Over the past decades, the construction industry has been attracted to modular construction because of its benefits for reduced project scheduling and costs. However, schedule deviation risks in the logistics process of modular construction can derail its benefits and thus interfere with its widespread application. To address this issue, we aim to develop a digital twin framework for real-time logistics simulation, which can predict potential logistics risks and accurate module arrival time. The digital twin, a virtual replica of the physical module, updates its virtual asset based on building information modeling (BIM) in near real-time using internet of thing (IoT) sensors. Then, the virtual asset is transferred and exploited for logistics simulation in a geographic information system (GIS)-based routing application. We tested this framework in a case project where modules are manufactured at a factory, delivered to the site via a truck, and assembled onsite. The results show that potential logistical risks and accurate module arrival time can be detected via the suggested digital twin framework. This paper’s primary contribution is the development of a framework that mediates IoT, BIM, and GIS for reliable simulation which predicts potential logistics risks and accurate module delivery time. Such reliable risk prediction enables effective supply chain coordination, which can improve project performance and the widespread application of modular construction.

According to the Center for Construction Research and Training (CPWR) and the Korea Occupational Safety & Health Agency (KOSHA), falls from ladders are a leading cause of fatalities. The current safety inspection process to enforce height-related rules is manual and time-consuming. It requires the physical presence of a safety manager, for whom it is sometimes impossible to monitor an entire area in which ladders are being used. Deep learning-based computer vision technology has the potential to capture a large amount of useful information from a digital image. Therefore, this paper presents a deep learning-based height assessment method using a single known value in an image to measure working height, monitor compliance to safety rules, and ensure worker safety. The proposed method comprises (1) extraction of safety rules from the KOSHA database related to the A-type ladder; (2) object detection (Single Shot Multibox Detector SSD) (3) a height-computing module (HCM) to estimate the working height of the worker (how high a worker is from the ground); and (4) classification of worker behavior (using the developed SSD-based HCM) based on the best practices derived from the KOSHA database. The developed algorithm has been tested on four different scenarios based on KOSHA safety rules, with heights ranging from under 1.2 m to over 2 m. Additionally, the proposed method was evaluated on 300 images for binary classification (safe and unsafe) and achieved an overall accuracy of 85.33%, verifying its feasibility for intelligent height estimation and compliance monitoring.

As a temporary facility, scaffolding has an essential role in providing a work environment at height in the construction industry. According to the Occupational Safety and Health Administration (OSHA), approximately 65% of laborers work on scaffolding. Scaffolding work information needs to be effectively managed with reliability to provide a safe environment. However, managing information of the scaffolding work process remains challenging in forgery risk and manual verification. Blockchain has been widely introduced as an accountable and efficient information management solution. This study presents a blockchain-based system for scaffolding work to grant reliability and efficiency of information management. The system is developed to secure applicability by considering three aspects: (1) optimal blockchain platform regarding characteristics of scaffolding work; (2) storage method to address the hindrance of blockchain; (3) information needed to be compared for verifying adequacy. The detailed configuration and process model for the system is categorically presented, and validated via a case study. The case study confirmed that the system was able to store the information in the block smoothly, verify the information using the smart contract successfully, and remain the block size constant by using off-chain. The proposed system has the potential of practical applicability and could contribute to mitigate potential safety risks associated with inadequate scaffolding work management. Furthermore, the proposed system development flow can be leveraged as a guideline to extend blockchain applications to diverse areas in the construction domain.

With the advancement of light detection and ranging (LiDAR) technology, the mobile laser scanner (MLS) has been regarded as an important technology to collect geometric representations of the indoor environment. In particular, methods for detecting indoor objects from indoor point cloud data (PCD) captured through MLS have thus far been developed based on the trajectory of MLS. However, the existing methods have a limitation on applying to an indoor environment where the building components made by concrete impede obtaining the information of trajectory. Thus, this study aims to propose a building component detection algorithm for MLS-based indoor PCD without trajectory using random sample consensus (RANSAC)-based region growth. The proposed algorithm used the RANSAC and region growing to overcome the low accuracy and uniformity of MLS caused by the movement of LiDAR. This study ensures over 90% precision, recall, and proper segmentation rate of building component detection by testing the algorithm using the indoor PCD. The result of the case study shows that the proposed algorithm opens the possibility of accurately detecting interior objects from indoor PCD without trajectory information of MLS.

Building information modeling (BIM) and modular construction are important technologies for construction industry sustainability. This study proposes a relational matrix of key activities and BIM tasks of modular construction projects to analyze practical BIM tasks in Korea. To achieve this objective, 11 key activities and eight BIM tasks are identified through a comprehensive literature review and expert interviews. Then, the relational matrix of key activities and BIM tasks is proposed, and the BIM tasks in the matrix are analyzed in terms of necessity and efficiency using 5-point Likert scales. Finally, the matrix with the BIM utilization index is suggested. As a result, the average BIM utilization index is 0.80 in the off-site phase, and the index results show that 3D shop drawings have the highest index. In the on-site phase, the average BIM UI is 0.73 and the integration of a 4D model with quantity take-off is the most efficient at 0.85. Additionally, from the decision-maker’s perspective, the priority through the index presented helps in making decisions and in practical BIM execution planning. The proposed matrix is a practical reference for decision-makers considering the application of BIM in modular projects, and it contributes to a sustainable construction industry.