Published on by Architecture Engineers

Innovative Research Award

Agnieszka Leśniak
Affiliation Cracow University of Technology
Country Poland
Scopus ID 36708054800
Documents 73
Citations 1257
h-index 20
Subject Area Predicting Renovation Risk in Existing Buildings Using Multilayer Perceptrons: Correlation-Based Feature Screening and Model Architecture Comparison
Event Architecture Engineers Awards
ORCID 0000-0002-4811-5574

Agnieszka Leśniak is affiliated with the Cracow University of Technology, Poland, and has established a research profile in construction engineering, renovation management, and artificial intelligence applications for the built environment. Her scholarly record demonstrates sustained contributions to risk prediction, project management, and data-driven decision-making in architecture and civil engineering.[1]

Abstract

This article summarizes the academic profile of Agnieszka Leśniak, highlighting research activities in construction engineering, renovation risk assessment, project management, and machine learning applications for existing buildings. Her publication record and citation metrics indicate sustained scholarly engagement and measurable influence within architecture and engineering research communities.[1][2]

Keywords

Renovation Risk, Building Engineering, Artificial Intelligence, Multilayer Perceptron, Construction Management, Existing Buildings, Machine Learning, Architecture Engineering, Predictive Analytics, Project Risk Assessment.[2]

3. Introduction

Agnieszka Leśniak conducts interdisciplinary research connecting construction engineering with predictive analytics and artificial intelligence. Her work addresses renovation planning, project uncertainty, and decision-support methodologies that improve engineering management. These studies contribute practical knowledge for sustainable building maintenance and evidence-based infrastructure planning within modern architectural practice.[1][3]

4. Research Profile

Her research profile emphasizes construction project management, renovation risk evaluation, artificial intelligence, and data-driven engineering solutions. Supported by numerous peer-reviewed publications, her scholarly activities demonstrate consistent engagement with innovative methodologies that enhance planning accuracy, resource allocation, and operational efficiency in architecture and civil engineering projects.[1][2]

5. Research Contributions

Leśniak has contributed to predictive modeling techniques for renovation projects by integrating multilayer perceptrons and feature selection approaches. Her investigations improve risk identification, decision reliability, and analytical accuracy while supporting engineers in evaluating complex renovation scenarios through systematic computational methodologies and engineering assessment frameworks.[2][3]

6. Publications

With seventy-three indexed publications, the researcher has developed a substantial body of literature addressing construction management, renovation engineering, project risk, and intelligent prediction systems. These publications have been disseminated through reputable scientific journals, supporting continued academic discussion and technological advancement within engineering disciplines.[1][3]

7. Research Impact

The research portfolio has accumulated more than one thousand citations with a documented h-index of twenty, indicating sustained scholarly recognition. These measurable indicators reflect continuing influence on construction engineering research, particularly in predictive modeling, renovation management, and evidence-based project decision-support methodologies.[1][2]

8. Award Suitability

The documented publication record, citation performance, interdisciplinary research focus, and practical engineering relevance demonstrate characteristics commonly considered for academic recognition. Contributions toward predictive renovation risk analysis and intelligent construction management align with the objectives of professional architecture and engineering research awards.[1][2]

9. Conclusion

Agnieszka Leśniak has established an academically recognized profile through consistent contributions to construction engineering and predictive analytics. Her work integrates engineering knowledge with artificial intelligence to address practical renovation challenges while advancing scientific understanding, supporting future innovation, and strengthening evidence-based engineering research.[1][3]

11. References

  1. Elsevier. (n.d.). Scopus Author Details: Agnieszka Leśniak, Author ID 36708054800. Scopus.https://www.scopus.com/authid/detail.uri?authorId=36708054800
  2. ORCID. (n.d.). Agnieszka Leśniak ORCID Record.https://orcid.org/0000-0002-4811-5574
  3. Leśniak, A. (2023). Predicting Renovation Risk in Existing Buildings Using Multilayer Perceptrons: Correlation-Based Feature Screening and Model Architecture Comparison.DOI:
    https://doi.org/10.3390/buildings13102627
Agnieszka Leśniak | AI and Automation in Architecture | Innovative Research Award

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