Şahin Tolga Güvel | Engineering | Research Excellence Award

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Şahin Tolga Güvel | Engineering | Research Excellence Award

Osmaniye Korkut Ata Üniversitesi | Turkey

Assist. Prof. Dr. Şahin Tolga Güvel is an Assistant Professor in the Department of Civil Engineering, Project Management Division, at Osmaniye Korkut Ata University. He earned his PhD in Civil Engineering from Çukurova University with a focus on occupational health and safety practices in construction projects, following a master’s degree that examined the mechanical properties of high-strength concrete. His academic and research interests center on construction management, project management, occupational health and safety, and the application of modern technologies such as Building Information Modeling (BIM) in construction processes. He has also supervised graduate-level research, including studies on integrating BIM with material procurement and inventory management in construction projects

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Citations
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Documents
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3


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Featured Publications


The level of implementation of occupational health and safety legislation in the Turkish construction sector

– Çukurova University Journal of Engineering-Architecture Faculty, 2018 (Cited by 9)


Diversification of rebar standard lengths and optimization to reduce rebar waste rate

– Gazi University Journal of Engineering and Architecture, 2021 (Cited by 3)


A novel special length rebar order approach based on AI optimization techniques for reduction of rebar cutting waste

– Journal of Construction Engineering, Management & Innovation, 2023 (Cited by 2)

Nandha Gopal | Engineering | Editorial Board Member

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Nandha Gopal | Engineering | Editorial Board Member

NANDHA COLLEGE OF TECHNOLOGY | India

Dr. N. Nandhagopal is a distinguished academic and researcher in Electronics, Communication Engineering, and Embedded System Technology, recognized for his extensive contributions to engineering education, research innovation, and academic leadership. With a strong foundation in electronics and applied electronics, further strengthened by advanced degrees including an M.E., Ph.D., and a highly commended D.Sc., he has developed deep expertise in embedded systems, medical image processing, and intelligent computational techniques. His doctoral research, focused on computer-aided diagnosis for automatic detection of brain tumors using MRI imaging, reflects his commitment to impactful, technology-driven healthcare solutions. Over the course of his career, he has held progressively responsible positions across several engineering institutions, beginning as a lecturer and advancing to Assistant Professor, Associate Professor, Professor, Head of Department, and Principal. His leadership roles demonstrate his ability to guide academic programs, mentor faculty, and enhance institutional quality. Throughout his professional journey, he has contributed to curriculum development, research supervision, and the implementation of innovative teaching methodologies aligned with AICTE standards. Dr. Nandhagopal is known for fostering research culture, promoting interdisciplinary collaboration, and integrating emerging technologies into engineering education. His strong academic background, combined with over a decade of teaching and administrative experience, highlights his dedication to advancing engineering knowledge and shaping the next generation of engineers.

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Featured Publications

Nandhagopal, N., & Karnan, M. (2010). Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques. In Proceedings of the 2010 IEEE International Conference on Computational Intelligence and Computing Research (pp. 241–246). IEEE.

Navaneethan, S., & Nandhagopal, N. (2021). RE-PUPIL: Resource efficient pupil detection system using the technique of average black pixel density. Sādhanā, 46(3), 114.

Satya Sreedhar, P. S., & Nandhagopal, N. (2022). Classification similarity network model for image fusion using ResNet50 and GoogLeNet. Intelligent Automation & Soft Computing, 31(3), 1–12.

Radhakrishnan, M., Panneerselvam, A., & Nachimuthu, N. (2020). Canny edge detection model in MRI image segmentation using optimized parameter tuning method. Intelligent Automation & Soft Computing, 26(6), 1–10.

Nandhagopal, N., Navaneethan, S., Nivedita, V., Parimala, A., & Valluru, D. (2021). Human eye pupil detection system for different iris database images. Journal of Computational and Theoretical Nanoscience, 18(4), 1239–1242.

Nadhir Al-Ansari | Engineering | Excellence in Citation Achievement Award

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Nadhir Al-Ansari | Engineering | Excellence in Citation Achievement Award

LuleaUniversity of Technology | Sweden

Professor Nadhir Al-Ansari is a distinguished scholar in civil, environmental, and natural resources engineering, renowned for his extensive contributions to water resources and environmental sustainability. Currently serving at Luleå University of Technology in Sweden, he has held multiple academic leadership roles throughout his career, including Dean and Head of Department, and has executed more than 60 major research projects across Iraq, Jordan, and the United Kingdom. His prolific scholarly output includes over 790 research articles published in international and national journals, numerous book chapters, 21 books and special issues, as well as a patent related to the physical separation of iron oxides. A dedicated mentor, he has supervised more than 70 postgraduate students across various universities, fostering the next generation of experts in water and environmental engineering. Professor Al-Ansari’s work has been widely recognized through numerous prestigious awards, such as the Mesopotamia Prize for Water Resources, honors from the Iraqi Ministry of Higher Education and Scientific Research, awards from the Ministry of Water and Irrigation in Jordan, and the British Council’s 70th Anniversary recognition as one of the top five scientists in Cultural Relations. He is also the recipient of several scientific and community service accolades from organizations in Iraq, Jordan, Turkey, and beyond. Actively engaged in global scientific discourse, he serves on the editorial boards of 24 international journals and maintains membership in several leading professional societies, including the Chartered Institution of Water and Environment Management, the International Association of Hydrological Sciences, the International Commission for Continental Erosion, and the Arab Integrated Water Resources Management Network. He also founded and previously presided over the Rafidain Organization for Water Resources Protection and Development, reflecting his lifelong commitment to advancing sustainable water resource management and environmental protection worldwide.

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Featured Publications

Nguyen, Q. H., Ly, H. B., Ho, L. S., Al-Ansari, N., Le, H. V., Tran, V. Q., Prakash, I., … (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021(1), Article 4832864.

Kumar, R., Qureshi, M., Vishwakarma, D. K., Al-Ansari, N., Kuriqi, A., Elbeltagi, A., … (2022). A review on emerging water contaminants and the application of sustainable removal technologies. Case Studies in Chemical and Environmental Engineering, 6, 100219.

Tao, H., Hameed, M. M., Marhoon, H. A., Zounemat-Kermani, M., Heddam, S., … (2022). Groundwater level prediction using machine learning models: A comprehensive review. Neurocomputing, 489, 271–308.

Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J. J., … (2020). Flood detection and susceptibility mapping using Sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbors. Remote Sensing, 12(2), 266.

Al-Ansari, N. (2013). Management of water resources in Iraq: Perspectives and prognoses. Engineering, 5(6), 667–684.

Nhu, V. H., Shirzadi, A., Shahabi, H., Singh, S. K., Al-Ansari, N., Clague, J. J., … (2020). Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve Bayes tree, artificial neural network, and support vector machine models. International Journal of Environmental Research and Public Health, 17(8), 2749.