Gültekin AKTAŞ | Civil Engineering | Best Researcher Award

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Mr. Gültekin AKTAŞ | Civil Engineering | Best Researcher Award

Assoc. Prof. Dr at Dicle University Department of Civil Engineering, Turkey.

Gultekin Aktas is a distinguished researcher in civil engineering, specializing in structural dynamics and concrete behavior. He holds a PhD in Civil Engineering from Dicle University and has made significant contributions through innovative research on topics such as fresh concrete behavior under vibration, prediction models using artificial neural networks, and mold design for precast concrete elements. Aktas’s work is published in reputable journals like Structural Engineering and Mechanics and KSCE Journal of Civil Engineering, showcasing his technical expertise and diverse methodologies. His research employs advanced computational techniques and experimental validations, reflecting a high level of proficiency. Despite his robust contributions, expanding his focus to include interdisciplinary approaches and increasing collaborative efforts could further enhance his impact. Overall, Aktas’s innovative research and technical skills make him a notable candidate for the Research for Best Researcher Award.

Profile

Education

Gultekin Aktas is a distinguished academic with extensive expertise in civil engineering. He completed his PhD at Dicle University, Diyarbakir, Turkey, where he has been associated with the Engineering Faculty since 1995. His educational background in civil engineering has provided him with a solid foundation in structural analysis, computational methods, and practical applications. During his doctoral studies, Aktas focused on advanced topics in structural dynamics and computational modeling, which have significantly influenced his subsequent research. His work integrates theoretical insights with practical challenges, reflecting his deep understanding of both fundamental concepts and real-world engineering issues. Aktas’s ongoing affiliation with Dicle University highlights his commitment to academic excellence and his role in advancing civil engineering knowledge through both teaching and research.

Professional Experience

Dr. Gultekin Aktas is a distinguished academic in civil engineering, holding a position at the Engineering Faculty of Dicle University in Diyarbakir, Turkey, since 1995. His extensive professional experience encompasses a broad range of research and teaching roles. Aktas has focused on innovative areas such as the behavior of fresh concrete under vibration, finite grid solutions for circular plates, and computer-aided design algorithms for precast concrete elements. His research has been published in leading journals, including Structural Engineering and Mechanics and KSCE Journal of Civil Engineering. Aktas’s expertise lies in employing advanced computational methods and theoretical models to address complex engineering problems, reflecting his commitment to both practical and theoretical advancements in structural engineering. His contributions to the field are marked by a strong emphasis on experimental validation and computational analysis, highlighting his significant role in advancing civil engineering research and education.

Research Skills

Gultekin Aktas possesses a diverse set of research skills that underline his expertise in civil engineering. His proficiency in utilizing advanced computational techniques is evident from his work with mass-spring models, artificial neural networks, and finite grid solutions, which he employs to analyze and predict the behavior of structural elements under various conditions. Aktas demonstrates strong technical abilities in developing and validating algorithms for concrete element design and structural dynamic analysis. His research often involves a blend of theoretical modeling and experimental validation, showcasing his capacity to integrate different methodologies to address complex engineering problems. Additionally, his capability to produce high-quality, peer-reviewed publications reflects his thorough understanding of structural engineering principles and computational methods. Aktas’s adeptness at applying both theoretical and practical approaches underscores his comprehensive skill set and contributes significantly to advancements in civil engineering research.

Award and Recognition

Gultekin Aktas has earned notable recognition for his contributions to civil engineering, particularly in the fields of structural dynamics and concrete behavior. His innovative research has been published in leading journals such as Structural Engineering and Mechanics and KSCE Journal of Civil Engineering, underscoring his impact on the field. Aktas’s work, including his studies on the behavior of fresh concrete under vibration and finite grid solutions for circular plates, has significantly advanced understanding and practical applications in structural engineering. Although specific awards or formal recognitions are not listed, his high-quality publications and influential research demonstrate a strong reputation among peers. Aktas’s contributions reflect his dedication to advancing engineering knowledge and solving complex problems, solidifying his standing as a respected researcher in his domain.

Conclusion

Gultekin Aktas is a strong candidate for the Research for Best Researcher Award. His diverse and innovative research contributions to civil engineering, coupled with his technical proficiency and publication record, demonstrate his significant impact in his field. While there are opportunities to broaden his research focus and enhance his collaborative efforts, Aktas’s accomplishments highlight his potential as a leading researcher. His continuous engagement in cutting-edge research and publication makes him a deserving candidate for this prestigious award.

Publications Top Notes

  1. Examination of Precast Concrete Movement Subjected to Vibration Employing Mass-Spring Model with Two Convective Masses
    • Authors: Aktas, G.
    • Journal: Shock and Vibration
    • Year: 2024
    • Citations: 0
  2. Displacement prediction of precast concrete under vibration using artificial neural networks
    • Authors: Aktas, G., Ozerdem, M.S.
    • Journal: Structural Engineering and Mechanics
    • Year: 2020
    • Volume: 74(4), pp. 559–565
    • Citations: 3
  3. Prediction of behavior of fresh concrete exposed to vibration using artificial neural networks and regression model
    • Authors: Aktas, G., Ozerdem, M.S.
    • Journal: Structural Engineering and Mechanics
    • Year: 2016
    • Volume: 60(4), pp. 655–665
    • Citations: 11
  4. Investigation of fresh concrete behavior under vibration using mass-spring model
    • Authors: Aktas, G.
    • Journal: Structural Engineering and Mechanics
    • Year: 2016
    • Volume: 57(3), pp. 425–439
    • Citations: 4
  5. A finite grid solution for circular plates on elastic foundations
    • Authors: Karaşin, H., Gülkan, P., Aktas, G.
    • Journal: KSCE Journal of Civil Engineering
    • Year: 2015
    • Volume: 19(4), pp. 1157–1163
    • Citations: 9
  6. Experimental confirmation for the validity of Ritz method in structural dynamic analysis
    • Authors: Aktas, G., Karasin, A.
    • Journal: Journal of Theoretical and Applied Mechanics (Poland)
    • Year: 2014
    • Volume: 52(4), pp. 981–993
    • Citations: 4
  7. Computer-aided mold design algorithm for precast concrete elements
    • Authors: Aktas, G., Tanrikulu, A.K., Baran, T.
    • Journal: ACI Materials Journal
    • Year: 2014
    • Volume: 111(1), pp. 77–87
    • Citations: 7

 

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