Dr Diani Sirimewan
Faculty of Engineering,
School of Architecture & Built Environment
Personal details
Positions
- Lecturer in Construction and Project Management
Faculty of Engineering,
School of Architecture & Built Environment
Keywords
Construction and Demolition Waste Management, Material Recycling and Resource Recovery, Computer Vision and Image Processing, Deep Learning and Neural Networks, Circular Economy, Industrial Automation
Research field
Environmental management, Building
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2020
Qualifications
- Doctor of Philosophy (Construction Engineering) (Monash University)
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Teaching
Unit Coordination and Teaching
- ABH323 Contract Administration
- ABH322 Cost Planning and Control
Publications
- Sirimewan, D., Dayarathna, S., Raman, S., Bai, Y. & Arashpour, M. (2025). A benchmark dataset for class-wise segmentation of construction and demolition waste in cluttered environments. Scientific Data, 12(1). https://eprints.qut.edu.au/261856
- Sirimewan, D., Kineber, A., Raman, S., Garcia, R. & Arashpour, M. (2025). Leveraging large-scale vision foundation models for automated construction and demolition waste recognition. Advanced Engineering Informatics, 68. https://eprints.qut.edu.au/261850
- Sirimewan, D., Kunananthaseelan, N., Raman, S., Garcia, R. & Arashpour, M. (2024). Optimizing waste handling with interactive AI: Prompt-guided segmentation of construction and demolition waste using computer vision. Waste Management, 190, 149–160. https://eprints.qut.edu.au/261848
- Sirimewan, D., Bazli, M., Raman, S., Mohandes, S., Kineber, A. & Arashpour, M. (2024). Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild. Journal of Environmental Management, 351. https://eprints.qut.edu.au/261849
- Sirimewan, D., Harandi, M., Peiris, H. & Arashpour, M. (2024). Semi-supervised segmentation for construction and demolition waste recognition in-the-wild: Adversarial dual-view networks. Resources, Conservation and Recycling, 202. https://eprints.qut.edu.au/261847
QUT ePrints
For more publications by Diani, explore their research in QUT ePrints (our digital repository).
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A complete list of publications is available at: https://www.qut.edu.au/about/our-people/academic-profiles/diani.sirimewan