Development of Human Poses for the Determination of On-site Construction Productivity in Real-time

Abstract

To enhance the capability of rapid construction, an automated on-site productivity measurement system is developed. Employing the concepts of Computer Vision and Artificial Intelligence, the developed system wirelessly acquires a sequence of images of construction activities. It first processes these images in real-time to generate human poses that are associated with construction activities at a project site. The human poses are classified into three categories as effective work, ineffective work, and contributory work. Then, a built-in neural network determines the working status of a worker by comparing in-coming images to the developed human poses. The labor productivity is determined from the comparison statistics. This system has been tested for accuracy on a bridge construction project. The results of analyses were accurate as compared to the results produced by the traditional productivity measurement method. This research project made several major contributions to the advancement of construction industry. First, it applied advanced image processing techniques for analyzing construction operations. Second, the results of this research project made it possible to automatically determine construction productivity in real-time. Thus, an instant feedback to the construction crew was possible. As a result, the capability of rapid construction was improved using the developed technology.