Sahand university of technology - Iran
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Early Academic Pursuits
Assistant Professor Dr. Nabi Mehri Khansari began his academic journey with a BSc in Mechanical Engineering from the Iran University of Science and Technology (IUST), one of the premier engineering institutions in Iran. His academic excellence earned him direct admission with a talent-based quota into the MSc and PhD programs in Aerospace Engineering at the University of Tehran. His graduate and doctoral work laid a strong theoretical and applied foundation in structural mechanics, with a developing interest in damage detection by machine learning, which later became a core focus of his research.
Professional Endeavors
Dr. Khansari’s professional trajectory is marked by both academic distinction and international experience. He served as a research fellow at NTNU (Norwegian University of Science and Technology) in Trondheim, Norway—one of the top institutions in Europe for applied engineering research. Currently an Assistant Professor, he works at the forefront of structural diagnostics in aerospace and mechanical systems. He is affiliated with the Aerial Structural Laboratory (ASL) and is actively involved in academic and industrial collaborations focusing on composite structure modeling, fracture analysis, and smart diagnostics using artificial intelligence.
Contributions and Research Focus
Dr. Khansari’s research primarily focuses on Damage and Fracture Detection in Composite Structures, leveraging Machine Learning and Deep Learning algorithms. His pioneering efforts integrate computational mechanics with intelligent diagnostic systems, positioning his work at the intersection of traditional mechanics and data-driven innovation. His contributions have practical applications in aerospace, mechanical systems, and structural health monitoring. He has also developed advanced simulation models to improve predictive accuracy in failure detection and life-cycle analysis, particularly utilizing damage detection by machine learning methodologies.
Impact and Influence
Dr. Nabi Mehri Khansari has established a significant presence in the academic community. As a reviewer for more than 15 international journals, including Materials & Design (Elsevier), Mechanics Based Design of Structures and Machines (Taylor & Francis), and Theoretical and Applied Fracture Mechanics (Elsevier), he has contributed to the scientific integrity and advancement of mechanical and aerospace engineering. His reviews and editorial activities reflect his expert knowledge in fracture mechanics, structural modeling, and damage detection by machine learning, which has positioned him as a thought leader in this niche domain.
Academic Cites
Dr. Khansari’s research output is regularly cited in high-impact journals across domains including structural integrity, materials science, and intelligent systems. His work—particularly in the application of machine learning for damage detection—has become a reference point for emerging researchers and professionals looking to enhance structural safety and performance using AI-driven methods. His contributions to hybrid modeling techniques and deep learning frameworks have enhanced computational precision in engineering diagnostics.
Legacy and Future Contributions
As an emerging leader in aerospace engineering and computational mechanics, Dr. Khansari's legacy is being shaped through his groundbreaking work on damage detection by machine learning. His continued contributions are expected to refine and expand AI-assisted methodologies for real-time structural monitoring and predictive maintenance. With his role in mentoring students, publishing impactful research, and bridging academic knowledge with practical engineering applications, he is set to leave a lasting imprint on the next generation of smart structural systems and computational diagnostics.
📘Damage Detection by Machine Learning
Dr. Khansari’s impactful research has significantly advanced the use of damage detection by machine learning in composite structural systems. His models offer precise fracture identification and performance evaluation. His innovative use of damage detection by machine learning has improved both the accuracy and responsiveness of structural health monitoring. As a pioneer in this field, he continues to push boundaries in applying damage detection by machine learning for aerospace, mechanical, and smart material applications.
✍️ Notable Publication
1️⃣An experimental investigation of HA/Al₂O₃ nanoparticles on mechanical properties of restoration materials
Authors: M. Safarabadi, N. Khansari, A. Rezaei
Journal: Engineering Solid Mechanics
Year: 2014
Citations: 89
2️⃣Mixed mode I/II fracture criterion for orthotropic materials based on damage zone properties
Authors: M. Fakoor, N.M. Khansari
Journal: Engineering Fracture Mechanics
Year: 2016
Citations: 55
3️⃣ Probabilistic micromechanical damage model for mixed mode I/II fracture investigation of composite materials
Authors: N.M. Khansari, M. Fakoor, F. Berto
Journal: Theoretical and Applied Fracture Mechanics
Year: 2019
Citations: 52
4️⃣General mixed mode I/II failure criterion for composite materials based on matrix fracture properties
Authors: M. Fakoor, N.M. Khansari
Journal: Theoretical and Applied Fracture Mechanics
Year: 2018
Citations: 33
5️⃣Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
Authors: S. Shamsirband, N. Mehri Khansari
Journal: Journal of Zhejiang University-SCIENCE A
Year: 2021
Citations: 19
6️⃣A new approach for investigation of mode II fracture toughness in orthotropic materials
Authors: M. Fakoor, N.M. Khansari
Journal: Latin American Journal of Solids and Structures
Year: 2018
Citations: 17
7️⃣Numerical & experimental assessment of mixed-modes (I/II) fracture of PMMA/hydroxyapatite nanocomposite
Authors: M. Ataei-Aazam, M. Safarabadi, M. Beygzade, N.M. Khansari
Journal: Theoretical and Applied Fracture Mechanics
Year: 2023
Citations: 15