With the wave of the Fourth Industrial Revolution, the construction industry is also witnessing the application of numerous state-of-the-art technologies. Among these, augmented reality (AR) technology has the advantage of utilizing existing 3D models and BIM data and is thus an area of active research. However, the main area of research to date has either been in visualizing information during the design phase, where architects and project stakeholders can share viewings, or in confirming the required information for construction management through visualization during the construction phase. As such, more research is required in the application of AR during the facility management (FM) phase. Research utilizing BIM in the FM phase, which constitutes the longest period during the lifecycle of a building, has been continuously carried out but has faced challenges with regard to on-site application. The reason for this is that information required for BIM during the design, construction and FM phases is different, and the reproduced information is vast, so identifying the required BIM data for FM and interfacing with other systems is difficult. As a measure to overcome this limitation, advanced countries such as the US and UK have developed and are using Construction Operations Building information exchange (COBie), which is an open-source BIM-based information exchange system. In order to effectively convert open-source BIM data to AR data, this research defined COBie data for windows and doors, converted them to a system and validated that it could actually be applied for on-site FM. The results of this system’s creation and validation showed that the proposed AR-based smart FMS demonstrated faster and easier access to information compared with existing 2D blueprint-based FM work, while information obtained through AR allowed for immediate, more visual and easier means to express the information when integrated with actual objects.

Regular scaffolding quality inspection is an essential part of construction safety. However, current evaluation methods and quality requirements for temporary structures are based on subjective visual inspection by safety managers. Accordingly, the assessment process and results depend on an inspector’s competence, experience, and human factors, making objective analysis complex. The safety inspections performed by specialized services bring additional costs and increase evaluation times. Therefore, a temporary structure quality and safety evaluation system based on experts’ experience and independent of the human factor is the relevant solution in intelligent construction. This study aimed to present a quality evaluation system prototype for scaffolding parts based on computer vision. The main steps of the proposed system development are preparing a dataset, designing a neural network (NN) model, and training and evaluating the model. Since traditional methods of preparing a dataset are very laborious and time-consuming, this work used mixed real and synthetic datasets modeled in Blender. Further, the resulting datasets were processed using artificial intelligence algorithms to obtain information about defect type, size, and location. Finally, the tested parts’ quality classes were calculated based on the obtained defect values.

In the construction industry, the process of carrying heavy loads from one location to another by means of a crane is inevitable. This reliance on cranes to carry heavy loads is more obvious when it comes to high-rise building construction. Depending on the conditions and requirements on-site, various types of construction lifting equipment (i.e., cranes) are being used. As off-site construction (OSC) is gaining more traction recently, cranes are becoming more important throughout the construction project as precast concrete (PC) members are major components of OSC calling for lifting work. As a result of the increased use of cranes on construction sites, concerns about construction safety as well as the effectiveness of existing load collision prevention systems are attracting more attention from various parties involved. Besides the inherent risks associated with heavy load lifting, the unpredictable movement of on-site workers around the crane operation area, along with the presence of blind spots that obstruct the crane operator’s field-of-view (FOV), further increase the accident probability during crane operation. As such, the need for a more reliable and improved collision avoidance system that prevents lifted loads from hitting other structures and workers is paramount. This study introduces the application of deep learning-based object detection and distance measurement sensors integrated in a complementary way to achieve the stated need. Specifically, the object detection technique was used with the application of an Internet Protocol (IP) camera to detect the workers within the crane operation radius, whereas ultrasonic sensors were used to measure the distance of surrounding obstacles. Both applications were designed to work concurrently so as to prevent potential collisions during crane lifting operations. The field testing and evaluation of the integrated system showed promising results.

With the advancement of state-of-the-art technologies, the semiconductor industry plays a key role as an essential component in the manufacture of various electronic products. Since the manufacturing of a semiconductor goes through very sophisticated and complex processes, efficient and accurate work and management are essential in the design, construction, and operation stages of the semiconductor fabrication (FAB) plant. Recently, the combined application of building information modeling (BIM) and augmented reality (AR) technology has gained increased attention in this semiconductor FAB industry as an advanced way to improve work efficiency and accuracy while eliminating other related problems, such as human errors. Despite the perceived benefits of combined use of BIM and AR, many technical problems still exist when integrating the target test model and the 3D virtual object model using BIM data and existing AR visualization technology, due to the unique characteristics of the FAB sites. To solve these problems, this study proposed an AR-based real-time BIM data compatibility verification method for future FAB digital twin implementation and demonstrated that it could be converted into a system and applied to actual FAB sites. As a result of the development and verification of this system, the proposed AR-based real-time BIM data compatibility verification system enables the accurate fitting of the AR model and actual object through AR tracking and anchoring technology considering the characteristics of FAB sites. After the fitting, the system was able to maintain compatibility, even when the camera moved and the marker moved away from the screen. By expanding the effective distance of compatibility between the AR model and the actual object, it was possible to increase the AR application range between the 3D virtual object model and the test target model and to improve the compatibility.

Currently, BIM implementation in domestic FAB construction projects is in such an early stage that they are benchmarking the BIM implementation method applied in the conventional building sector. As such, in order to enhance the usability of BIM in FAB construction projects, it is necessary to have a correct understanding of FAB construction and to apply BIM in accordance with the characteristics of FAB. This requires defining critical BIM competencies needed by the participants of FAB construction projects in undertaking their tasks as well as identifying their current level of competency possessed. Accordingly, this study defined and presented BIM competencies that reflect the characteristics of FAB through the analysis of typical tasks related to FAB construction projects. For this, project participants from three FAB construction project sites in Korea (where BIM was implemented) were recruited, and a survey was conducted on their status of competency level. Moreover, a comparative analysis was conducted focusing on the correlations between their career characteristics (‘Construction project experience’, ‘BIM-based construction project experience’, ‘FAB construction project experience’, ‘BIM-based FAB construction project experience’ and ‘Overall project experiences’) and their current level of BIM competencies. As a result, high correlations were found between BIM competencies and BIM-based FAB construction project experience for the owner group, whereas competency items related to BIM-based construction project experience showed high correlations for the designer group.

More bridges today require maintenance with age, owing to increasing structural loads from traffic and natural disasters. Routine inspection for damages, including in the aftermath of special events, is conducted by experts. To address the limitations of human inspection, deep-learning-based analysis of bridge damage is being actively conducted. However, such models exhibit deteriorated performance in classifying multiple classes. Most existing algorithms do not use in situ images. Hence, the results of the model training do not accurately reflect the actual damage. This study utilizes an extant method and proposes a new model of combination training by bridge member. By integrating the two approaches, we propose a bridge damaged-object-detection deep-combination framework (BDODC-F). To ensure variety in the type of damaged objects and enhanced model performance, a deep-learning-based super-resolution module is employed. For performance improvement and optimization, a deep-learning combination model based on individual training by bridge member is proposed. The BDODC-F improved the mean average precision by 191.6% and 112.21% in the combination model. We expect the framework to aid engineers in the automated detection and identification of bridge damage.