Qingmin Wang | Statistical Computing and Programming | Innovative Research Award

Published on by Statistics

Innovative Research Award

Qingmin Wang,
Zhejiang University.

Qingmin Wang
Affiliation Zhejiang University
Country China
Scopus ID 57322536800
Documents 25
Citations 342
h-index 12
Subject Area Civil Engineering
Event World Statistics Awards
ORCID 0000-0001-6353-7014

Qingmin Wang is a researcher affiliated with Zhejiang University whose indexed scholarly record includes 25 documents, 342 citations, and an h-index of 12. His stated subject area is Civil Engineering. These bibliometric indicators provide a structured overview of his documented research activity and scholarly visibility. [1]

Abstract

Qingmin Wang is affiliated with Zhejiang University, China, and is associated with the field of Civil Engineering. Available bibliometric information records 25 scholarly documents, 342 citations, and an h-index of 12. This article summarizes the documented research profile, contributions, publications, scholarly impact, and relevance to the Innovative Research Award. [1]

Keywords

Civil Engineering; statistical analysis; engineering research; research publications; bibliometrics; scholarly impact; Innovative Research Award; World Statistics Awards.

Introduction

Qingmin Wang is affiliated with Zhejiang University in China and works within Civil Engineering. His documented scholarly profile comprises 25 Scopus-indexed documents, 342 citations, and an h-index of 12. These indicators offer measurable context for reviewing his research activity, publication record, and academic visibility in engineering research. [3]

Research Profile

Wang’s research profile is situated within Civil Engineering and is supported by a documented Scopus author record. The profile identifies Zhejiang University as his affiliation and records 25 documents, 342 citations, and an h-index of 12. These metrics provide quantitative indicators for examining his scholarly output and citation visibility. [1]

Research Contributions

Wang’s documented contributions can be considered through his publication activity and indexed citation record in Civil Engineering. His 25 recorded documents represent a body of scholarly output, while 342 citations indicate subsequent academic referencing. Together, these measures provide evidence for assessing the reach and continuity of his research contributions. [2]

Publications

The available Scopus record identifies 25 documents associated with Qingmin Wang. The indexed publication count provides a measurable basis for describing his scholarly productivity. Individual publication titles, journals, publication years, and DOI information should be verified against authoritative publisher and indexing records before being used for detailed bibliographic analysis. [3]

Research Impact

The recorded citation count of 342 and h-index of 12 provide quantitative measures of Wang’s scholarly visibility within indexed research literature. Citation metrics can help describe how publications have been referenced by subsequent studies, although they should be interpreted alongside publication quality, research context, collaboration, and disciplinary citation practices. [2]

Award Suitability

For the Innovative Research Award, Wang’s documented affiliation, Civil Engineering subject area, publication activity, and citation record provide relevant evidence for consideration. The available metrics indicate an established indexed research profile. Final award assessment should additionally consider submitted research materials, originality, methodological quality, significance, and the evaluation criteria established by the World Statistics Awards. [2]

Conclusion

Qingmin Wang’s academic profile is associated with Zhejiang University and Civil Engineering, with 25 indexed documents, 342 citations, and an h-index of 12. These indicators establish a measurable record of scholarly activity and visibility. Further evaluation can incorporate publication details, research originality, methodological contributions, and supporting academic documentation. [1]

References

  1. Zhao, W., Chang, T., Wang, Q., Li, Y., Cui, Y., Yang, Y., & Sun, B. (2026). Tensile performance of the vertical joint of a novel precast RC shear wall structure with concentrated connection bars: Experimental and numerical investigation. Journal of Building Engineering.

    https://doi.org/10.1016/j.jobe.2026.117242

  2. Zhao, W., Zheng, T., Li, Y., Yang, Y., & Wang, Q. (2025). Shear behavior of keyed vertical joints with lap splices for precast RC frame-shear wall structures. Engineering Structures.

    https://doi.org/10.1016/j.engstruct.2025.121100

  3. Yuan, L., Yang, Y., Wang, Q., Zhao, Q., Cui, Y., & Zhao, W. (2025). Investigation on the dimensions design and anchorage mechanism of headed bars utilized as shear reinforcements.

    https://doi.org/10.1016/j.istruc.2025.108647

Vikas Mehta | Statistical Computing and Programming | Research Excellence Award

Published on by Statistics

Dr. Vikas Mehta | Statistical Computing and Programming | Research Excellence Award

Korean National Institute for International Education | South Korea

Dr. Vikas Mehta is a structural engineer and researcher specializing in seismic performance optimization, sustainable construction materials, and the application of advanced computational and machine learning methodologies to civil infrastructure systems. He completed his Ph.D. in Civil Engineering at Keimyung University, South Korea, where his award-winning doctoral research introduced innovative modifier-based and data-driven techniques for improving shear strength prediction and design accuracy in reinforced concrete beam-column joints. His expertise spans nonlinear finite element modeling, fragility analysis, physics-informed and graph-based machine learning, geospatial analytics, and performance-based seismic assessment, supported by strong proficiency in ETABS, OpenSees, SeismoSoft, Abaqus, MATLAB, Q-GIS, SPSS, Python, PyTorch, WEKA, and OriginPro. Dr. Mehta serves as a Postdoctoral Researcher at the Chonnam National University R&BD Foundation, contributing to advanced safety technologies for nuclear power plant structures under extreme hazard scenarios, including buckling resistance enhancement, retrofit optimization, and complex wind–terrain interaction studies. His professional background includes academic appointments in structural and construction engineering, where he taught subjects in earthquake engineering, finite element analysis, and structural systems while supervising graduate research and contributing to curriculum and laboratory development. Dr. Mehta has authored a substantial body of SCI-indexed research on seismic damage prediction, torsional behavior modeling, hybrid AI-mechanics frameworks, recycled and sustainable materials, computational methods, and structural performance evaluation, complemented by multiple patents in construction materials, damping devices, and waste-based composites. He has presented at leading international and national conferences and contributed to funded collaborative research, including projects involving global academic and industry partners. His professional affiliations include membership in ASCE, the Institute of Physics (AMInstP), IAEME (Fellow), and licensure as a Class-A engineer under the Himachal Pradesh Town and Country Planning Act. Dr. Mehta’s contributions to structural engineering and computational mechanics continue to gain international visibility, reflected in an h-index of 7, over 172 citations, and more than 19 published documents, underscoring his growing influence in machine learning–driven structural design, seismic resilience, and sustainable construction innovation.

Profiles: Scopus | Orcid

Featured Publications

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Corrigendum to “Adaptive simulation and data-driven hybrid modeling for predicting shear strength and failure modes of interior reinforced concrete beam-column joints”.

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Predictive framework for shear strength and failure modes of exterior reinforced concrete beam–column joints using machine learning. Structural Concrete. h.

Sagar, G. S., Mukthi, S., & Mehta, V. (2025). Analyzing compressive, flexural, and tensile strength of concrete incorporating used foundry sand: Experimental and machine learning insights. Archives of Computational Methods in Engineering.

Mehta, V., Thakur, M. S., & Chey, M. H. (2025). Enhancing seismic design accuracy of RC beam-column joints: Modifier-based approach for shear strength predictions. Structures.

Mehta, V., Jang, S. H., & Chey, M. H. (2025). Adaptive simulation and data-driven hybrid modeling for predicting shear strength and failure modes of interior reinforced concrete beam-column joints. Structures.