SangUk Han

About 80–90% of accidents are caused by the unsafe actions and behaviors of employees in construction. Behavior management thus plays a key role in enhancing safety, and particularly, behavior observation is the most critical element for modifying workers’ behavior in a safe manner. However, there is a lack of practical methods to measure workers’ behavior in construction. To analyze workers’ actions, this paper uses an advanced and economical depth sensor to collect motion data and then investi

Abstract Background For construction management, data collection is a critical process for gathering and measuring information for the evaluation and control of ongoing project performances. Taking into account that construction involves a significant amount of manual work, worker monitoring can play a key role in analyzing operations and improving productivity and safety. However, time-consuming tasks involved in field observation have brought up the issue of implementing worker observation in

Rapid development of motion sensors and video processing has triggered growing attention to action recognition for safety and health analysis, as well as operation analysis, in construction. Specifically for occupational safety and health, worker behavior monitoring allows for the automatic detection of workers’ unsafe actions and for feedback on their behavior, both of which enable the proactive prevention of an accident by reducing the number of unsafe actions that occur. Previous studies prov

Evaluating the site-selection process for photovoltaic (PV) plants is essential for securing available areas for solar power plant installation in limited spaces. Although the vicinities of highway networks can be suitable for installing PV plants, in terms of economic feasibility, they have rarely been investigated because the impacts of various factors, including geographic or weather patterns, have not been analyzed. In this respect, this study conducts a case study on selecting the site for

In modular construction, quality control is a crucial step in meeting quality requirements, leading to the completion of a project within the planned schedule and cost. Currently, quality inspection is visually performed by inspectors, which can be costly and unreliable. This study thus proposes a vision-based approach to off-site quality inspection that reconstructs (three-dimensional) 3D point clouds using a projector-camera system and computes the deviations between scans and virtual model to

The application of deep learning (DL) for solving construction safety issues has achieved remarkable results in recent years that are superior to traditional methods. However, there is limited literature examining the links between DL and safety management and highlighting the contributions of DL studies in practice. Thus, this study aims to synthesize the current status of DL studies on construction safety and outline practical challenges and future opportunities. A total of 66 influential cons

Workers in the construction industry are frequently exposed to physically demanding manual tasks with a high level of ergonomic risk. To prevent ergonomic injuries and disorders, posture-based ergonomic evaluation methods, which require inputs describing the worker’s posture (e.g., body joint angles), have been developed and are used widely in practice. However, the reliability of these ergonomic methods has not been investigated fully from the input measurement perspective, as when collected by

Safety training and management are among the constant tasks of project management on any construction site. A review of literature on the causation model and improvement factors for safety confirms that construction accidents can be preventable with consistent safety management and effective communication between managers and workers. One limitation of traditional safety management, however, is that workers may not be efficiently informed of hazardous locations and safety-related issues. In that

In the construction industry, the unsafe actions and behavior of workers are the most significant causes of accidents. Measurement of worker behavior thus can be used as a positive indicator in assessing safety management and preventing accidents. The monitoring of worker behavior, however, has not been applied to safety management in practice due to the time-consuming and painstaking nature of this type of monitoring. To address this problem, this paper utilizes a computer vision-based approach