Assistant Professor, Ayatollah Boroujerdi University, Iran
Mahdi Shadabfar is a leading researcher and academic in the fields of artificial intelligence and machine learning, with a focus on geotechnical engineering. He currently serves as a Research Fellow at Lakehead University, Canada, specializing in probabilistic geohazard analysis using deep learning techniques. His extensive background includes postdoctoral research at Sharif University of Technology in Iran and Tongji University in China. Dr. Shadabfar has contributed significantly to geotechnical engineering, particularly in reliability analysis, risk assessment, and resilience of civil infrastructure under uncertain conditions. He holds a Ph.D. in Geotechnical Engineering from Hohai University, China, and has received numerous prestigious awards and honors for his work. Dr. Shadabfar’s research explores deep learning, Internet of Things (IoT), and disaster response strategies, aiming to drive innovation in civil and geotechnical engineering practices.
Profile
Google Scholar
Education
Dr. Mahdi Shadabfar holds a Ph.D. in Geotechnical Engineering from Hohai University, China (2012-2017), where he conducted pioneering research in the reliability analysis of induced damage by single-hole rock blasting. He earned his Master’s degree in Civil Engineering, Earthquake Engineering, from Shahid Beheshti University, Tehran, Iran (2009-2011), focusing on the seismic behavior of buried steel pipelines. His Bachelor’s degree, also from Shahid Beheshti University (2005-2009), was in Civil Engineering, with a thesis on retrofitting RC structures using FRP. Throughout his academic journey, Dr. Shadabfar has been recognized for his excellence in research and academic performance, achieving high GPAs in his studies. He has also pursued various short-term programs and received international recognition, including visiting tsunami-stricken areas of Japan as part of a research program sponsored by Japan’s Ministry of Education, Culture, Sports, Science, and Technology (MEXT).
Experience
Dr. Shadabfar has vast academic and professional experience in civil and geotechnical engineering. He is currently a Research Fellow at Lakehead University in Canada, focusing on probabilistic geohazard analysis using deep learning. Prior to this, he completed postdoctoral research at Sharif University of Technology in Iran, studying performance-based reliability analysis of underground structures, and at Tongji University in China, where he conducted risk and reliability assessments for geotechnical systems. In addition to his research roles, Dr. Shadabfar has taught at several institutions worldwide, including Fujian University of Technology (China), Lorestan University (Iran), and Shahid Beheshti University (Iran), where he led courses in earthquake engineering, geostatistics, and structural analysis. He also created and delivered online courses on his personal website, focusing on topics like deep neural networks and Monte Carlo sampling, demonstrating his commitment to academic innovation and knowledge dissemination in the field of geotechnical engineering.
Awards and Honors
Dr. Mahdi Shadabfar has received numerous prestigious awards and honors throughout his career. He was awarded funding from the Northern Ontario Heritage Fund Corporation (NOHFC) to support his research at Lakehead University. He also received national talent funding from Iran’s National Elites Foundation (INEF) for his research position at Sharif University of Technology. Dr. Shadabfar’s postdoctoral research at Tongji University in China was supported by dedicated research funding for two years. His academic excellence has been recognized through the “Academic Innovation” award from Hohai University for two consecutive years (2013-2014). Furthermore, he was awarded a full scholarship by the China Scholarship Council (CSC) for his doctoral studies in China. These accolades highlight his exceptional contributions to geotechnical engineering and research, reflecting his innovative approach to applying artificial intelligence and machine learning techniques in infrastructure resilience and risk analysis.
Research Focus
Dr. Mahdi Shadabfar’s research focuses on applying artificial intelligence and machine learning techniques in the field of geotechnical engineering, specifically in reliability analysis, risk assessment, and the resilience of civil infrastructure. His current work at Lakehead University revolves around probabilistic geohazard analysis through deep learning models. Dr. Shadabfar’s research is centered on optimizing urban infrastructure systems, improving disaster response planning, and assessing the impacts of climate change on civil structures. He is also deeply involved in exploring the Internet of Things (IoT) for smart cities and sustainable infrastructure development, along with predictive maintenance strategies. Furthermore, his work in digital twin technology and augmented reality aims to revolutionize civil engineering design and construction. Dr. Shadabfar’s research seeks to integrate cutting-edge technologies to create robust, sustainable, and resilient infrastructure systems capable of responding effectively to natural disasters and environmental challenges.
Publication Top Notes
- Deep learning-based automatic recognition of water leakage area in shield tunnel lining 🛠️💧
- Rock fragmentation induced by a TBM disc-cutter considering the effects of joints 🏗️💥
- Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings 📸🔧
- Beam damage detection under a moving load using random decrement technique and Savitzky–Golay filter 🏗️⚙️
- Resilience-based design of infrastructure: Review of models, methodologies, and computational tools 🏢💡
- Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning 🧱🔍
- An optimization strategy to improve the deep learning‐based recognition model of leakage in shield tunnels 🔍🛠️
- Multi-source data driven method for assessing the rock mass quality of a NATM tunnel face via hybrid ensemble learning models 📊🪨
- A combined review of vibration control strategies for high-speed trains and railway infrastructures 🚄🔧
- Approximation of the Monte Carlo sampling method for reliability analysis of structures 🧮🔬