Showing 28 results for Gholizadeh
V. Nzarpour, S. Gholizadeh,
Volume 13, Issue 1 (1-2023)
Abstract
Design optimization of cable-stayed bridges is a challenging optimization problem because a large number of variables is usually involved in the optimization process. For these structures the design variables are cross-sectional areas of the cables. In this study, an efficient metaheuristic algorithm namely, momentum search algorithm (MSA) is used to optimize the design of cable-stayed bridges. The MSA is inspired by the Physics and its superiority over many metaheuristics has been demonstrated in tackling several standard benchmark test functions. In the current work, the performance of MSA is compared with that of two other metaheuristics and it is shown that the MSA is an efficient algorithm to tackle the optimization problem of cable-stayed bridges.
S. Mohammadhosseini , S. Gholizadeh,
Volume 13, Issue 1 (1-2023)
Abstract
The main aim of this study, is to evaluate the seismic reliability of steel concentrically braced frame (SCBF) structures optimally designed in the context of performance-based design. The Monte Carlo simulation (MCS) method and neural network (NN) techniques were utilized to conduct the reliability analysis of the optimally designed SCBFs. Multi-layer perceptron (MLP) trained by back propagation technique was used to evaluate the required structural responses and then the total exceedence probability associated with the seismic performance levels was estimated by the MCS method. Three numerical examples of 5-, 10-, and 15-story SCBFs with fixed and optimal topology of braces are presented and their probability of failure was evaluated considering the resistance characteristics and the seismic loading of the structures. The numerical results indicate that the SCBFs with optimal topology of braces were more reliable than those with fixed topology of braces.
M. Nabati , S. Gholizadeh,
Volume 13, Issue 2 (4-2023)
Abstract
The purpose of the current study is to design steel moment resisting frames for optimal weight in the context of performance-based design. The performance-based design optimization of steel moment frames is a highly nonlinear and complex optimization problem having many local optima. Therefore, an efficient algorithm should be used to deal with this class of structural optimization problems. In the present study, a modified Newton metaheuristic algorithm (MNMA) is proposed for the solution of the optimization problem. In fact, MNMA is the improved version of the original Newton metaheuristic algorithm (NMA), which is a multi-stage optimization technique in which an initial population is generated at each stage based on the results of the previous stages. Two illustrative examples of 5-, and 10-story steel moment frames are presented and a number of independent optimization runs are achieved by NMA and MNMA. The numerical results demonstrate the better performance of the proposed MNMA compared to the NMA in solving the performance-based optimization problem of steel moment frames.
S. Gholizadeh, C. Gheyratmand , N. Razavi,
Volume 13, Issue 3 (7-2023)
Abstract
The main objective of this study is to optimize reinforced concrete (RC) frames in the framework of performance-based design using metaheuristics. Three improved and efficient metaheuristics are employed in this work, namely, improved multi-verse (IMV), improved black hole (IBH) and modified newton metaheuristic algorithm (MNMA). These metaheuristic algorithms are applied for performance-based design optimization of 6- and 12-story planar RC frames. The seismic response of the structures is evaluated using pushover analysis during the optimization process. The obtained results show that the IBH outperforms the other algorithms.
G. Sedghi, S. Gholizadeh, S. Tariverdilo ,
Volume 13, Issue 4 (10-2023)
Abstract
In this paper an enhanced ant colony optimization algorithm with a direct constraints handling strategy is proposed for the optimization of reinforced concrete frames. The construction cost of reinforced concrete frames is considered as the objective function, which should be minimized subject to geometrical and behavioral strength constraints. For this purpose, a new probabilistic function is added to the ant colony optimization algorithm to directly satisfy the geometrical constraints. Furthermore, the position of an ant in each iteration is updated if a better solution is found in terms of objective value and behavioral strength constraints satisfaction. Five benchmark design examples of planar reinforced concrete frames are presented to illustrate the efficiency of the proposed algorithm.
A. Yadbayza-Moghaddam, S. Gholizadeh,
Volume 14, Issue 1 (1-2024)
Abstract
The primary objective of this paper is to propose a novel technique for hybridizing various metaheuristic algorithms to optimize the size of discrete structures. To accomplish this goal, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and enhanced colliding bodies optimization (ECBO) are hybridized to propose a new algorithm called hybrid PSO-ECBO (HPE) algorithm. The performance of the new HPE algorithm is investigated in solving the challenging structural optimization problems of discrete steel trusses and an improvement in results has been achieved. The numerical results demonstrate the superiority of the proposed HPE algorithm over the original versions of PSO, ECBO, and some other algorithms in the literature.
S. Gholizadeh, C. Gheyratmand,
Volume 14, Issue 2 (2-2024)
Abstract
The main objective of this paper is to optimize the size and layout of planar truss structures simultaneously. To deal with this challenging type of truss optimization problem, the center of mass optimization (CMO) metaheuristic algorithm is utilized, and an extensive parametric study is conducted to find the best setting of internal parameters of the algorithm. The CMO metaheuristic is based on the physical concept of the center of mass in space. The effectiveness of the CMO metaheuristic is demonstrated through the presentation of three benchmark truss layout optimization problems. The numerical results indicate that the CMO is competitive with other metaheuristics and, in some cases, outperforms them.
Z.h.f. Jafar, S. Gholizadeh,
Volume 14, Issue 2 (2-2024)
Abstract
The main objective of this study is to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures at different seismic performance levels using feed-forward back-propagation (FFBP) neural network models. FFBP neural network models with varying numbers of hidden layer neurons (5, 10, 15, 20, and 50) were trained to predict the maximum inter-story drift ratios of 5- and 10-story steel MRF structures. The numerical simulations indicate that FFBP neural network models with ten hidden layer neurons better predict the inter-story drift ratios at seismic performance levels for both 5- and 10-story steel MRFs compared to other neural network models.
