Hossein Salehzadeh | Civil Engineering | Excellence in Research

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Assoc. Prof. Dr. Hossein Salehzadeh | Civil Engineering | Excellence in Research

Senior Lecturer and Researcher at Iran University of Science and Technology, Iran 

Dr. Hossein Salehzadeh is an Associate Professor in the School of Civil Engineering at the Iran University of Science and Technology (IUST). With a Ph.D. in Geotechnics from Manchester University, his expertise encompasses the behavior of carbonate sediments, triaxial testing, and cemented sands. Dr. Salehzadeh has made significant contributions to geotechnical engineering through extensive research, teaching, and consulting. He has been instrumental in designing and constructing Iran’s first national geotechnical centrifuge and has authored numerous publications in esteemed journals. His work not only advances academic understanding but also addresses practical challenges in civil engineering.

Professional Profile

Education

Dr. Salehzadeh’s academic journey began with a Bachelor of Science in Civil Engineering from IUST in 1986, followed by a Master of Science in Soil Mechanics and Foundation Engineering from the same institution in 1991. He then pursued his doctoral studies at Manchester University in the United Kingdom, earning a Ph.D. in Geotechnics in 2000. His doctoral research focused on the behavior of non-cemented and cemented carbonate sands under static and cyclic loading, laying the foundation for his future contributions to geotechnical engineering.

Professional Experience

Since 1993, Dr. Salehzadeh has been a dedicated faculty member at IUST, ascending to the rank of Associate Professor. He has held leadership roles, including Head of the Geotechnical Group since 2003. His professional experience extends beyond academia into consulting, where he has been involved in civil design projects, geotechnical studies of marine exploratory wells, and quality control of significant infrastructure projects such as the Ghomrud water conveyance tunnel. His expertise has been sought in evaluating tunneling methods and preparing national manuals, reflecting his influence on both academic and practical aspects of civil engineering.

Research Interests

Dr. Salehzadeh’s research interests are centered on the mechanics of carbonate sediments, cyclic behavior of soils under wave-induced loading, and the properties of cemented soils. He has a particular focus on marine geotechnics, investigating the unique challenges posed by offshore and coastal environments. His work often involves advanced experimental techniques, including the use of geotechnical centrifuge modeling, to simulate and analyze soil behavior under various loading conditions. This research is crucial for the safe and efficient design of foundations and other structures in marine settings.

Research Skills

Dr. Salehzadeh possesses a robust set of research skills, particularly in experimental geotechnics. He has designed and constructed specialized equipment, such as a large-scale oedometer and a geotechnical centrifuge with a one-meter radius, to facilitate advanced soil testing. His proficiency in triaxial testing, soil stabilization techniques, and the development of national standards for unconfined and direct shear tests underscores his technical expertise. Additionally, his ability to integrate experimental findings with practical applications has been demonstrated through his consulting work on various civil engineering projects.

Awards and Honors

Throughout his career, Dr. Salehzadeh has been recognized for his contributions to geotechnical engineering. He has authored and translated several books in Persian on topics such as soil mechanics and tunneling, contributing to the education of future engineers. His leadership in designing and building Iran’s first national geotechnical centrifuge stands as a testament to his commitment to advancing research infrastructure in the country. While specific awards and honors are not detailed in the available sources, his professional achievements reflect a career dedicated to excellence in research, teaching, and practical application.

Conclusion

Dr. Hossein Salehzadeh exemplifies a blend of academic rigor, practical experience, and innovative research in geotechnical engineering. His educational background, professional endeavors, and research contributions have significantly impacted both the academic community and the civil engineering industry in Iran. Through his teaching, leadership, and consulting work, Dr. Salehzadeh continues to influence the development of infrastructure and the advancement of geotechnical practices, embodying the role of a dedicated scholar and engineer.

Publication Top Notes

  1. “Evaluating scale effects in the modeling of buried offshore pipes in Chabahar carbonate sand using centrifuge testing”

    • Authors: H. Salehzadeh, A.A. Heshmati R, M. Aani
    • Year: 2024

  2. “Effect of non-plastic marine silt on physical and mechanical properties of Konarak carbonate sand”

    • Authors: H. Salehzadeh, A.A. Heshmati R, A. Karimabadi
    • Year: 2024
    • Citations: 1

  3. “Investigating the cyclic behavior of Konarak carbonate sand–silt mixtures: An energy-based approach”

    • Authors: H. Salehzadeh, A.A. Heshmati R, A. Karimabadi
    • Year: 2024

  4. “New critical state constitutive model considering particle breakage implicitly for uncemented carbonate sands”

    • Authors: S. Shakeri Talarposhti, A.A. Heshmati, H. Salehzadeh
    • Year: 2024

 

Mohammad Ali Heravi | Civil Engineering | Best Researcher Award

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Mr. Mohammad Ali Heravi | Civil Engineering | Best Researcher Award

PhD. Student at Semnan University, Iran

Mr. Mohammadali Heravi is a dedicated and ambitious Ph.D. candidate ing Civil Engineerin at Semnan University, Iran. With a strong academic foundation, he has developed expertise in structural health monitoring, particularly through the application of deep learning and artificial intelligence. His doctoral research is focused on developing innovative unsupervised deep learning methods to advance structural health monitoring systems. Mr. Heravi also holds an M.Sc. in Civil Engineering from Shahrood University of Technology, where he explored structural damage detection using empirical mode decomposition and statistical pattern recognition. His academic journey began with a B.Sc. in Civil Engineering from Azad University of Mashhad. Currently, he is furthering his research as a Ph.D. researcher at Western University of Ontario, Canada, where he is working on zero-shot transfer learning approaches for structural health monitoring. Mr. Heravi is passionate about contributing to the field of civil engineering through innovative research and collaboration with leading experts.

Profile

Education

Mr. Mohammadali Heravi is currently pursuing a Ph.D. in Civil Engineering at Semnan University, Iran, where he has maintained an impressive GPA of 18.49/20. His doctoral research focuses on developing novel unsupervised deep learning approaches for structural health monitoring. Prior to this, he earned his M.Sc. in Civil Engineering from Shahrood University of Technology, Iran, between 2017 and 2020, with a GPA of 18.03/20. His master’s thesis centered on structural damage detection using improved empirical mode decomposition and statistical pattern recognition. He began his academic journey with a B.Sc. in Civil Engineering from Azad University of Mashhad, Iran, where he graduated in 2016 with a GPA of 15.50/20. Throughout his academic career, Mr. Heravi has demonstrated a strong commitment to advancing his knowledge and expertise in civil engineering, particularly in the areas of structural health monitoring and artificial intelligence.

Professional Experience

Mr. Mohammadali Heravi has amassed significant professional experience in the field of civil engineering, with a focus on structural health monitoring and the application of artificial intelligence. He is currently a Ph.D. researcher in Civil and Environmental Engineering at Western University of Ontario, Canada, where he is developing novel zero-shot transfer learning approaches for structural health monitoring. His research builds on his earlier work as a Ph.D. candidate at Semnan University, Iran, where he began his exploration of unsupervised deep learning techniques in structural health monitoring. Additionally, Mr. Heravi’s experience includes his role as a researcher during his M.Sc. at Shahrood University of Technology, where he specialized in structural damage detection using advanced statistical methods. His professional journey is characterized by a deep commitment to advancing the field of civil engineering through innovative research and practical applications.

Research Interests

Mr. Mohammadali Heravi’s research interests are deeply rooted in the field of civil engineering, with a particular focus on Structural Health Monitoring (SHM) through vibration and vision-based methods. He is keenly interested in Structural Vibration Control and the innovative application of Artificial Intelligence (AI) in engineering structures, especially through Machine Learning, Deep Learning, and Data Mining techniques. His work also extends to Reliability and Numerical Analysis, where he explores the robustness and safety of engineering designs. Additionally, Mr. Heravi is engaged in Image and Signal Processing, utilizing these technologies to enhance the accuracy and efficiency of structural assessments. His research aims to integrate cutting-edge AI methodologies with traditional engineering practices to address complex challenges in the field.

Research Skills

Mr. Mohammadali Heravi possesses a diverse set of technical and professional skills that support his research in civil engineering. He is proficient in programming languages such as Python, with four years of experience, and MATLAB, with six years of expertise. His skills extend to Machine Learning and Deep Learning frameworks, including PyTorch, TensorFlow, and Scikit-Learn, which he applies in his research on structural health monitoring and artificial intelligence. Additionally, Mr. Heravi is well-versed in engineering software like ETABS and SAP2000, crucial for structural analysis and design. He also has experience with various Python libraries, including Numpy, OpenCV, and Pandas, which aid in data manipulation and image processing. Beyond his technical capabilities, Mr. Heravi excels in non-programming software such as Microsoft Office, Photoshop, and Adobe Premiere, which enhance his ability to present research findings and manage projects effectively. His skill set reflects a well-rounded expertise in both the theoretical and practical aspects of civil engineering and artificial intelligence.

Conclusion

Mr. Mohammadali Heravi’s strong academic background, extensive research experience, technical skills, and dedication to advancing civil engineering make him an exemplary candidate for the Best Researcher Award. His contributions to structural health monitoring, particularly through innovative AI applications, highlight his potential to significantly impact the field.

Publications Top Notes

Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP

  • Authors: H. Naderpour, M. Sharei, P. Fakharian, M.A. Heravi
  • Journal: Journal of Soft Computing in Civil Engineering
  • Volume: 6 (1), 66-87
  • Cited By: 30
  • Year: 2022

Structural Health Monitoring by Probability Density Function of Autoregressive-Based Damage Features and Fast Distance Correlation Method

  • Authors: M.A. Heravi, S.M. Tavakkoli, A. Entezami
  • Journal: Journal of Vibration and Control
  • Volume: 28 (19-20), 2786-2802
  • Cited By: 10
  • Year: 2022

Transferring Damage Detection Knowledge Across Rotating Machines and Framed Structures: Harnessing Domain Adaptation and Contrastive Learning

  • Authors: R. Soleimani-Babakamali, M.H. Soleimani-Babakamali, M.A. Heravi, et al.
  • Journal: Mechanical Systems and Signal Processing
  • Volume: 221, 111743
  • Year: 2024

Deep Ensemble Learning for Rapid Large-Scale Post-Earthquake Damage Assessment—Application to 2023 Kahramanmaraş Earthquake Sequence

  • Authors: M.H. Soleimani-Babakamali, M. Askari, M.A. Heravi, R. Sisman, N. Attarchian, et al.
  • Year: 2023