Manvendra Singh Chauhan | Civil Engineering | Best Researcher Award

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Assoc. Prof. Dr. Manvendra Singh Chauhan | Civil Engineering | Best Researcher Award

Babasaheb Bhimrao Ambedkar University, India

Assoc. Prof. Dr. Manvendra Singh Chauhan, an accomplished academician and researcher in Civil Engineering, currently serves as Head of the Department of Civil Engineering at Babasaheb Bhimrao Ambedkar University (A Central University), Lucknow, India. He earned his Ph.D. in Civil Engineering from the Indian Institute of Technology (Banaras Hindu University), Varanasi in 2015, with research on modeling the River Ganga at the Varanasi bend, following his M.Tech. in Hydraulics and Water Resources Engineering and B.Tech. in Civil Engineering. With more than a decade of academic and professional experience, Dr. Chauhan has held faculty and leadership positions at reputed institutions including RV Institute of Technology, Holy Mary Institute of Technology & Science (where he also served as Vice-Principal), and Chitkara University, alongside prior industry experience with Simplex Infrastructures and SDCE Projects. His research interests span hydraulics and water resources, river modeling, sedimentation and erosion, environmental remediation, sustainable construction materials, and applications of geoinformatics in watershed management. He possesses strong research skills in hydrological modeling, GIS applications, data analysis, statistical modeling, AutoCAD, SPSS, and advanced tools for river flow and sediment studies. Dr. Chauhan has published widely in reputed Scopus and SCI-indexed journals such as Journal of Hydroinformatics, Asian Journal of Civil Engineering, Technologies, and Materials Today Proceedings, with a record of 9 indexed documents, 122 citations, and an h-index of 3, in addition to authoring books and Springer book chapters. His innovative outlook is reflected in patents including the granted “Motorized Water Sampler for Water Bodies” (2023) and an IoT-based intelligent irrigation system. Recognized for his contributions, he has been actively involved in organizing international conferences, workshops, and faculty development programs, and is a member of professional bodies such as the Institution of Engineers (India), Indian Association of Hydrologists, and Indian Water Resources Society. In conclusion, Dr. Chauhan stands out as a dedicated researcher, innovator, and academic leader whose impactful contributions to civil engineering and water resources continue to advance sustainable solutions for society and inspire future generations of engineers.

Profile: Scopus | ORCID

Featured Publications

Kesarwani, S., Shukla, G., & Chauhan, M. S. (2025). Utilisation of waste corn cob ash in cement concrete: A statistical approach toward environmental sustainability. Asian Journal of Civil Engineering. Advance online publication.

Chauhan, M. S., Omar, P. J., Dikshit, P. K. S., & Dwivedi, S. B. (2025). Development and validation of a rating curve for the Ganga River at the Varanasi bend. Journal of Hydroinformatics, 27(4), 657–669.

Sen, P., Bhattacharya, P., Mukherjee, G., Ganguly, J., Marik, B., Thapliyal, D., Verma, S., Verros, G. D., Chauhan, M. S., & Arya, R. K. (2023). Advancements in doping strategies for enhanced photocatalysts and adsorbents in environmental remediation. Technologies, 11(5), 144.

Fayaz, M., Krishnaiah, R. V., Raju, K. V. B., & Chauhan, M. S. (2023). Study and analysis of strength parameters of concrete with addition of stone dust, PVC, and fibers. Materials Today: Proceedings. Advance online publication.

Fayaz, M., Krishnaiah, R. V., Raju, K. V. B., & Chauhan, M. S. (2023). Experimental study on mechanical properties of concrete using mineral admixtures. Materials Today: Proceedings. Advance online publication.

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