Ali Amini

Mr. Ali Amini

Amirkabir University of Technology
orcid

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2021 in Applied Soft Computing
Verified
Journal Article
Closed access
Ali AminiAli Amini
Ali Amini
AAA. Abdollahi
A. Abdollahi
Mohammad Amin Hariri-ArdebiliMohammad Amin Hariri-Ardebili
Mohammad Amin Hariri-Ardebili
ULUpmanu Lall
Upmanu Lall

4 July 2024 in Engineering Applications of Artificial Intelligence
Verified
Journal Article
Open access
AAAzam Abdollahi
Azam Abdollahi
DLDeli Li
Deli Li
JDJian Deng
Jian Deng
Ali AminiAli Amini
Ali Amini

27 February 2022 in Reliability Engineering & System Safety
Verified
Journal Article
Closed access
AAAzam Abdollahi
Azam Abdollahi
Ali AminiAli Amini
Ali Amini
Mohammad Amin Hariri-ArdebiliMohammad Amin Hariri-Ardebili
Mohammad Amin Hariri-Ardebili


26 January 2021 in Water
Verified
Journal Article
Open access
PDF file
Mohammad Amin Hariri-ArdebiliMohammad Amin Hariri-Ardebili
Mohammad Amin Hariri-Ardebili
GMGolsa Mahdavi
Golsa Mahdavi
AAAzam Abdollahi
Azam Abdollahi
Ali AminiAli Amini
Ali Amini
Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue problem. The situation becomes even more complicated if the material distribution is assumed to be heterogeneous within the dam body (as opposed to conventional isotropic homogeneous relationship). This paper proposes a hybrid Random Field (RF)–Polynomial Chaos Expansion (PCE) surrogate model for uncertainty quantification and sensitivity assessment of dams. For different vibration modes, the most sensitive spatial locations within dam body are identified using both Sobol’s indices and correlation rank methods. Results of the proposed hybrid model is further validated using the classical random forest regression method. The outcome of this study can improve the results of system identification and dynamic analysis by properly determining the vibration characteristics.

1 July 2021 in Journal of Constructional Steel Research
Verified
Journal Article
Closed access
SJSeyed Alireza Jalali
Seyed Alireza Jalali
Ali AminiAli Amini
Ali Amini
IMIman Mansouri
Iman Mansouri
JHJong Wan Hu
Jong Wan Hu