Research Excellence Award
| Mr. Ehsan Govahi |
| Affiliation |
K. N. Toosi University of Technology |
| Country |
Iran |
| Scopus ID |
57224947757 |
| Documents |
3 |
| Citations |
80 |
| h-index |
3 |
| Subject Area |
Engineering |
| Event |
International Forensic Scientist Awards |
| ORCID |
0000-0003-3891-6068 |
Mr. Ehsan Govahi
K. N. Toosi University of Technology, Iran
Mr. Ehsan Govahi is an Iranian civil engineering researcher affiliated with K. N. Toosi University of Technology. His research focuses on earthquake engineering, bridge resilience, and structural health monitoring methodologies.[1]
His studies integrate seismic analysis with machine learning approaches for structural damage detection. Govahi has contributed to multiple peer-reviewed publications in infrastructure engineering and seismic vulnerability assessment.[2][3]
Abstract
This article summarizes the academic profile and engineering contributions of Ehsan Govahi. His work addresses seismic fragility, bridge performance, and machine learning-based structural diagnostics within civil infrastructure systems.[2]
Keywords
Earthquake Engineering; Structural Health Monitoring; Seismic Fragility; Machine Learning; Bridge Engineering; Infrastructure Resilience; Civil Engineering; Neural Networks.
Introduction
Research in earthquake engineering plays a critical role in improving infrastructure resilience and public safety. Ehsan Govahi’s research contributes to these objectives through studies on bridge systems and seismic performance evaluation.[3]
He earned his M.Sc. in Earthquake Engineering from K. N. Toosi University of Technology. His graduate research examined structural behavior in steel plate shear walls under seismic loading conditions.[6]
Research Profile
Govahi’s research profile combines structural engineering with computational analysis techniques. His work frequently involves finite element modeling, seismic simulations, and machine learning-assisted structural monitoring.[7]
He has worked extensively with engineering software platforms including ABAQUS, OpenSees, MATLAB, SAP2000, and Python. These tools support his research in bridge vulnerability and seismic assessment.[7]
Research Contributions
Govahi contributed to studies investigating seismic fragility and mitigation strategies for bridge piers. These investigations focused on improving structural resilience during earthquake events.[4]
His research also explored machine learning methods for identifying local damage in reinforced concrete bridges. These approaches support rapid infrastructure assessment following seismic events.[2]
More recently, he participated in developing convolutional neural network models for detecting seismic damage in moment-frame buildings. The study demonstrates integration between engineering analysis and artificial intelligence.[5]
Publications
- Govahi, E., Salkhordeh, M., & Mohammadi, R. K. (2025). A strengthened convolutional neural network algorithm for identifying the extent of seismic damage in moment-frame buildings.[5]
- Salkhordeh, M., Mirtaheri, M., Rabiee, N., Govahi, E., & Soroushian, S. (2023). A rapid machine learning-based damage detection technique for detecting local damages in reinforced concrete bridges. DOI: 10.1080/13632469.2023.2193277.[2]
- Govahi, E., Salkhordeh, M., & Mirtaheri, M. (2022). Cyclic performance of different mitigation strategies proposed for segmental precast bridge piers. DOI: 10.1016/j.istruc.2021.12.020.[3]
- Salkhordeh, M., Govahi, E., & Mirtaheri, M. (2021). Seismic fragility evaluation of various mitigation strategies proposed for bridge piers. DOI: 10.1016/j.istruc.2021.05.041.[4]
Research Impact
Govahi’s research publications have received approximately 80 citations within engineering and infrastructure studies. His work demonstrates measurable visibility in seismic engineering research.[1]
The integration of machine learning into structural assessment represents a notable aspect of his research impact. His studies contribute to modern infrastructure monitoring and damage evaluation techniques.[2]
Award Suitability
Ehsan Govahi demonstrates strong alignment with the objectives of the Research Excellence Award. His work combines seismic engineering research with computational intelligence applications for infrastructure analysis.[4]
His participation in post-earthquake inspection activities in Kermanshah Province also reflects practical engagement with structural safety and disaster response engineering.[8]
Conclusion
Ehsan Govahi has contributed to research in earthquake engineering, bridge resilience, and machine learning-assisted structural diagnostics. His scholarly activities support continued advancements in infrastructure safety and seismic assessment methodologies.[1]
External Links
References
- Elsevier. (n.d.). Scopus author details: Ehsan Govahi, Author ID 57224947757.https://www.scopus.com/authid/detail.uri?authorId=57224947757
- Salkhordeh, M., et al. (2023). A rapid machine learning-based damage detection technique for detecting local damages in reinforced concrete bridges.https://doi.org/10.1080/13632469.2023.2193277
- Govahi, E., et al. (2022). Cyclic performance of different mitigation strategies proposed for segmental precast bridge piers.https://doi.org/10.1016/j.istruc.2021.12.020
- Salkhordeh, M., Govahi, E., & Mirtaheri, M. (2021). Seismic fragility evaluation of various mitigation strategies proposed for bridge piers.https://doi.org/10.1016/j.istruc.2021.05.041
- Govahi, E., Salkhordeh, M., & Mohammadi, R. K. (2025). A strengthened convolutional neural network algorithm for identifying the extent of seismic damage in moment-frame buildings.
