Dr. Yihai Fang
Dr. Yihai Fang
Dr Yihai Fang is a Senior Lecturer in Construction Engineering and Management at the Department of Civil Engineering at Monash University. He received his Ph.D. in Civil Engineering from the Georgia Institute of Technology. Before joining Monash, he worked as a Postdoctoral Associate at the College of Design, Construction and Planning at the University of Florida. Yihai‘s research interests include Construction Automation and Informatics, Construction Robotics, Digital Twin for Construction and Built Environments, and Construction Safety and Human Factors.
Qualifications
- Ph.D, Civil Engineering, Georgia Institute of Technology, USA, 2016
- B.Sc, Civil Engineering, Tongji University, China, 2011
Expertise
- Construction Automation and Informatics
- Construction Robotics
- Digital Twin for Construction and Built Environments
- Construction Safety and Human Factors
Academic Appointments
Monash University, Australia Aug 2017 – Present
Senior Lecturer, Department of Civil Engineering
University of Florida, Gainesville, FL 2016 – 2017
Postdoctoral Associate, College of Design, Construction and Planning
Georgia Institute of Technology, Atlanta, GA 2011 – 2016
Graduate Research Assistant, School of Civil and Environmental Engineering
Active Reviewer for Journals
- ASCE Construction Engineering and Management
- ASCE Computing in Civil Engineering
- ASCE Management in Engineering
- Safety Science
- Automation in Construction
- Advanced Engineering Informatics
- Tunnelling and Underground Space Technology
- Frontiers in Built Environment
- Engineering, Construction and Architectural Management
Research Interests
Dr. Fang’s research interests include Construction Automation and Informatics, Construction Robotics, Digital Twin for Construction and Built Environments, and Construction Safety and Human Factors. He has extensive experience in machine learning, sensing and 3D mapping, and Virtual and Augmented Reality (VR/AR).
Research Projects
Current projects
Enhancing road work zone safety through smart sensing and interaction with autonomous vehicles
Road work zones pose significant safety risks to motorists, pedestrians and construction workers because of changed road and traffic conditions, congested and dynamic workspace, and the interaction of traffic vehicles, construction equipment and workers in close proximity. In Australia, approximately 50 deaths and 750 injuries occur each year in road worksite crashes. The estimated cost of this is more than $400 million a year. This project envisions a theoretical framework integrating proximity sensing, vision-based perception and data acquisition systems into a holistic smart technological configuration capable of predictive decision. The integration of such smart systems provides a coherent view of road work operations, enabling real-time communication across multiple platforms and stakeholders, prompting collaboration between drivers, on-site workers, traffic engineers and infrastructure designers to better inform the implementation of future safety measures.
Funded by the Australian Research Council (ARC)
Development of crowdsourcing technology for pavement condition assessment including the use of smart mobile phones
Pavement deterioration such as potholes, cracks and reduced surface friction can damage vehicles and increase safety risks. Information about pavement conditions could help road users to avoid damaged roads and keep the relevant agencies informed so that remedy actions can be taken in time. However, conventional methods for pavement condition monitoring and assessment, especially for large road networks, are costly and time-consuming. Recent development in sensor hardware and data analytics algorithms, as well as the prevailing use of smartphones, provides new opportunities for collecting asset data in an accurate, timely, and cost-effective way. In addition, sensor systems aboard modern vehicles (e.g., ABS, suspension travel detectors, cameras) have the potential to provide additional data that can be used to assess pavement conditions.
Funded by the Australian Research Council (ARC)
Collaborative robotics for structural assembly and construction automation
Recent robotic technologies present a great opportunity for the construction industry to improve quality and productivity while no state-of-the-art research infrastructure has been developed yet for this need. The proposed facility aims to provide a unique platform for research and development for structural assembly and construction automation. It features a flexible and adaptive design and instrumentation of structures and space for a team of collaborative robotics in an interactive environment to achieve automated prefabrication, assembly and building. The outcomes are expected to transform the current labor-intensive construction industry into a highly automated and accurate manufacturing industry with significant benefits to the economy and safety.
Funded by the Australian Research Council (ARC)
Characterising road roughness using accelerometer- and profilometric-based methods
Road roughness is an important indicator of the level of service and cost to the user of using the road. Initially, until the 1990s, it was estimated through calibrated measurement of the displacement of vehicle chassis relative to the point of contact with the road. In the 1990s, we developed the means to directly measure changes in profile height on the road and link that to the International Roughness Index (IRI). Both require specific and sometimes expensive equipment.
With the advent of smartphones and compact measuring and computing powers, attention is turning to use 3-axis accelerometry to estimate road roughness. This is done through mobile phone apps and similar low-cost and uncalibrated technologies. What is not known is the relationship between the definitive profilometric measurement of roughness and roughness measured by ‘handheld’ accelerometer devices such as mobile phones. This project aims to find the accelerometer-based method of measuring road roughness which provides the closest match to profilometric-based methods.
Funded by the Australian Research Council (ARC) and Australian Road Research Board (ARRB)
Past projects
A security-aware and contractually supported framework for data management in Building Information Modelling (BIM) enabled infrastructure projects
Building Information Modelling (BIM) is transforming the way infrastructures are planned, built and operated. BIM is a collaborative approach that relies on sharing data and models among all teams involved in project delivery. This promises increased efficiencies, but also raise new challenges in relation to data management and data security. This research project investigates and addresses how these risks can be minimized through adopting appropriate cybersecurity protocols and contractual risk allocations.
Funded by Monash Infrastructure
BIM-based Cyber-physical Systems for Intelligent Crane Operation in Complex and Dynamic Environments
The execution of construction project requires multiple interdependent actors to work synergistically in ever-changing and space constrained environments. A compelling demonstration of such scenario is crane lift operations. Cranes are an indispensable component in building and infrastructure construction that are responsible for transporting materials and equipment from delivery trucks or laydown areas to their installation locations. This project aims to develop a novel Building Information Modelling-based Cyber-Physical System (BIM-CPS) for intelligent crane lifting operation in complex and dynamic construction environments.
Funded by Monash Faculty of Engineering
Real-time Safety Assistance to Improve Operators' Situational Awareness in Crane Lifting Operations
Funded by the National Science Foundation (NSF), USA
Advanced Blind Lift Safety Using Crane Motion Sensors and Real-time Visualization
Funded by Chevron’s Energy Technology Company (ETC), USA
Best Management Practices for the Storage of Historic Steel Trusses
Funded by the Georgia Department of Transportation (GDOT), USA
For my most up-to-date publications, please refer to the Google Scholar site.
(Co-CI) ARC Linkage Infrastructure, Equipment and Facilities, “Collaborative robotics for structural assembly and construction automation” LE210100019, 2021-2023, $664,580
(Co-CI) Building 4.0 CRC Project, “Critical path impact through productised approach” 2021-2022, AU$150,432
(Co-CI) Building 4.0 CRC Project, “Automated tracking of construction materials for improved supply chain logistics and provenance” 2020-2021, AU$175,003 <Completed>
(Lead CI) Building 4.0 CRC Project, “Field Data Collation to support real-time operational management – Scoping Study” 2020-2021, AU$180,000 <Completed>
(Lead CI) ARC Industry Transformation Hub Project, “Development of crowdsourcing technology for pavement condition assessment including the use of smart mobile phones” IH18.03.4, 2020-2024, AU$156,550
(Lead CI) ARC Industry Transformation Hub Project, “Enhanced road safety through smart sensing for road construction, planned interruptions/worksites and interaction with autonomous vehicles” IH18.03.7, 2020-2024, AU$163,550
(Lead CI) Research Consulting Project, “Automated task planning for robotised brick paving in walkway construction” 2019-2020, AU$76,608 <Completed>
(Co-CI) Building 4.0 CRC Project, “VR/AR technologies in vocational education and training” 2019-2020, AU$151,653 <Completed>
(Lead CI) Innovation Connection Grant, “High-fidelity simulation for crane lift planning and training in complex construction environments”, ICG001494, AU$42,476 <Completed>
(Co-CI) ARC Industry Transformation Hub Project, “Use of big data analytics including machine learning to interpret pavement condition data for pavement remaining life analysis and decision making” IH18.03.5, 2019-2023, AU$130,550
(Co-CI) ARC Industry Transformation Hub Project, “Remote sensing and advanced analysis of measurements of density through the possible use of appropriate sensing techniques, including LIDAR and GPS” IH18.03.3, 2019-2023, AU$264,705
(Co-CI) ARC Industry Transformation Hub Project, “Development of smart traffic management options with connected and autonomous vehicles (CAVs)” IH18.04.5, 2019-2023, AU$129,576
(Lead CI) Monash Infrastructure Seed Fund, Monash University, “A security-aware and contractually supported framework for data management in Building Information Modelling (BIM) enabled infrastructure projects” 2019-2019, AU$49,776 <Completed>
(Co-CI) Monash Infrastructure Seed Fund, Monash University, “Image-based crack evaluation and database design using bridge information modelling and artificial intelligence” 2019-2019, AU$49,776 <Completed>
(Lead CI) Faculty of Engineering Seed Fund, Monash University, “BIM-based Cyber-Physical Systems for Intelligent Crane Operation in Complex and Dynamic Environments” 2019-2019, AU$26,000 <Completed>
Supervision
PHD
Songbo Hu (completed)
Identification and prevention of crane operational hazards by integrating BIM and lift path planning
2018 to 2021
Mostafa Hallaji (completed)
A digital twin framework for predictive maintenance in wastewater treatment plants using BIM and deep learning
2019 to 2022
Jiantsen Goh
Contextual visualization of crane lift information for real-time execution in modular integrated construction
2021 to 2024
Qiqin Yu
Characterising road roughness using accelerometer- and profilometric-based methods
2021 to 2024
Yizhe Wang
Optimising human-robot collaboration in modular structure assembly
2021 to 2024
Tanghan Jiang
Quantifying safety risks in remotely operated crane lifts
2022 to 2025
Qishen Ye
Enhancing road work zone safety through smart sensing and interaction with vehicles
2022 to 2025
Tyson Kieu
Digital Twin for temporary structures in construction processes
2022 to 2025
Junlin Wang
A data-driven and knowledge-based approach for automated crane lift planning and monitoring
2022 to 2025
Teaching Commitments
- ENG1021 - Spatial Communication in Engineering
- CIV5899 - Infrastructure Information Management