Hyounkwan Kim

For proper construction site management and plan revisions during construction, it is necessary to understand a construction site’s status in real time. Many vision-based construction site-monitoring methods exist, but current technology has not achieved the accuracy required to robustly recognize objects such as construction equipment, workers, and materials in actual jobsite images. To address this issue, this paper proposes a deep convolutional network-based construction object-detection meth

Accurate analyses of equipment idle time are crucial for the efficient utilization of construction equipment in large construction projects. The less idle time the equipment has, the higher productivity it can achieve. However, it is not feasible for field personnel to visually observe the operation of construction equipment all day. An image processing-based methodology is presented in this paper to automatically quantify the idle time of hydraulic excavators. The image color space (hue, satura

Construction-site images that are now easily obtained from digital cameras have the potential to automatically provide the project status information. For example, once construction objects such as concrete columns are accurately identified and counted, the current level of project progress in the column installation activity can easily be measured. However, in order to identify and count the number of concrete columns installed at a particular point of time, a robust object recognition methodol

This paper identifies 31 critical success factors (CSFs) and suggests a framework for effective stakeholder management in long-term complex megaconstruction (LCM) projects that require more than 10 years for multipurpose development. The results of a survey on the prioritization of these 31 CSFs reveal that LCM projects involve more stakeholders than do general construction projects and require a correspondingly wider range of changes during each project. To identify more systematic and strategi