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
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.
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.
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