Nima graduated with a bachelor's degree in civil engineering and a master's degree in structural civil engineering from the University of Tabriz (One of the top ten universities in Iran). He is currently studying for his 2nd Ph.D. at the University of Miami (UM) with distinguished professor Dr. Antonio Nanni. He got his 1st Ph.D. at the Iran University of Science and Technology (IUST) with distinguished professor Dr. Ali Kaveh. He worked on the subject of steel structures, particularly in the experimental and numerical investigation of Steel braced frames with Dr. Siamak Talatahari. In addition, he has been actively involved in engineering optimization, especially Evolutionary algorithms, with Professor Dr. Seyed Ali Mirjalili. His main research interests lie in Artificial Intelligence, Machine Learning, FRP, Concrete, and Metaheuristic algorithms. In addition, he has been actively involved in engineering optimization, especially evolutionary algorithms. he has been engaged in research in single and multi-objective engineering optimization, especially in solving large-scale and practical structural design problems. Recently, he has been working on artificial Intelligence techniques and applications for FRP-Integrated concrete structures.
We are excited to announce that the next speaker in the CAE Spring 2024 Seminar Series will be Nima Khodadadi, PhD student, Civil & Architectural Engineering at the University of Miami. Please note that this is a joint CAE seminar, mandatory for all CAE PhD students. The talk is titled “Data-Driven PSO-CatBoost Machine Learning Model to Predict the Compressive Strength of CFRP-Confined Circular Concrete Specimens” and will take place through Zoom on Friday, Feb 2 at 11: 00 AM.
Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm's performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented (PI)
Wulkan Family American Public Transportation Foundation Endowed Scholarship Fund is awarded to a graduate student that excels in the field of public transportation. The winner is awarded with educational stipend; appropriate recognition in APTA website; paid travel expenses and attendance fees to one APTA annual convention; and an industry mentor.
“With such big data sets, we need to apply machine learning and AI algorithms to find the most sustainable solutions,” Khodadadi said. “Since concrete produces more than 8 percent of the world’s carbon dioxide emissions, civil engineers have an excellent opportunity to contribute to sustainability on a global basis.”A native of Iran, Khodadadi earned a bachelor's degree in civil engineering and a master's degree in structural civil engineering from the University of Tabriz. He obtained his first Ph.D. in artificial intelligence at the Iran University of Science and Technology, one of the nation’s top universities. To date, he has written more than 50 papers and 10 book chapters relating to concrete and sustainability, including three since coming to the U.S. last year...
Nature-inspired metaheuristic approaches draw their core idea from biological evolution in order to create new and powerful competing algorithms. Such algorithms can be divided into evolution-based and swarm-based algorithms. This paper proposed a new nature-inspired optimizer called the Greylag Goose Optimization (GGO) algorithm. The proposed algorithm (GGO) belongs to the class of swarm-based algorithms and is inspired by the Greylag Goose. Geese are excellent flyers and during their seasonal migrations, they fly in a group and can cover thousands of kilometers in a single flight. ..
An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the intelligent group foraging behaviors exhibited by electric eels in nature. The algorithm mathematically models four key foraging behaviors: interaction, resting, hunting, and migration, to provide both exploration and exploitation during the optimization process. In addition, an energy factor is developed to manage the transition from global search to local search and the balance between exploration and exploitation in the search space. EEFO reveals various foraging patterns based on the foraging characteristics of electric eels. In this study, such dynamic patterns and behaviors are mathematically imitated to design an effective global optimizer.
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