Smart construction utilizes the latest IoT “smart” technologies, such as Cyber-Physical Systems, to aid in construction processes. A Cyber-Physical System (CPS) is an amalgamation of computation, networking and physical objects. There are various examples of CPS being employed in sectors varying from manufacturing, robotics, and smart cities. Where the application and literature are lacking, however, is construction. This paper focuses on construction machinery operation and work tracking through CPS. The relevant technologies required for creating a system with these capabilities are Augmented Reality (AR) and Digital Twin (DT). Two salient features of a CPS are real-time state knowledge and control. These are handled by the sensors and micro-controllers, respectively, allowing for bi-directional communication between the virtual models and physical objects. Remote control and tracking/monitoring of construction machinery can expedite certain on-site processes such as operations logging and 4D BIM, while improving operation precision and safety.

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.

The more detailed building information modeling (BIM) is, the more accurate are the quantities that can be extracted in BIM-based quantity take-off. However, the time and cost typically allowed for modeling are insufficient to extract accurate quantities throughout a project’s life cycle. In addition, variations in quantity interfere with estimating accurate construction costs. Despite the recognition that extracted quantities can differ depending on the details of building interior components, the number and extent of discrepancies, or whether these would differ by material, is unknown. Therefore, this study identifies and analyzes the approximately 6–9% quantity discrepancy (QD) in quantities extracted from different building interior components, in order to improve the accuracy of cost estimation using BIM. The discrepancy was analyzed by comparing the extracted quantities via two different model types, specifically a compositely modeled object and an individually modeled object. These models were applied to four cases of interior work in residential units. Results indicate that the QD occurs non-sequentially and exceeds the loss rates for some interior materials. Furthermore, this study identifies the causative factors of the QD by common features observed in the particular materials and suggests modeling and measurement strategies for minimizing QD.

Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site Construction (OSC)”, a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement. The problem with OSC construction is that the quality inspection of Precast Concrete (PC) members produced at the factory and brought to the construction site is not carried out accurately and systematically. Due to the shortage of quality inspection manpower, a lot of time and money is wasted on inspecting PC members on-site, compromising inspection efficiency and accuracy. In this study, the major inspection items to be checked during the quality inspection are classified based on the existing PC member quality inspection checklist and PC construction specifications. Based on the major inspection items, the items to which AI technology can be applied (for automatic quality inspection) were identified. Additionally, the research was conducted focusing on the detection of cracks, which are one of the major types of defects in PC members. However, accurate detection of cracks is difficult since the inspection mostly relies on a visual check coupled with subjective experience. To automate the detection of cracks for PC members, video images of cracks and non-cracks on the surface were collected and used for image training and recognition using Convolutional Neural Network (CNN) and object detection, one of the deep learning technologies commonly applied in the field of image object recognition. Detected cracks were classified according to set thresholds (crack width and length), and finally, an automated PC member crack detection system that enables automatic crack detection based on mobile and web servers using deep learning and imaging technologies was proposed. This study is expected to enable more accurate and efficient on-site PC member quality inspection. Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspection work was reduced. This led to a reduction of 13 min of total work time, thereby improving work efficiency and convenience. Since quality inspection information can be stored and managed in the system database, human errors can be reduced while managing the quality of OSC work systematically and accurately. It is expected that through optimizing and upgrading our proposed system, quality work for the precise construction of OSC projects can be ensured. At the same time, systematic and accurate quality management of OSC projects is achievable through inspection data. In addition, the smart quality inspection system is expected to establish a smart work environment that enables efficient and accurate quality inspection practices if applied to various construction activities other than the OSC projects.

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.