New Approach to Optimization of Reinforced Concrete Beams

Lepš, M.; Šejnoha, Michal

doi:10.4203/ccp.69.6.3

Cited by 9 publications

(10 citation statements)

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“…The principles of genetic algorithms were firstly proposed by J. H. Holland [9]; the books of D. E. Goldberg [7] and Z. Michalewicz [14] are the most popular publications that deal with this topic. Genetic algorithms have been successfully used to solve optimization problems in combinatorics (see [8]) as well as in different engineering tasks, see for example [12,13,15].…”

Section: Introductionmentioning

confidence: 99%

Improvements of real coded genetic algorithms based on differential operators preventing premature convergence

Hrstka

Kučerová

2004

Advances in Engineering Software

149

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This paper presents several types of evolutionary algorithms (EAs) used for global optimization on real domains. The interest has been focused on multimodal problems, where the difficulties of a premature convergence usually occurs. First the standard genetic algorithm (SGA) using binary encoding of real values and its unsatisfactory behavior with multimodal problems is briefly reviewed together with some improvements of fighting premature convergence. Two types of real encoded methods based on differential operators are examined in detail: the differential evolution (DE), a very modern and effective method firstly published by R. Storn and K. Price [17], and the simplified real-coded differential genetic algorithm SADE proposed by the authors [11]. In addition, an improvement of the SADE method, called CERAF technology, enabling the population of solutions to escape from local extremes, is examined. All methods are tested on an identical set of objective functions and a systematic comparison based on a reliable methodology [1] is presented. It is confirmed that real coded methods generally exhibit better behavior on real domains than the binary algorithms, even when extended by several improvements. Furthermore, the positive influence of the differential operators due to their possibility of self-adaptation is demonstrated. From the reliability point of view, it seems that the real encoded differential algorithm, improved by the technology described in this paper, is a universal and reliable method capable of solving all proposed test problems.

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Section: Introductionmentioning

confidence: 99%

Improvements of real coded genetic algorithms based on differential operators preventing premature convergence

Hrstka

Kučerová

2004

Advances in Engineering Software

149

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“…A metaheuristic algorithm, music inspired harmony search method developed by Geem et al [2] is employed in the optimization methodology. Several metaheuristic methods have been employed in the design of RC members [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

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confidence: 99%

The Optimization of Slender Reinforced Concrete Columns

Bekdaş

Niğdeli

2014

Proc Appl Math and Mech

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Slenderness is an important issue in design of reinforced concrete (RC) columns. Especially for long columns, second order effects may be not so small to neglect, but the calculation of second order effects may take too much time. For that reason, ACI 318 design code includes a simple approach in order to increase the flexural moment of columns according to their slenderness. Thus, second order effects are considered. In optimization, the effect of slenderness can be considered by using the factored design flexural moments. In this paper, harmony search (HS) algorithm is employed to find the optimum design variables of slender RC columns. These design variables are web width, height, diameter and number of reinforcements. The optimization objective is total cost of materials including concrete and steel. The developed method is effective to find the optimal design for axial force, flexural moment and shear force values. As numerical examples, optimum design of columns with different lengths, but with the same loadings and material properties were investigated. Thus, the effect of slenderness was seen on the optimum costs. By the increase of column length, increase of total material cost is more than a linear increase. This situation shows us the effect of slenderness on optimum RC columns This paper proposes an optimization methodology for reinforced concrete (RC) columns considering slenderness. The proposed methods is used to find design variables of the structural members. In classical design of the engineer, cross-sectional dimensions are assumed and the required reinforcement area is calculated in order to choose design reinforcements. Since steel and concrete have different costs, the most economical design can be only provided by using optimization methods. Design requirements and methods given in ACI318 [1] are considered in optimization process. A metaheuristic algorithm, music inspired harmony search method developed by Geem et al. [2] is employed in the optimization methodology. Several metaheuristic methods have been employed in the design of RC members [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].

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“…The basic parameters subjected to optimization were the beam width b, which was assumed to take discrete values between 0. For more details see [9,10]. The computation was terminated if an individual with price smaller than 573.5 CZK was found or the number of objective function calls exceeded 1, 000, 000.…”

Section: Results For the Reinforced Concrete Beam Layout Problemmentioning

confidence: 99%

“…The constraints in this procedure deal with allowable strength and serviceability limits given by a chosen standard -in our case EUROCODE 2 (EC2) [2]. An interested reader can find implementation details for example in [9].…”

Section: Reinforced Concrete Beam Layoutmentioning

confidence: 99%

“…This paper presents several optimization methods that were developed and tested for different types of optimization tasks. The integer augmented simulated annealing (IASA), derived from a binary version of the algorithm [9], was developed to optimize a reinforced concrete beam layout from the economic point of view. For solving the problem of a periodic unit cell layout [16], the real-coded simulated annealing was applied.…”

Section: Introductionmentioning

confidence: 99%

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A Competitive Comparison of Different Types of Evolutionary Algorithms

Hrstka¹,

Kučerová²,

Lepš³

et al.

Proceedings of the Sixth International Conference on the Application of Artificial Intelligence to Civil and Structural Enginee

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This paper presents comparison of several stochastic optimization algorithms developed by authors in their previous works for the solution of some problems arising in Civil Engineering. The introduced optimization methods are: the integer augmented simulated annealing (IASA), the real-coded augmented simulated annealing (RASA) [10], the differential evolution (DE) in its original fashion developed by R. Storn and K. Price [15] and simplified real-coded differential genetic algorithm (SADE) [6]. Each of these methods was developed for some specific optimization problem; namely the Chebychev trial polynomial problem, the so called type 0 function and two engineering problems -the reinforced concrete beam layout and the periodic unit cell problem respectively. Detailed and extensive numerical tests were performed to examine the stability and efficiency of proposed algorithms. The results of our experiments suggest that the performance and robustness of RASA, IASA and SADE methods are comparable, while the DE algorithm performs slightly worse. This fact together with a small number of internal parameters promotes the SADE method as the most robust for practical use.

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New Approach to Optimization of Reinforced Concrete Beams

Cited by 9 publications

References 0 publications

Improvements of real coded genetic algorithms based on differential operators preventing premature convergence

Improvements of real coded genetic algorithms based on differential operators preventing premature convergence

The Optimization of Slender Reinforced Concrete Columns

A Competitive Comparison of Different Types of Evolutionary Algorithms

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