Recent Publications

A Modeling Methodology Towards Digital Twin Development in Smart Factories for the Industry 4.0 Human Augmentation Experiments

Due to the rapid changes in the production environment and level of task complexity, workers are faced with more knowledge-intensive tasks requiring higher order thinking and better decision-making. Digital Twin has been identified as a promising technology for addressing the challenges of smart factories by integrating physical and virtual spaces allowing data simulation and performance enhancement. The goal of this research is to simulate a manufacturing assembly line work cell using BIM-enabled digital twinning methodology.

A Methodology for BIM-enabled Automated Reality Capture in Construction Inspection with Quadruped Robots

Construction inspection is an important part of the construction management process to ensure that the project is compliant with the requirements, regulations, and standards. Due to time and cost constraints, inspectors representing owners, as well as architects, designers, and other stakeholders might not be able to visit and inspect the project site in person as often as needed. Inspections performed by  mobile robots can allow more frequent inspections and can help communicate project status with all stakeholders more regularly.

Unmanned Aerial Manipulator (UAM) in Construction: Opportunities and Challenges

Unmanned Aerial Vehicles (UAVs) have been utilized as an alternative medium to human workers in data collection processes in various industries. The capabilities of UAVs are now being extended from passive tasks of data collection to active tasks of interacting with the environment by equipping them with manipulators and robotic arms to function as Unmanned Aerial Manipulators (UAMs). Research on UAMs has been growing in the last few years.

Fundamentals and Prospects of Four-Legged Robot Application in Construction Progress Monitoring

Progress monitoring in the construction industry is mostly a manual process through in-person visual inspection and it leads to inconsistent, time-consuming, labor-intensive, and error-prone data acquisition. The key in progress monitoring is accurate, timely, and regular data collection and analysis during the construction process. Automating the process of regular data collection in progress monitoring can enable systematic recording of construction progress.

BIM-enabled facilities operation and maintenance: A review

Building Information modeling (BIM) has the potential to advance and transform facilities Operation and Maintenance (O&M) by providing a platform for facility managers to retrieve, analyze, and process building information in a digitalized 3D environment. Currently, because of rapid developments in BIM, researchers and industry professionals need a state-of-the-art overview of BIM implementation and research in facility O&M. This paper presents a review of recent publications on the topic.

A framework of developing machine learning models for facility life-cycle cost analysis

Machine learning techniques have been used for predicting facility-related costs but there is a lack of research on developing machine learning models for the complete life-cycle cost (LCC) analysis of facilities. This research aims to systematically investigate the feasibility of forecasting facilities' LCC by implementing machine learning on historical data. The authors propose a comprehensive and generalizable framework for developing facility LCC analysis machine learning models.

Introduction to cyber-physical systems in the built environment

Cyber-physical systems (CPS) imply interconnected physical and digital systems, in which the digital twin serves as a medium to visualize, simulate, manifest, observe, and control the physical built environment. The physical and digital twins of CPS are reciprocally connected and synchronized in real time through interconnected sensors and actuators. The key potential of CPS is the infinite horizon it opens for data analytics that can be performed on the digital twin’s sensory input.