Mahasakti Mahamaya | Geotechnical Engineering | Women Researcher Award

Published on by Civil Engineering

Dr. Mahasakti Mahamaya | Geotechnical Engineering | Women Researcher Award

Associate Professor | OP Jindal University | India

Dr. Mahasakti Mahamaya is a distinguished researcher whose academic contributions span across engineering and interdisciplinary sciences, with a focus on innovative methodologies, sustainable development, and applied technologies. Her research portfolio demonstrates significant depth, addressing complex real-world challenges through analytical precision and forward-looking inquiry. Dr. Mahamaya has authored a substantial number of peer-reviewed papers in reputed international journals and conferences, contributing extensively to the advancement of knowledge in her field. Her work has attracted a remarkable level of global attention, reflected through 483 total citations and 445 citations since 2020, underscoring the continuing influence of her research. With an h-index of 10 and an i10-index of 10, Dr. Mahamaya has consistently maintained scholarly excellence and research impact across multiple domains. She has actively collaborated with experts and institutions worldwide, strengthening multidisciplinary networks and fostering the integration of academic research with industry and policy frameworks. Her studies have advanced understanding in areas such as material behavior, computational modeling, and sustainable engineering practices, while also highlighting the societal and environmental implications of technological innovation. Through mentorship, publication, and collaborative initiatives, Dr. Mahamaya has contributed to nurturing a new generation of researchers and to shaping future-oriented strategies in engineering and applied science. Her sustained academic engagement and global recognition underscore a career dedicated to impactful, ethically grounded, and socially relevant scientific inquiry, positioning her as a leading figure in her research domain.

Profile: Scopus | ORCID | Google Scholar
Featured Publications:

Suman, S., Mahamaya, M., & Das, S. K. (2016). Prediction of maximum dry density and unconfined compressive strength of cement stabilised soil using artificial intelligence techniques. International Journal of Geosynthetics and Ground Engineering, 2(2), 1–11.

Mahamaya, M., Das, S. K., Reddy, K. R., & Jain, S. (2021). Interaction of biopolymer with dispersive geomaterial and its characterization: An eco-friendly approach for erosion control. Journal of Cleaner Production, 127778.

Parhi, P. S., Garanayak, L., Mahamaya, M., & Das, S. K. (2017). Stabilization of an expansive soil using alkali activated fly ash based geopolymer. International Congress and Exhibition "Sustainable Civil Infrastructures".

Mahamaya, M., & Das, S. K. (2017). Characterization of mine overburden and fly ash as a stabilized pavement material. Particulate Science and Technology, 35(6), 660–666.

Das, S. K., Mahamaya, M., & Reddy, K. R. (2020). Coal mine overburden soft shale as a controlled low strength material. International Journal of Mining, Reclamation and Environment, 34(10), 725–747.

Ramin Vafaei Poursorkhabi | Geotechnical Engineering | Best Researcher Award

Published on by Civil Engineering

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.