Optimization of steel structures with one genetic algorithm according to three international building codes

Prendes-Gero, María-Belén; Bello-García, A.; Coz-Díaz, Juan-José del; Suárez-Domínguez, Francisco-José; Nieto, Paulino-José García

doi:10.7764/rdlc.17.1.47

Cited by 14 publications

(11 citation statements)

References 5 publications

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“…Several modes of encoding design information into chromosomes have been proposed [3]. Binary digits (the dominant encoding type) were used in the early works of the use of genetic algorithms for truss optimization [8,11,14]. Later on, other studies [15] have adopted decimal and value encoding to successfully perform discrete optimization of structures.…”

Section: Genetic Algorithms For Structural Optimizationmentioning

confidence: 99%

“…Developed by John Holland [2] in the 1970s, GAs have been successfully applied in several structural design and optimization problems [3,4,5], including but not limited to cost [6,7], weight [8] and topology optimization problems [9]. Most reinforced concrete structural optimization problems are treated as continuous in their function form, and the design variables can theoretically assume any positive real number.…”

Section: Introductionmentioning

confidence: 99%

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Cost optimization of reinforced concrete frames using genetic algorithms

Habte

Yilma

2020

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Cost optimization of reinforced concrete building frames using genetic algorithms is presented. Unlike previous works that used simplified discrete or continuous optimization models, this work considers constructability issues as well as the effects of shear and torsional actions in the design optimization of reinforced concrete frames. An integrated software system has been developed to implement the proposed optimization procedure using genetic algorithms. Examples have been incorporated in order to compare the results from the proposed study with that of a previous work which follows a different heuristic and with the traditional “design–check–revise” method. The structural design procedures recommended in the Eurocode-2 have been strictly followed in this work. Special emphasis has been given to structural analysis methods and studying computational efficiency of the developed framework. To improve the performance and computational complexity of the algorithm, the effect of genetic parameters such as mutation and crossover on the optimization process has been thoroughly studied. The method developed in this work proves to have a lot of advantages over the traditional “design–check–revise” paradigm and other heuristic methods.

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Section: Genetic Algorithms For Structural Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cost optimization of reinforced concrete frames using genetic algorithms

Habte

Yilma

2020

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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“…Kociecki and Adeli [13] and Prendes-Gero et al [14] implemented GA for optimization of different steel structural systems in 2015 and 2018, respectively, and observed gains of around 10% when compared to non-optimized designs. Both studies also highlighted the ability to work with discrete variables when using GA as an important characteristic for obtaining good results.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the structural system with composite beam and composite slab using Genetic Algorithm

Breda

Pietralonga

Alves

2020

Rev. IBRACON Estrut. Mater.

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The objective of this research is to create a software to optimize choice variables adopted by an engineer in a structural system with steel-concrete composite beams and steel decks, such as: beam shape, composite slab sheeting, number and spacing of beams, slab thickness and interaction ratio between beam and slab. To accomplish this, a program that uses the Genetic Algorithm optimization tool provided by Matlab R2015a was developed. To meet safety requirements, restrictions on the Ultimate Limit State were implemented in the code, following the normative requirements of ABNT NBR 8800: 2008. Case studies of a problem found in the literature and another of a real structure, are presented to serve as references for software evaluation. Results indicate that the use of optimization processes is fundamental to design increasingly cost-effective structures.

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“…Prendes-Gero et al evaluated the capability of GA algorithms with three different building codes (Spanish, European, and American). Finally, it has found that the results of the optimization show the heaviest structures with the American code and the lightest structures with the European code [21]. In another study, genetic algorithm was implemented to optimize a plane steel truss structure under point loadings.…”

Section: Introductionmentioning

confidence: 99%

A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm

Razavi

Siahpolo

Adeli

2020

Journal of Engineering

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The most important feature of the behavior factor is that it allows the structural designer to be able to evaluate the structural seismic demand, using an elastic analysis, based on force-based principles quickly. In most seismic codes, this coefficient is merely dependent on the type of lateral resistance system and is introduced with a fixed number. However, there is a relationship between the behavior factor, ductility (performance level), structural geometric properties, and type of earthquake (near and far). In this paper, a new and accurate correlation is attempted to predict the behavior factor (q) of EBF steel frames, under near-fault earthquakes, using the genetic algorithm (GA). For this purpose, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3−, 6−, 9−, 12−, 15, and 20− story steel EBF frames were considered with 3 different types of link beam, 3 different types of column stiffness, and 3 different types of brace slenderness. Using nonlinear time history under 20 near-fault earthquake, all models were analyzed to reach 4 different performance levels. 6769 data were used as GA training data. Moreover, to validate the correlation, 2257 data were used as test data for calculating mean squared error (MSE) and correlation coefficient (R) between the predicted values of (q) and the real values. In addition, the MSE and R were calculated for correlation in the train and test data. Also, the comparison of the response of maximum inelastic displacement of 5 stories EBF from the proposed correlation and the mean inelastic time-history analysis confirms the accuracy of the estimate relationship.

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Optimization of steel structures with one genetic algorithm according to three international building codes

Cited by 14 publications

References 5 publications

Cost optimization of reinforced concrete frames using genetic algorithms

Cost optimization of reinforced concrete frames using genetic algorithms

Optimization of the structural system with composite beam and composite slab using Genetic Algorithm

A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm

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