Shaokai Wang | Geotechnical Engineering | Research Excellence Award

Dr. Shaokai Wang | Geotechnical Engineering | Research Excellence Award

Lecturer | North China University of Water Resources and Electric Power | China

Dr. Shaokai Wang is a Lecturer at North China University of Water Resources and Electric Power, holding a Ph.D. in Geological Engineering with over five years of experience in teaching and research. His expertise lies in geological and geotechnical engineering, with a strong focus on geological hazard mitigation, loess-related geohazards, and landslide monitoring and early warning systems. He has led and contributed to nationally funded projects supported by the National Natural Science Foundation of China, including research on catastrophic loess discontinuities and innovative biopolymer-based soil improvement technologies. Dr. Wang has published peer-reviewed articles in high-impact international journals such as Engineering Geology and Journal of Asian Earth Sciences, accumulating citations that reflect the relevance of his work. His research integrates field investigations, numerical simulations, and risk modeling to support disaster prevention, sustainable land-use planning, and resilient infrastructure development, contributing meaningfully to societal safety in hazard-prone regions.

Citation Metrics (Scopus)

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Citations
461

Documents
8

h-index
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Top Publications

Static liquefaction capacity of saturated undisturbed loess in South Jingyang platform

Water (Switzerland), 2020 · Open Access

Mukhtiar Ali Soomro | Soil-structure interaction | Excellence in Research Award

Prof. Dr. Mukhtiar Ali Soomro | Soil-structure interaction | Excellence in Research Award

Professor | China University of Mining and Technology | China

Prof. Dr. Mukhtiar Ali Soomro is a geotechnical engineering researcher with a strong publication record in soil–structure interaction, tunnelling, piled foundations, deep excavations, and ground deformation analysis. His research integrates advanced numerical modelling, centrifuge testing, and analytical approaches to investigate the response of piles, piled rafts, embankments, and masonry structures under complex loading and excavation conditions. He has published extensively in high-impact international journals such as Tunnelling and Underground Space Technology, Computers and Geotechnics, Canadian Geotechnical Journal, and Geomechanics and Engineering. His work contributes significantly to safer and more sustainable underground and foundation engineering practices.

Citation Metrics (Scopus)

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Citations 931

Documents 44

h-index
16

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Featured Publications


Three-Dimensional Centrifuge Modelling of Pile Group Responses to Side-by-Side Twin Tunnelling


– Tunnelling and Underground Space Technology, 2014 (Citations: 104)

Load Transfer Mechanism in Pile Group Due to Single Tunnel Advancement in Stiff Clay


– Tunnelling and Underground Space Technology, 2015 (Citations: 97)

IoT-Based Smart Garbage Monitoring & Collection System Using WeMos and Ultrasonic Sensors


– International Conference on Computing, Mathematics and Engineering, 2019 (Citations: 94)

Ramin Vafaei Poursorkhabi | Geotechnical Engineering | Best Researcher Award

Assoc. Prof. Dr. Ramin Vafaei Poursorkhabi | Geotechnical Engineering | Best Researcher Award

Associate Professor | Islamic Azad University | Iran

Assoc. Prof. Dr. Ramin Vafaei Poursorkhabi has built a strong research profile focusing on civil engineering, geotechnical engineering, structural analysis, soil improvement techniques, and the application of artificial intelligence in solving complex engineering challenges. His work spans across diverse areas such as the stabilization of soils through innovative methods like geopolymerization, evaluation of dispersive clay properties, monitoring and analysis of dam structures, and the use of metaheuristic algorithms for seismic response reduction and subsurface modeling. He has contributed significantly to advancements in hydraulic conductivity estimation, environmental optimization in road construction, and the reinforcement of geotechnical stability through geogrid applications. His studies also include offshore platform reliability, wave–structure interaction, and improvements in rubble mound breakwater resistance, showcasing an interdisciplinary approach that connects geotechnical, structural, and coastal engineering. By integrating clustering techniques, fuzzy logic, wavelet-based artificial neural networks, and hybrid optimization methods, he has introduced innovative models to enhance predictive accuracy and engineering design efficiency. Several of his publications highlight practical applications through case studies of large infrastructure projects, including dams, offshore platforms, and municipal roads, providing a blend of theoretical modeling and applied research. Additionally, his collaboration with scholars across multiple institutions has fostered a multidisciplinary approach to engineering problems, producing solutions that are both technically sound and environmentally conscious. The consistent use of computational intelligence tools demonstrates his commitment to bridging traditional engineering with modern machine learning techniques, aiming to optimize performance, reduce risk, and ensure structural safety. His publications in international journals and conference proceedings reflect not only academic contribution but also practical impact in real-world infrastructure development. This research track record establishes Ramin Vafaei Poursorkhabi as an impactful contributor in advancing the fields of geotechnical and structural engineering with strong integration of intelligent systems. 105 Citations 31 Documents 6 h-index View.

Profile: Scopus | ORCID | Research Gate 
Featured Publications:

Using the clustering method to find the final environmental parameters coefficients in road construction projects. (2025). Scientific Reports.

Experimental investigation of a special chemical additive for improving the geotechnical properties of dispersive clay soils. (2025). Results in Engineering.

Estimation of hydraulic conductivity using gradation information through Larsen fuzzy logic hybrid wavelet artificial neural network and combined artificial intelligence models. (2025). Discover Applied Sciences.