Mina Mortazavi
Lecturer
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About
Mina Mortazavi is a Lecturer at the University of Technology Sydney, Australia. Her research areas include structural engineering, seismic performance, and advanced material applications, as evidenced by her recent articles on post-buckling analysis, polymer concrete panels, and the behavior of composite structures under various conditions. She employs deep neural networks and hybrid metaheuristic algorithms to address complex engineering problems. Mina's contributions to the field also encompass the study of earthquake-induced effects on structures and innovative repair techniques for fire-damaged elements.
Articles (11)
Year:
2025
Year:
2024
Year:
2024
Year:
2023
Year:
2023
Year:
2023
The aim of this paper is to study the performance of a composite floor system at different heat stages using artificial intelligence to derive a sustainable design and to select the most critical factors for a sustainable floor system at elevated temperatures. In a composite floor system, load bearing is due to composite action between steel and concrete materials which is achieved by using shear connectors. Although shear connectors play an important role in the performance of a composite floor system by transferring shear force from the concrete to the steel profile, if the composite floor system is exposed to high temperature conditions excessive deformations may reduce the shear-bearing capacity of the composite floor system. Therefore, in this paper, the slip response of angle shear connectors is evaluated by using artificial intelligence techniques to determine the performance of a composite floor system during high temperatures. Accordingly, authenticated experimental data on monotonic loading of a composite steel-concrete floor system in different heat stages were employed for analytical assessment. Moreover, an artificial neural network was developed with a fuzzy system (ANFIS) optimized by using a genetic algorithm (GA) and particle swarm optimization (PSO), namely the ANFIS-PSO-GA (ANPG) method. In addition, the results of the ANPG method were compared with those of an extreme learning machine (ELM) method and a radial basis function network (RBFN) method. The mechanical and geometrical properties of the shear connectors and the temperatures were included in the dataset. Based on the results, although the behavior of the composite floor system was accurately predicted by the three methods, the RBFN and ANPG methods represented the most accurate values for split-tensile load and slip prediction, respectively. Based on the numerical results, since the slip response had a rational relationship with the load and geometrical parameters, it was dramatically predictable. In addition, slip response and temperature were determined as the most critical factors affecting the shear-bearing capacity of the composite floor system at elevated temperatures.
Year:
2023
Year:
2023
Year:
2023
Year:
2022
Year:
2021
Collaborators (7)
Peyman Mehrabi
University of Western Sydney
Harry Far
Senior Lecturer in Structural Engineering
Faculty of Engineering and Information Technology, University of Technology Sydney
Adham E. Ragab
College of Engineering
Aria Ghabussi
Graduate Teaching Assistant
Texas Tech University
Mohammad Talebi-Kalaleh
University of Alberta
Raghu Betha
Texas Tech University
Ahmed M. El-Sherbeeny
College of Engineering
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