Master of science in the filed of engineering geology, University of Tehran, Iran
Ms. Hanan Samadi is an emerging scholar in the field of geotechnical engineering, specializing in tunneling and underground structures. With a robust academic background and significant contributions to research in machine learning applications in geotechnics, she has established herself as a promising expert in her field.
🎓 Education:
Ms. Samadi obtained her M.Sc. in Engineering Geology from the University of Tehran in 2021, where she graduated with a stellar GPA of 4.0/4.0 for her major. Her thesis, supervised by Dr. Jafar Hassanpour and Dr. Jamal Rostami, focused on the use of artificial intelligence to investigate EPB operating parameters. She completed her B.Sc. in Geology Science from Payame Noor University in 2018.
👨🏫 Experience:
Ms. Samadi has served as a course mentor and workshop mentor at various prestigious institutions, including Amirkabir University, Shahid Beheshti University, and the Iranian Mining Engineering Organization. Her mentorship has covered topics such as machine learning in tunneling, mechanized excavation, and the application of artificial neural networks in construction.
🔬 Research Interests:
Her research interests encompass tunneling, mechanized excavation, rock mechanics, soil mechanics, and geotechnics. She is particularly focused on developing machine learning and deep learning algorithms to enhance geotechnical and tunneling processes.
🏆 Awards:
Ms. Samadi has received numerous accolades, including the Young Scientists Festival award for developing new tunneling software and multiple scholarships for her academic achievements. She was also recognized for her exceptional thesis at the University of Tehran.
📚 Publications Top Notes:
Estimation of settlement of pile group in clay using soft computing techniques, Geotechnical and Geological Engineering, 2024. Cited by 28.
Maximum surface settlement prediction in EPB TBM tunneling using soft computing techniques, Journal of Physics: Conference Series, 2021. Cited by 14.
Tunnel wall convergence prediction using optimized LSTM deep neural network, Geomech and Eng, 2022. Cited by 7.
Developing the empirical models for predicting the EPB operating parameters in strong Limestone, Iranian Journal of Engineering Geology, 2022. Cited by 6.
Soil Classification Modelling Using Machine Learning Methods, CCITAI, 2022. Cited by 5.
Utilization of rock mass parameters for performance prediction of rock TBMs using machine learning algorithms (publication details incomplete).