIM and BD research in the China National Knowledge Infrastructure database, and provides a follow-up micro qualitative research with content analysis. To ensure the comprehensiveness of the research, the core articles of the topic in the web of science database have been sorted out and analyzed for fully understanding the development of BIM and BD in the field of Chinese construction, resulting in identifying the current research hotspots and trends in China. The results suggest that the popular research keywords on BIM and BD in China since the year 2015 have mainly been focused on informatization, the internet of things, and rail transportation. Three fruitful research themes in BIM and BD research in China have been identified, including smart construction, smart operation, and bridge informatization.

Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring of these factors are vital. This review traces the evolution of the Eco-concept of stored grain in China, focusing on micro- and macro-structural composition, the Multi-field/Re-coupling structure, and Smart Construction of SGEs, while introducing the four development lines and Wisdom Methodology of SGEs. The current status and challenges of SGEs in China are also discussed. The Eco-concept of stored grain in China has progressed through the initial exploration period, formation and practice periods, and has now entered its fourth stage, marked by a shift to include interactions of multiple biological fields. This evolution extends beyond the traditional binary relationship and offers emerging technologies greater scope for scientific and intelligent theoretical analysis of grain storage practices. The Wisdom Methodology employs a multifaceted, Mechanism and Data-driven approach, incorporating four driving methods, and is now widely recognized as a leading strategy for researching Smart Grain Systems. Digital Twin technology enables precise simulations and mappings of real-world SGEs in a virtual environment, supporting accurate assessments and early warnings for issues concerning grain conditions. Driven by Mechanism and Data, Digital Twin solutions are a pioneering trend and emerging hotspot with vast potential for enhancing the intelligence and wisdom of future grain storage processes. Overall, this review provides valuable guidance to practitioners for advancing high-quality Smart Grain Systems, enhancing sustainable and intelligent grain storage practices.

The integration of drones in the construction industry has ushered in a new era of efficiency, accuracy, and safety throughout the various phases of construction projects. This paper presents a comprehensive overview of the applications of drones in the construction industry, focusing on their utilization in the design, construction, and maintenance phases. The differences between the three different types of drones are discussed at the beginning of the paper where the overview of the drone applications in construction industry is then described. Overall, the integration of drones in the construction industry has yielded transformative advancements across all phases of construction projects. As technology continues to advance, drones are expected to play an increasingly critical role in shaping the future of the construction industry.

To increase the speed and efficiency of expressways construction, information management is being gradually introduced into the construction process. However, progress is limited due to the complexity of expressway engineering and application limitations of information technology. Design and delivery are still dominated by paper files, and the management of test and inspection data is still relatively extensive. Research to date into digital expressway construction has been piecemeal and fragmented with a lack of research related to the whole construction process and a data-centric information management system yet to be realized. In response, through literature research and semi-structured interviews, the framework of a data-driven digital whole-process highway construction management platform was determined. A whole process management platform was established according to the framework, and the functional application of the proposed platform was explained through a case. The framework is proposed from the perspective of the whole process of collaborative sharing., which provides a new way of thinking to solve the problems existing in the current field of expressway construction whole-process management. It also provides data-centric management, electronic design and delivery, a refined workflow, and an efficient management process. [ABSTRACT FROM AUTHOR]

To increase the speed and efficiency of expressways construction, information management is being gradually introduced into the construction process. However, progress is limited due to the complexity of expressway engineering and application limitations of information technology. Design and delivery are still dominated by paper files, and the management of test and inspection data is still relatively extensive. Research to date into digital expressway construction has been piecemeal and fragmented with a lack of research related to the whole construction process and a data-centric information management system yet to be realized. In response, through literature research and semi-structured interviews, the framework of a data-driven digital whole-process highway construction management platform was determined. A whole process management platform was established according to the framework, and the functional application of the proposed platform was explained through a case. The framework is proposed from the perspective of the whole process of collaborative sharing., which provides a new way of thinking to solve the problems existing in the current field of expressway construction whole-process management. It also provides data-centric management, electronic design and delivery, a refined workflow, and an efficient management process.

In this study, five Smart Construction Objects (SCOs) are developed and demonstrated. The usefulness and characteristics of the developed SCOs were evaluated using a tri-axial diagram analysis method. In addition, a smart construction management system associated with the developed SCOs is proposed. The efficacy of the management system is demonstrated by applying it to various types of construction site, including tunnel construction, railway construction, and underground water tank construction. The results of the tri-axial diagram analysis showed that the developed SCOs have improved awareness and autonomy compared to previously available ones. Multiple on-site applications of the developed smart construction management system resulted in a significant reduction in the time and cost required for construction by more than 40% and 70%, respectively, compared to conventional methods. This is attributable to the simplification of construction resource management procedures, JSA (Job Security Analysis), TBM (Tool Box Meeting), PTW (Permit to Work), and nonconformity management.