Mahdi Shadabfar | Geotechnical Engineering | Best Researcher Award

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Prof. Mahdi Shadabfar | Geotechnical Engineering | Best Researcher Award

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 🧮🔬

 

 

Gulseren dagdelenler | Engineering Geology | Women Researcher Award

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Assoc. Prof. Dr. Gulseren dagdelenler | Engineering Geology | Women Researcher Award

Gulseren dagdelenler, Hacettepe University, Turkey

Gulseren Dagdelenler is a distinguished researcher and academic at Hacettepe University, specializing in geological engineering. Her research focuses on engineering geology, landslides, GIS, rock mechanics, and soft computing methods. With extensive expertise, she has contributed significantly to improving rock excavation techniques and landslide susceptibility mapping. Her academic journey spans from her undergraduate studies at Hacettepe University to her Ph.D. in geological engineering. With numerous publications and active participation in scientific research, she is a leading figure in her field. Gulseren’s work has earned recognition both nationally and internationally, making her a prominent researcher in the geotechnical engineering community. Beyond academia, she is passionate about contributing to safer, more sustainable construction practices and environmental protection, particularly in landslide-prone regions.

Profile

Education

Gulseren Dagdelenler completed her education at Hacettepe University, where she earned her Bachelor’s degree in Geological Engineering (1999–2003). She then pursued a Master’s degree in the same field at Hacettepe University’s Graduate School of Natural and Applied Sciences (2003–2006), focusing on rock material classification. Building on her academic foundation, she completed her Ph.D. at the same institution from 2007 to 2013, concentrating on landslide susceptibility mapping and evaluation techniques. Throughout her academic career, she has remained dedicated to advancing the field of geological engineering, particularly in areas related to rock mechanics, excavation, and environmental geology. Her research has not only contributed to geological theory but also has practical applications in civil engineering, disaster management, and resource extraction. Gulseren’s strong academic background has laid the foundation for her successful career as a researcher and educator.

Experience

Gulseren Dagdelenler has had an impressive academic career at Hacettepe University. Starting as a Research Assistant in 2007, she has advanced to the position of Ph.D. Research Assistant since 2012, where she continues to contribute to the university’s research output. Throughout her career, Gulseren has worked extensively on topics related to engineering geology, such as landslide susceptibility, rock excavation methods, and the application of geographic information systems (GIS) and remote sensing technologies. Her experience includes both theoretical research and practical studies that have led to the development of tools and methods for predicting rock behavior in excavation processes. Gulseren’s ability to combine geological engineering with modern technology has made her a leading figure in her field. She has also collaborated with various professionals, contributing to the geotechnical engineering community, and published widely in respected academic journals, making her an integral part of the university’s research environment.

Awards and Honors

Gulseren Dagdelenler has received several prestigious awards and honors throughout her academic career. In 2020, her paper “A Flexible System for Selection of Rock Mass Excavation Method,” co-authored with Harun Sonmez and Charalampos Saroglou, won the award from the Turkish National Committee on Roads (YTMK), recognizing its contribution to rock excavation engineering. Her research has been widely cited, with numerous publications in respected journals such as Journal of Rock Mechanics and Geotechnical Engineering, Arabian Journal of Geosciences, and Bulletin of Engineering Geology and the Environment. Her contributions to rock mechanics, geotechnical engineering, and landslide research have garnered recognition within the scientific community. Gulseren’s work is not only well-regarded for its academic rigor but also for its practical implications in environmental safety and engineering practices. These accolades reflect her continued excellence and leadership in the field of geological engineering and geotechnical research.

Research Focus

Gulseren Dagdelenler’s research focuses on several key areas within geological engineering, including engineering geology, landslides, and rock mechanics. One of her primary research interests is landslide susceptibility mapping, particularly in areas prone to geological hazards, such as the Gallipoli Peninsula. She combines remote sensing, GIS, and soft computing methods to enhance the accuracy and efficiency of landslide prediction. Her work also extends to the study of rock excavation techniques, where she has developed innovative methods for selecting excavation methods based on rock mass properties. Additionally, she explores weathering in rocks, the behavior of rock masses under stress, and liquefaction phenomena. Gulseren’s interdisciplinary approach integrates geotechnical engineering with advanced technologies like artificial intelligence to address complex geological problems. Her research not only contributes to scientific knowledge but also has practical applications in civil engineering, environmental management, and disaster mitigation.

Publication Top Notes

  1. “A Flexible System for Selection of Rock Mass Excavation Method” 🪓🪨
  2. “A Novel Approach to Structural Anisotropy Classification for Jointed Rock Masses Using Theoretical Rock Quality Designation Formulation Adjusted to Joint Spacing” 📏🪨
  3. “Comparison of the Efficiency Evaluations of Selected Excavatability Classifications for Rock Masses” ⛏️🪨
  4. “An Empirical Method for Predicting the Strength of Bim Materials Using Modifications of Lindquist’s and Leps’ Approaches” 🧱🔬
  5. “A Flexible System for Selection of Rock Mass Excavation Method” 🪓🪨
  6. “Comparison of the Landslide Susceptibility Maps Using Two Different Sampling Techniques with the Frequency Ratio (Fr) Method” 🌍🌧️
  7. “Landslide Susceptibility Mapping at Ovacık-Karabük (Turkey) Using Different Artificial Neural Network Models: Comparison of Training Algorithms” 🧠🌍
  8. “Prediction of Mono-Wire Cutting Machine Performance Parameters Using Artificial Neural Network and Regression Models” 🤖🔩
  9. “Application of Chebyshev Theorem to Data Preparation in Landslide Susceptibility Mapping Studies: An Example from Yenice (Karabük, Turkey) Region” 🧮📍
  10. “Predicting Uniaxial Compressive Strength and Deformation Modulus of Volcanic Bimrock Considering Engineering Dimension” 🏔️🧱

Hengyu Liu | Geotechnical Engineering Award | Best Researcher Award

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Mr Hengyu Liu | Geotechnical Engineering Award | Best Researcher Award

Mr Hengyu Liu, School of Resources and Safety Engineering, Central South University, China

Hengyu Liu is a PhD candidate specializing in Geotechnical Engineering at Central South University. His research focuses on the intelligent prediction and management of geological hazards in mining environments. Liu has authored multiple papers in esteemed journals and conferences, showcasing his expertise in data-driven modeling and simulation of slope stability and rock mechanics. He holds patents and software copyrights related to mining safety technologies, underscoring his innovative contributions to the field. Liu actively participates in national research projects and has presented his work internationally, demonstrating his commitment to advancing safety engineering in resource extraction.

Publication Profile

Orcid

Education

Hengyu Liu is pursuing his PhD in Safety Engineering at Central South University. His academic journey began in 2019, focusing on civil engineering with a specialization in Geotechnical Engineering. Liu has consistently excelled academically, leveraging his expertise to explore predictive modeling techniques for assessing geological risks in mining contexts. His educational background is complemented by practical experiences in software development and patent innovations aimed at enhancing safety measures in mining operations.

Experience 

Hengyu Liu has extensive research experience in the field of Geotechnical Engineering, particularly in the application of advanced modeling techniques to predict and mitigate geological hazards. He has authored and co-authored several papers published in prestigious journals like “Nature Communications” and “Applied Sciences,” highlighting his contributions to the understanding of slope stability, rock mechanics, and landslide prediction. Liu has also led and participated in national research projects focusing on slope deformation in open-pit mines, demonstrating leadership and collaborative skills in multidisciplinary environments. His work includes the development of simulation platforms and the implementation of innovative technologies to improve safety standards in mining practices.

Research focus

Hengyu Liu’s research centers on the intelligent prediction and management of geological hazards in mining environments. He specializes in leveraging data-driven approaches and advanced modeling techniques such as deep learning and optimization algorithms to analyze and forecast slope stability, rockburst intensity, and landslide risks. Liu’s work aims to enhance safety protocols and operational efficiencies in mining operations through predictive analytics and simulation platforms. His research contributes significantly to the field of Geotechnical Engineering, addressing critical challenges in resource extraction while advocating for sustainable and safe mining practices.

Publication Top Note

“Deep learning in rockburst intensity level prediction: performance evaluation and comparison of the NGO-CNN-BiGRU-Attention model” 📚

Wubin Wang | Transportation Geotechnics Award | Best Researcher Award

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Mr. Wubin Wang | Transportation Geotechnics Award | Best Researcher Award

Civil Engineering Lab Technician, Southwest Jiaotong University, China

Wubin Wang is a dedicated civil engineering lab technician at Southwest Jiaotong University. His primary focus is on the research and development of new maglev subgrade structures, subgrade dynamics, and intelligent transportation systems. With a robust portfolio of leading or participating in 10 research projects, Wang has made significant contributions to his field, evident in his numerous publications in esteemed journals such as Transportation Geotechnics and Railway Engineering Science.

Profile

Scopus

Education 🎓

Wubin Wang has built a strong educational foundation in civil engineering, equipping him with the skills and knowledge necessary to excel in his research endeavors. His academic journey has been marked by continuous learning and development, preparing him to tackle complex engineering challenges.

Experience 🛠️

With extensive experience as a civil engineering lab technician, Wubin Wang has played a crucial role in various research projects. His expertise in subgrade dynamics and intelligent transportation systems has led to innovative advancements in maglev subgrade structures. Wang’s practical experience is complemented by his active participation in collaborative projects with industry leaders such as the China Railway Construction Corporation Limited.

Research Interests 🔍

Wubin Wang’s research interests lie at the intersection of civil engineering and transportation technology. He is particularly focused on the development of new maglev subgrade structures, understanding subgrade dynamics, and advancing intelligent transportation systems. His work aims to improve the efficiency, safety, and cost-effectiveness of transportation infrastructure.

Awards and Recognitions 🏆

Wubin Wang’s contributions to civil engineering have been acknowledged through various accolades and nominations. His innovative research on MLS maglev subgrades, which offers a cost-effective alternative to traditional methods, has earned him a nomination for the prestigious Popular Engineer Awards.

Publications  📚

  1. Experimental Study on the Dynamic Behavior of a New Medium–Low-Speed Maglev Subgrade Structure (2024) – Available at SSRN 4737335, Corresponding author Link
    • Cited by: Various research articles focusing on maglev subgrade dynamics.
  2. A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory (2024) – Sensors, 24(11), 3661, Corresponding author Link
    • Cited by: Studies on railway subgrade assessment and ML applications in civil engineering.
  3. Physical Modeling of Long-Term Dynamic Characteristics of the Subgrade for Medium–Low-Speed Maglevs (2023) – Railway Engineering Science, 31(3), 293-308, Corresponding author Link
    • Cited by: Research on dynamic characteristics of subgrade structures.
  4. Numerical Analysis of Subgrade Behavior under a Dynamic Maglev Train Load (2022) – Advances in Civil Engineering, 2022(1), 2014376 Link
    • Cited by: Papers on dynamic load effects in civil engineering.
  5. In Situ Experimental Study of Natural Diatomaceous Earth Slopes under Alternating Dry and Wet Conditions (2022) – Water, 14(5), 831 Link
    • Cited by: Articles focusing on soil behavior and environmental conditions.
  6. Analysis of Behaviors of the Railway Subgrade with a New Waterproof Seal Layer (2022) – Materials, 15(3), 1180 Link
    • Cited by: Research on waterproofing in civil engineering materials