2020-08-07T14:46:07+04:30
http://ijoce.iust.ac.ir/browse.php?mag_id=33&slc_lang=en&sid=1
33-360
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
OPTIMIZATION OF STEEL MOMENT FRAME BY A PROPOSED EVOLUTIONARY ALGORITHM
A.
Mahallati Rayeni
H.
Ghohani Arab
M. R.
Ghasemi
This paper presents an improved multi-objective evolutionary algorithm (IMOEA) for the design of planar steel frames. By considering constraints as a new objective function, single objective optimization problems turned to multi objective optimization problems. To increase efficiency of IMOEA different Crossover and Mutation are employed. Also to avoid local optima dynamic interference of mutation and crossover are considered. Feasible particles called elites which are very helpful for better mutation and crossover considered as a tool to increase efficiency of proposed algorithm. The proposed evolutionary algorithm (IMOEA) is utilized to solve three well-known classical weight minimization problems of steel moment frames. In order to verify the suitability of the present method, the results of optimum design for planar steel frames are obtained by present study compared to other researches. Results indicate that, as far as the convergence, speed of the optimization process and quality of optimum design are concerned behavior, IMOEA is significantly superior to other meta-heuristic optimization algorithms with an acceptable global answer.
frame design
metaheuristic optimization algorithms
evolutionary algorithm
constraint handling
Structural optimization.
2018
10
01
511
524
http://ijoce.iust.ac.ir/article-1-360-en.pdf
33-361
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
PERFORMANCE-BASED SEISMIC DESIGN OPTIMIZATION FOR MULI-COLUMN RC BRIDGE PIERS, CONSIDERING QUASI-ISOLATION
H.
Fazli
A.
Pakbaz
In this paper an optimization framework is presented for automated performance-based seismic design of bridges consisting of multi-column RC pier substructures. The beneficial effects of fusing components on seismic performance of the quasi-isolated system is duly addressed in analysis and design. The proposed method is based on a two-step structural analysis consisting of a linear modal dynamic demand analysis and a nonlinear static capacity evaluation of the entire bridge structure. Results indicate that the proposed optimization method is capable of producing cost-effective design solutions combining the fusing behavior of bearings and yielding mechanism of piers. The optimal designs obtained from models addressing the performance of fusing components are far more efficient than those that do not take care of quasi-isolation behavior.
optimization
performance-based seismic design
bridge
quasi-isolation
MCBO.
2018
10
01
525
545
http://ijoce.iust.ac.ir/article-1-361-en.pdf
33-362
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
EVELOPMENT OF ANFIS-PSO, SVR-PSO, AND ANN-PSO HYBRID INTELLIGENT MODELS FOR PREDICTING THE COMPRESSIVE STRENGTH OF CONCRETE
M.
Torkan
M.
Naderi Dehkordi
Concrete is the second most consumed material after water and the most widely used construction material in the world. The compressive strength of concrete is one of its most important mechanical properties, which highly depends on its mix design. The present study uses the intelligent methods with instance-based learning ability to predict the compressive strength of concrete. To achieve this objective, first, a set of data pertaining to concrete mix designs containing fly ash was collected. Then, mix design parameters were used as the inputs of the artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) developed for predicting the compressive strength. In all these models, prediction accuracy largely depends on the parameters of the learning model. Hence, the particle swarm optimization (PSO) algorithm, as a powerful population-based algorithm for solving continuous and discrete optimization problems, was used to determine the optimal values of algorithm parameters. The hybrid models were trained and tested with 426 experimental data and their results were compared by statistical criteria. Comparing the results of the developed models with the real values showed that the ANFIS-PSO hybrid model has the best performance and accuracy among the assessed methods.
concrete
compressive strength
artificial neural networks (ANN)
support vector machine (SVM)
adaptive neural-fuzzy inference system (ANFIS).
2018
10
01
547
563
http://ijoce.iust.ac.ir/article-1-362-en.pdf
33-363
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
STRUCTURAL SYSTEM RELIABILITY-BASED OPTIMIZATION OF TRUSS STRUCTURES USING GENETIC ALGORITHM
K.
Biabani Hamedani
V. R.
Kalatjari
Structural reliability theory allows structural engineers to take the random nature of structural parameters into account in the analysis and design of structures. The aim of this research is to develop a logical framework for system reliability analysis of truss structures and simultaneous size and geometry optimization of truss structures subjected to structural system reliability constraint. The framework is in the form of a computer program called RBO-S>S. The objective of the optimization is to minimize the total weight of the truss structures against the aforementioned constraint. System reliability analysis of truss structures is performed through branch-and-bound method. Also, optimization is carried out by genetic algorithm. The research results show that system reliability analysis of truss structures can be performed with sufficient accurately using the RBO-S>S program. In addition, it can be used for optimal design of truss structures. Solutions are suggested to reduce the time required for reliability analysis of truss structures and to increase the precision of their reliability analysis.
branch-and-bound method
system reliability analysis
size and geometry optimization
truss structures
genetic algorithm
2018
10
01
565
586
http://ijoce.iust.ac.ir/article-1-363-en.pdf
33-364
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
AN OPTIMUM APPROACH TOWARDS SEISMIC FRAGILITY FUNCTION OF STRUCTURES THROUGH METAHEURISTIC HARMONY SEARCH ALGORITHM
S.
Dehghani Fordoei
S.A.
Razavian Amrei
M.
Eghbali
M. Sh.
Nasrollah Beigi
Vulnerability assessment of structures encounter many uncertainties like seismic excitations intensity and response of structures. The most common approach adopted to deal with these uncertainties is vulnerability assessment through fragility functions. Fragility functions exhibit the probability of exceeding a state namely performance-level as a function of seismic intensity. A common approach is finding some response points of the fragility function and then fitting a typical probability distribution like lognormal through curve fitting estimation techniques. Maximum-likelihood approach is a fitting method to find the probability distribution parameters. Performing this approach for distributions like lognormal which is defined by just two parameters are straight forward while for more complicated distribution which are based on additional characterizing parameters is not feasible, since this approach is based on minimizing an error function through classic mathematical approaches like calculating partial derivations. An applicable modification is to add an efficient optimization approach to determine maximum-likelihood function. In this article, an optimization algorithm is proposed with maximum-likelihood-estimation and the results indicate the efficiency and feasibility of future developments in finding the most appropriate fragility function.
optimization
harmony search algorithm
vulnerability assessment
fragility function
maximum likelihood estimation.
2018
10
01
587
600
http://ijoce.iust.ac.ir/article-1-364-en.pdf
33-367
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
A MATHEMATICAL MODEL FOR SELECTING THE PROJECT RISK RESPONSES IN CONSTRUCTION PROJECTS
R.
Soofifard1
M.
Khakzar Bafruei
M.
Gharib
Risks are natural and inherent characteristics of major projects. Risks are usually considered independently in analysis of risk responses. However, most risks are dependent on each other and independent risks are rare in the real world. This paper proposes a model for proper risk response selection from the responses portfolio with the purpose of optimization of defined criteria for projects. This research has taken into account the relationships between risk responses; especially the relationships between risks, which have been rarely considered in previous works. It must be pointed out that not considering or superficial evaluation of the interactions between risks and risk responses reduces the expected desirability and increases project execution costs. This model is capable of optimization of different criteria in the objective function based on the proposed projects. Multi-objective Harmony Search (MOHS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve this model and the numerical results obtained are analyzed. Finally, it was observed that ranges of objective functions in MOHS are better than those in NSGA-II.
risk response
project risk management
risk interactions
risk interdependence
NSGA-II algorithm
MOHS algorithm.
2018
10
01
601
624
http://ijoce.iust.ac.ir/article-1-367-en.pdf
33-366
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
SIZE AND GEOMETRY OPTIMIZATION OF TRUSS STRUCTURES USING THE COMBINATION OF DNA COMPUTING ALGORITHM AND GENERALIZED CONVEX APPROXIMATION METHOD
P.
Darvishi
S.
Shojaee
In recent years, the optimization of truss structures has been considered due to their several applications and their simple structure and rapid analysis. DNA computing algorithm is a non-gradient-based method derived from numerical modeling of DNA-based computing performance by new computers with DNA memory known as molecular computers. DNA computing algorithm works based on collective intelligence. It works with doing random search in the search space and creating the initial random population by modeling DNA-based computing operators and applies the operators derived from genetic algorithm to achieve the optimum solution of the objective function. Generalized Convex Approximation (GCA) method is a gradient-based method that with approximation of the main function and starting from a point, finds the optimum solution using information about functions and their gradient. In this research, in order to minimize the weight of truss, the cross-section areas of the elements as discrete variables are optimized by DNA computing algorithm, and the coordinates of truss nodes as continuous variables are optimized by Generalized Convex Approximation (GCA) method. Therefore, to simultaneously optimize the size and geometry of truss structures, these two methods are used in combination. The results of numerical examples show the proper functioning of this process.
Optimization
Truss
DNA computing algorithm
Generalized Convex Approximation (GCA) method.
2018
10
01
625
656
http://ijoce.iust.ac.ir/article-1-366-en.pdf
33-368
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
OPTIMIZATION OF VERTICAL ALIGNMENT OF HIGHWAYS IN TERMS OF EARTHWORK COST USING COLLIDING BODIES OPTIMIZATION ALGORITHM
A. R.
Ghanizadeh
N.
Heidarabadizadeh
One of the most important factors that affects construction costs of highways is the earthwork cost. On the other hand, the earthwork cost strongly depends on the design of vertical alignment or project line. In this study, at first, the problem of vertical alignment optimization was formulated. To this end, station, elevation and vertical curve length in case of each point of vertical intersection (PVI) were considered as decision variables. The objective function was considered as earthwork cost and constraints were assumed as the maximum and minimum grade of tangents, minimum elevation of compulsory points, and the minimum length of vertical curves. For solving this optimization problem, the Colliding Bodies Optimization (CBO) algorithm was employed and results were compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In order to evaluate the effectiveness of formulation and CBO algorithm, three different highways were designed with respect to three different terrains including level, rolling and mountainous. After designing the preliminary vertical alignment for each highway, the optimal vertical alignments were determined by different optimization algorithms. The results of this research show that the CBO algorithm is superior to GA and PSO. Percentage of optimality (saving in earthworks cost) by CBO algorithm for level, rolling and mountainous terrains was determined as 44.14, 21.42 and 22.54%, respectively.
optimization
vertical alignment
earthworks cost
colliding bodies optimization (CBO).
2018
10
01
657
674
http://ijoce.iust.ac.ir/article-1-368-en.pdf
33-369
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
AN OPTIMIZATION PROCEDURE FOR CONCRETE BEAM-COLUMN JOINTS STRENGTHENED WITH FRP
K.
Khashi
H.
Dehghani
A. A.
Jahanara
This paper illustrates an optimization procedure of concrete beam-column joints subjected to shear that are strengthened with fiber reinforced polymer (FRP). For this aim, five different values have been considered for length, width and thickness of the FRP sheets which created 125 different models to strengthen of concrete beam-column joints. However, by using response surface methodology (RSM) in design expert software the number of these models is reduced to 20. Then, each of 20 models is simulated in ABAQUS finite element software and shear capacity is also determined. The relationship between different dimensions of the FRP sheets and shear capacity are specified by using RSM. Furthermore the optimum dimensions are determined by particle swarm optimization (PSO) algorithm.
fiber reinforced polymer
beam-column joints
response surface methodology
optimization.
2018
10
01
675
687
http://ijoce.iust.ac.ir/article-1-369-en.pdf
33-370
2020-08-07
10.1002
Iran University of Science & Technology
2228-7558
2018
8
4
GENERATION OF OPTIMIZED SPECTRUM COMPATIBLE NEAR-FIELD PULSE-LIKE GROUND MOTIONS USING ARTIFICIAL INTELLIGENCE
A.
Gholizad
S.
Eftekhar Ardabili
The existence of recorded accelerograms to perform dynamic inelastic time history analysis is of the utmost importance especially in near-fault regions where directivity pulses impose extreme demands on structures and cause widespread damages. But due to the scarcity of recorded acceleration time histories, it is common to generate proper artificial ground motions. In this paper an alternative approach is proposed to generate near-fault pulse-like ground motions. A smoothening approach is taken to extract directivity pulses from an ensemble of near-fault pulse-like ground motions. First, it is proposed to simulate nonpulse-type ground motion using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Wavelet Packet Transform (WPT). Next, the pulse-like ground motion is produced by superimposing directivity pulse on the previously generated nonpulse-type motion. The main objective of this study is to generate near-field spectrum compatible records. Particle Swarm Optimization (PSO) is employed to optimize both the parameters of pulse model and cluster radius in subtractive clustering and Principle Component Analysis (PCA) is used to reduce the dimension of ANFIS input vectors. Artificial records are generated for the first, second and third level of wavelet packet decomposition. Finally, a number of interpretive examples are presented to show how the method works. The results show that the response spectra of generated records are decently compatible with the target near-field spectrum, which is the main objective of the study.
near-field
directivity
synthetic ground motion
pulse-like
wavelet analysis
ANFIS.
2018
10
01
689
708
http://ijoce.iust.ac.ir/article-1-370-en.pdf