Amir Hossein Asjodi-•-KNTU

Biography

Dr. Amir Hossein Asjodi is an Assistant Professor of Earthquake Engineering at K.N. Toosi University of Technology. His research focuses on applying artificial intelligence in earthquake and structural engineering, using image processing and machine learning to automate and solve classical engineering challenges.
Dr. Asjodi also serves as a reviewer for leading civil engineering journals and teaches academic courses such as Machine Learning, Advanced Engineering Mathematics, Engineering Statics, Mechanics of Materials, and Structural Analysis. He is open to new collaborations in both research and teaching.

Educational Background

• 2020-2023: Ph.D. in Earthquake Engineering
-Sharif University of Technology
-Dissertation: Post-earthquake damage assessment of structural walls using image processing and machine learning techniques
• 2018-2020: M.Sc. in Structural Engineering
-Sharif University of Technology
-Thesis: Arc Length method for extracting crack pattern characteristics
• 2014-2018: B.Sc. in Civil Engineering
-K.N. Toosi University of Technology

Awards and Honors

• Winner of Dr. Tavakoli award from Sharif University of Technology as top doctoral candidate (2023)
• Ranked 1^st among graduated students of all Ph.D. civil engineering programs at Sharif University of Technology (2022)
• Academic scholarship on the memorial of flight PS-752 passengers, Sharif UT (2022)
• Ranked 1^st among graduated students of the B.Sc. civil engineering program at K.N Toosi University of Technology (2018)

Research Interest

• Computer vision, Image processing & Machine Learning
• Artificial Intelligence (AI)
• Structural Damage Assessment
• Structural Health Monitoring (SHM)
• Nonlinear Dynamic Analysis
• Automation of Civil Engineering Problems

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1- Asjodi AH , Saeidi S, Dolatshahi KM, Burton HV (2024) Quantifying Hybrid Failure Modes of Unreinforced Masonry Walls through Experimental Data Analysis. Journal of Structural Engineering, 150:04024155. https:// doi:10.1061/JSENDH.STENG-13028 (Link)
2- Hamidia M, Sheikhi M, Asjodi AH , Dolatshahi KM (2024) Computer vision-based quantification of updated stiffness for damaged RC columns after earthquake. Advances in Engineering Software 190:103597. https://doi.org/10.1016/j.advengsoft.2024.103597 (Link)
3- Rezaei S, Dolatshahi KM, Asjodi AH (2023) Multivariable fragility curves for unreinforced masonry walls. Bulletin of Earthquake Engineering 21:3357–3398. https://doi.org/10.1007/s10518-023-01649-3 (Link)
4- Asjodi AH , Dolatshahi KM (2023) Extended fragility surfaces for unreinforced masonry walls using vision-derived damage parameters. Engineering Structure 278:115467. https://doi.org/10.1016/j.engstruct.2022.115467 (Link)
5- Asjodi AH , Dolatshahi KM, Burton H V. (2023) Three-dimensional fragility surface for reinforced concrete shear walls using image-based damage features. Earthquake Engineering & Structural Dynamics 52:2533–2553. https://doi.org/10.1002/eqe.3832 (Link)
6- Hamidia M, Mansourdehghan S, Asjodi AH , Dolatshahi KM (2022) Machine learning-aided scenario-based seismic drift measurement for RC moment frames using visual features of surface damage. Measurement 205:112195. https://doi.org/10.1016/j.measurement.2022.112195 (Link)
7- Asjodi AH , Dolatshahi KM (2022) Peak drift ratio estimation for unreinforced masonry walls using visual features of damage. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-022-01523-8 (Link)
8- Hamidia M, Mansourdehghan S, Asjodi AH , Dolatshahi KM (2022) Machine learning-based seismic damage assessment of non-ductile RC beam-column joints using visual damage indices of surface crack patterns. Structures 45:. https://doi.org/10.1016/j.istruc.2022.09.010 (Link)
9- Mansourdehghan S, Dolatshahi KM, Asjodi AH (2022) Data-driven damage assessment of reinforced concrete shear walls using visual features of damage. Journal of Building Engineering 53:104509. https://doi.org/10.1016/j.jobe.2022.104509 (Link)
10- Asjodi AH , Dolatshahi KM, Ebrahimkhanlou A (2022) Spatial analysis of damage evolution in cyclic-loaded reinforced concrete shear walls. Journal of Building Engineering 49:. https://doi.org/https://doi.org/10.1016/j.jobe.2022.104032 (Link)
11- Asjodi AH , Daeizadeh MJ, Hamidia M, Dolatshahi KM (2021) Arc Length method for extracting crack pattern characteristics. Structural Control and Health Monitoring 28:. https://doi.org/10.1002/stc.2653 (Link)