Innovative Research Award
Vandana Esswein
Bauhaus University, Weimar
Vandana Esswein is a researcher affiliated with Bauhaus University, Weimar, Germany, whose documented research profile is associated with the application of machine learning in civil engineering. The available bibliometric information records 6 documents, 363 citations, and an h-index of 6 in Scopus. [1] These indicators provide a quantitative basis for describing the research profile considered for recognition under the Innovative Research Award presented through the Superior Engineering Research Awards.
Abstract
This academic recognition profile presents the research record of Vandana Esswein, affiliated with Bauhaus University, Weimar, Germany, in the area of machine learning applications in civil engineering. The supplied Scopus information records six documents, 363 citations, and an h-index of 6. [1] These bibliometric indicators are used as contextual evidence for assessing the researcher’s documented scholarly visibility and relevance to an innovation-oriented engineering award. The profile focuses on the intersection of computational methods and civil engineering applications, while maintaining a neutral distinction between bibliometric evidence and broader assessments of research quality.
Keywords
Machine Learning; Civil Engineering; Artificial Intelligence; Computational Engineering; Engineering Informatics; Data-Driven Modeling; Structural Analysis; Predictive Modeling; Research Innovation; Engineering Applications; Bauhaus University Weimar.
Introduction
Machine learning has increasingly become a component of computational research and engineering analysis, enabling researchers to investigate complex relationships within large or heterogeneous datasets. In civil engineering, such approaches may be applied to areas including prediction, classification, monitoring, optimization, infrastructure assessment, and decision support. The research area attributed to Vandana Esswein is identified as machine learning application in civil engineering, placing the researcher within this interdisciplinary field.
The present article is structured as an academic recognition profile rather than a comprehensive biography. Its principal quantitative information is based on the supplied Scopus author record, including the stated author identifier, document count, citation count, and h-index. [1] Bibliometric indicators can assist in describing scholarly visibility, but they do not independently establish the quality, originality, or practical significance of individual research outputs.
Research Profile
The available profile identifies Vandana Esswein with Bauhaus University, Weimar, Germany, and associates the researcher with machine learning applications in civil engineering. The supplied Scopus record lists 6 documents, 363 citations, and an h-index of 6. [1] The citation-to-document ratio calculated from these supplied values is approximately 60.5 citations per document, although such a ratio should be interpreted cautiously because citation distributions can vary substantially among individual publications and research fields.
The researcher’s stated subject area reflects an interdisciplinary connection between machine learning and civil engineering. Such interdisciplinary research can involve the use of computational learning methods to process engineering data, develop predictive models, identify patterns, and support analytical or decision-making workflows. The available information does not provide sufficient detail to attribute specific algorithms, datasets, infrastructure systems, or individual research outcomes beyond the stated subject area.
- Affiliation: Bauhaus University, Weimar, Germany.
- Research area: Machine learning application in civil engineering.
- Scopus author identifier: 57218095932.
- Documents recorded in the supplied profile: 6.
- Citations recorded in the supplied profile: 363.
- h-index recorded in the supplied profile: 6.
Research Contributions
The documented research orientation toward machine learning in civil engineering represents an intersection of artificial intelligence and established engineering methodologies. In this context, machine learning can complement conventional analytical approaches by providing computational techniques for extracting information from engineering datasets and supporting prediction-oriented tasks. The precise contribution of individual studies, however, should be evaluated from their respective publications rather than inferred solely from bibliometric indicators.
From the information supplied for this profile, the principal contribution area can be characterized as the application of data-driven and machine-learning concepts to civil engineering problems. This positioning is relevant to contemporary engineering research because digital methods are increasingly integrated into computational modeling, monitoring, infrastructure management, and engineering decision-support systems.
Publications
The supplied Scopus profile records 6 documents associated with the researcher. [1] The information provided for this recognition article does not include the titles, publication years, journals, conference proceedings, co-authors, or DOI identifiers of the individual documents. Accordingly, specific publication titles and DOI numbers are not reproduced here to avoid attributing bibliographic information that has not been supplied or independently verified.
For a complete publication-level assessment, individual documents should be reviewed through authoritative bibliographic records and, where available, their publisher pages and DOI registrations. Such verification can establish publication metadata independently of aggregate citation indicators.
Research Impact
The supplied bibliometric record reports 363 citations across 6 documents and an h-index of 6. [1] These values indicate measurable citation activity associated with the documented author profile. Citation counts provide one quantitative perspective on scholarly visibility, while the h-index summarizes the number of publications that have reached at least the corresponding citation threshold.
On the basis of the supplied figures, the profile has an average of approximately 60.5 citations per indexed document. This calculated value should not be interpreted as a measure of average research quality, since citation patterns are influenced by publication age, field-specific citation practices, collaboration, document type, and other factors. The available evidence therefore supports a bibliometric description of research impact rather than an absolute assessment of research significance.
Award Suitability
The Innovative Research Award is presented in the context of the Superior Engineering Research Awards. The documented subject area of machine learning application in civil engineering aligns with the broader theme of engineering innovation through the integration of computational intelligence and engineering practice. The supplied Scopus indicators further provide an objective bibliometric basis for considering the researcher’s documented scholarly activity. [1]
Award suitability should nevertheless be understood as a recognition assessment based on the evidence available for the profile. Bibliometric measures can support such an assessment, but a complete evaluation of innovation would ideally consider the originality of methods, technical contribution, reproducibility, practical relevance, publication quality, collaboration, and documented influence on engineering practice or subsequent research.
- The documented research area connects machine learning with civil engineering applications.
- The supplied profile records 6 Scopus documents.
- The supplied profile records 363 citations.
- The supplied profile records an h-index of 6.
- The affiliation is identified as Bauhaus University, Weimar, Germany.
Conclusion
Vandana Esswein’s supplied research profile is associated with Bauhaus University, Weimar, Germany, and focuses on machine learning applications in civil engineering. The reported Scopus record contains 6 documents, 363 citations, and an h-index of 6. [1] Together, these indicators provide a concise quantitative representation of the documented scholarly record and support the relevance of the profile to an innovation-oriented engineering recognition.
The Innovative Research Award profile emphasizes the interdisciplinary relationship between computational intelligence and civil engineering. Further assessment at the publication level would be appropriate for establishing the specific technical innovations, methodologies, and applications represented by the researcher’s individual works.
External Links
References
- Scopus author details: Vandana Esswein, Author ID 57218095932.
Scopus. https://www.scopus.com/authid/detail.uri?authorId=57218095932
- Evaluation of Machine Learning and Web-Based Process for Damage Score Estimation of Existing Buildings
https://www.mdpi.com/2075-5309/12/5/578
- A Synthesized Study Based on Machine Learning Approaches for Rapid Classifying Earthquake Damage Grades to RC Buildings
https://www.mdpi.com/2076-3417/11/16/7540
- ML-EHSAPP: a prototype for machine learning-based earthquake hazard safety assessment of structures by using a smartphone app
https://www.tandfonline.com/doi/full/10.1080/19648189.2021.1892829