John A Haug
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
John A Haug has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Chien-Ho Ko
University of Kansas
1 shared publication - Xiaowei Wang
Department of Civil, Environmental & Architectural Engineering, University of Kansas, Lawrence, KS 66045, USA
1 shared publication
Latest Publications
- Open Access
- 19 June 2025
- Published by Purdue University (bepress) in CIB Conferences
- Vol. 1 (1) , 75
A Conceptual Framework for Intelligent Scaffolding Monitoring System
Scaffolding safety is a significant concern in the construction industry. To address this, researchers have proposed various safety monitoring approaches. However, current intelligent monitoring systems fall short of fully integrating real-time data, and predictive capabilities, reducing their overall effectiveness in ensuring scaffolding safety. Therefore, this study aims to develop a conceptual framework to advance the intelligent scaffolding monitoring system based on cyber-physical systems (CPS) and digital twins (DT) integration. The framework focuses on developing a DT-enabled scaffolding risk assessment model (SRAM), a sensor-based real-time data collection system, and an intelligent cyber-physical scaffolding risk alert. This research provides essential insights for more accurate accident risk assessments, more efficient data collection system, and faster warning mechanisms for construction safety.
- 19 June 2025
- Published by Purdue University (bepress) in CIB Conferences
- Vol. 1 (1) , 75
Open AccessA Conceptual Framework for Intelligent Scaffolding Monitoring System
Scaffolding safety is a significant concern in the construction industry. To address this, researchers have proposed various safety monitoring approaches. However, current intelligent monitoring systems fall short of fully integrating real-time data, and predictive capabilities, reducing their overall effectiveness in ensuring scaffolding safety. Therefore, this study aims to develop a conceptual framework to advance the intelligent scaffolding monitoring system based on cyber-physical systems (CPS) and digital twins (DT) integration. The framework focuses on developing a DT-enabled scaffolding risk assessment model (SRAM), a sensor-based real-time data collection system, and an intelligent cyber-physical scaffolding risk alert. This research provides essential insights for more accurate accident risk assessments, more efficient data collection system, and faster warning mechanisms for construction safety.