Yufeng Jiang | Statistical Applications in Engineering | Best Researcher Award

Assoc Prof. Dr. Yufeng Jiang | Statistical Applications in Engineering | Best Researcher Award

Ocean University of China | China

Assoc Prof. Dr. Yufeng Jiang is an Associate Professor at the Ocean University of China, specializing in the health monitoring and safety assessment of offshore and marine engineering structures. With a strong academic foundation from the Ocean University of China, he has dedicated his career to advancing intelligent damage diagnosis methods that can directly utilize incomplete information while maintaining high noise robustness. He innovatively developed an iterative two-stage damage identification methodology capable of simultaneously locating structural damage and assessing its severity. Dr. Jiang has designed a hardware network of fiber optic sensors for condition monitoring of deepwater pressure-resistant subsea structures and created an intelligent structural health monitoring and early warning system, which has been successfully applied in a 500-meter deep-sea mixed-transport system demonstration project. His research has led to 20 Documents , 10 patents, and collaboration on three major research projects, resulting in a citation count of 111 and an h-index of 6, reflecting the significant impact of his work. Additionally, he has contributed to two consultancy projects and maintained collaborations across multiple institutions, consistently translating innovative research into practical engineering applications. Dr. Jiang continues to advance the field of marine structural safety with a focus on applied intelligence and robust monitoring solutions.

Profiles: Scopus  Orcid

Featured Publications

Liu, Y., Wang, S., Jiang, Y., & Du, J. (2025). A spatial deformation reconstruction method of deep-sea mining riser from sparse inclination measurements. Ocean Engineering.

Wang, C., Luo, D., Guo, Y., Zheng, Z., Jiang, Y., & Du, J. (2025). A novel stochastic model updating method for offshore platforms based on Kriging model with active learning. Ocean Engineering.

Jiang, Y., Ma, C., Wang, S., & Li, Y. (2024). A novel evolutionary algorithm for structural model updating with a hybrid initialization and multi-stage update strategy. Ocean Engineering.

Jiang, Y., Liu, Y., Wang, S., & Rakicevic, Z. (2024). Structural damage classification in offshore structures under environmental variations and measured noises using linear discrimination analysis. Structural Control and Health Monitoring.

Liu, Y., Jiang, Y., Zhao, H., Wang, S., & Han, J. (2023). Experimental investigation on vortex-induced vibration characteristics of a segmented free-hanging flexible riser. Ocean Engineering.

Abhijeet Das | Statistical Applications in Engineering | Machine Learning Award

Dr. Abhijeet Das | Statistical Applications in Engineering | Machine Learning Award

C.V. Raman Global University | India

Dr. Abhijeet Das, Ph.D. in Water Resource Engineering from C.V. Raman Global University, Bhubaneswar, is an accomplished civil engineering researcher specializing in watershed hydrology, hydrological modeling, climate change impact assessment, and GIS-based water resources management. With a strong academic foundation, including M.Tech and B.Tech degrees from Biju Patnaik University of Technology, he has combined rigorous research with nearly a decade of professional and teaching experience. Dr. Das has contributed extensively to collaborative national and international projects across Tunisia, USA, Oman, UK, South Africa, Syria, and Lebanon, focusing on water quality, hydrologic extremes, and sustainable water management through remote sensing, machine learning, and optimization techniques. He has published 88 documents indexed in Scopus, which have received 199 citations, achieving an h-index of 7, reflecting both productivity and the growing impact of his research contributions. His intellectual property portfolio includes over 30 patents filed in water resource engineering, geoinformatics, and environmental sustainability, showcasing innovation and applied problem-solving capacity. Dr. Das has actively engaged in more than 30 seminars, workshops, and international conferences, presenting advancements in civil and water resource engineering. His career trajectory illustrates a blend of academic excellence, applied research, and industry collaboration, making him a promising contributor to the advancement of sustainable infrastructure and water management systems.

Profiles: Scopus Orcid

Featured Publications

Das, A. (2025). An optimization-based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance. Discover Environment.

Das, A., & Mishra, S. (2025). Reimagining biofiltration for sustainable industrial wastewater treatment. Discover Sustainability.

Das, A. (2025). A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India. Discover Sustainability.

Das, A. (2025). Evaluation and prediction of surface water quality status for drinking purposes using integrated water quality indices, GIS approaches, and machine learning techniques. Desalination and Water Treatment.

Das, A., Mishra, S., & Tripathy, B. (2025). Bioplastics: A sustainable alternative or a hidden microplastic threat? Innovative Infrastructure Solutions.