Research and expertise
I have experience applying data science to tackle challenges in the construction industry. My research focuses on applying data analytics and machine learning to improve construction processes and enhance sustainability.
By combining technical knowledge in construction materials with advanced computational methods, I aim to develop practical, data-driven solutions that support more efficient and sustainable building practices.
Current research activity
- Intelligent circularity prediction for building deconstruction
Recent publications
- Najmoddin, H. Etemadfard, A. Hosseini, M. Ghalehnovi,” Multi-output Machine Learning for Predicting the Mechanical Properties of BFRC”, Journal of Case Studies in Construction Materials, 2024, https://doi.org/10.1016/j.cscm.2023.e02818
- Hosseini, H. Etemadfard, A. Najmoddin, M. Ghalehnovi,” Hyperparameters’ role in machine learning algorithm for modeling of compressive strength of recycled aggregate concrete”, Journal of Innovative Infrastructure Solutions, 2024, https://doi.org/10.1007/s41062-024-01471-z
Profile
My research aims to develop intelligent, data-driven approaches that enhance sustainability, support circular economy principles, and improve decision-making in construction practices. By combining my technical background with advanced computational methods, I strive to bridge traditional construction knowledge with cutting-edge AI tools to address real-world industry challenges.
I hold a Bachelor's and a Master's degree in Civil Engineering, with a specialization in Construction Management. I have always been fascinated by how innovative technologies can transform the construction industry, making processes smarter, safer, and more sustainable. This passion has led me to pursue a PhD at Loughborough University, UK, where I focus on applying artificial intelligence to building deconstruction.