A
Amir H. Alavi
Researcher at University of Pittsburgh
Publications - 219
Citations - 15536
Amir H. Alavi is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Genetic programming & Metaheuristic. The author has an hindex of 49, co-authored 202 publications receiving 11559 citations. Previous affiliations of Amir H. Alavi include Harvard University & Shahid Beheshti University.
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New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach
TL;DR: The proposed models correlate the concrete true-triaxial strength to mix design parameters and principal stresses, needless of conducting any time-consuming laboratory experiments and demonstrate superior performance to the other existing empirical and analytical models.
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Genetic-based modeling of uplift capacity of suction caissons
TL;DR: In this paper, classical tree-based genetic programming and its recent variants, namely linear genetic programming (LGP) and gene expression programming (GEP) are utilized to develop new prediction equations for the uplift capacity of suction caissons.
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A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization
TL;DR: A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herds optimization method.
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A web server for comparative analysis of single-cell RNA-seq data
TL;DR: An automated pipeline to download, process, and annotate publicly available scRNA-seq datasets to enable large scale supervised characterization and extended supervised neural networks to obtain efficient and accurate representations for sc RNA-seq data are developed.
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Fatigue cracking detection in steel bridge girders through a self-powered sensing concept
TL;DR: In this article, a self-powered piezo-floating-gate (PFG) sensor was used to detect distortion-induced fatigue cracking of steel bridges. But, the results indicate that the proposed method is capable of detecting different damage progression states, especially for the sensors that are located close to the damage location.