• Structural Engineering and Mechanics
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• v.44 no.4
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• pp.501-519
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• 2012

Truss weight is one of the most important factors in the cost of construction that should be reduced. Different methods have been proposed to optimize the weight of trusses. The artificial bee colony algorithm has been proposed recently. This algorithm selects the lightest section from a list of available profiles that satisfy the existing provisions in the design codes and specifications. An important issue in optimization algorithms is how to impose constraints. In this paper, the artificial bee colony algorithm is used for the discrete optimization of trusses. The fly-back mechanism is chosen to impose constraints. Finally, with some basic examples that have been introduced in similar articles, the performance of this algorithm is tested using the fly-back mechanism. The results indicate that the rate of convergence and the accuracy are optimized in comparison with other methods.

• Geomechanics and Engineering
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• v.24 no.3
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• pp.237-251
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• 2021

The main purpose of this study is to investigate the performance of the proposed hybrid teaching-learning based optimization algorithm on the optimum design of reinforced concrete (RC) cantilever retaining walls. For this purpose, three different design examples are optimized with 100 independent runs considering continuous and discrete variables. In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful meta-heuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms. Moreover, Rao-1 and Rao-2 algorithms are applied to this design problem for the first time. The objective function is defined as minimizing the total material and labor costs including concrete, steel, and formwork per unit length of the cantilever retaining walls subjected to the requirements of the American Concrete Institute (ACI 318-05). Furthermore, the effects of peak ground acceleration value on minimum total cost is investigated using various stem height, surcharge loads, and backfill slope angle. Finally, the most robust results were obtained by HTLBO with 50 populations. Consequently the optimization results show that, depending on the increase in PGA value, the optimum cost of RC cantilever retaining walls increases smoothly with the stem height but increases rapidly with the surcharge loads and backfill slope angle.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.237-250
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• 2003

Since an orthotropic steel deck bridge has large number of design variables and shows complex structural behavior, it would be very difficult and impractical to directly use a Conventional Single Level (CSL) optimization algorithm for its optimum design. Thus, in this paper, an Improved Multi Level Design Synthesis (IMLDS) optimization algorithm is proposed to improve the computational efficiency. In the proposed IMLDS algorithm, a coordination method is introduced to divide the bridge into main girders and orthotropic steel deck with preserving the characteristics of the structural behavior. For an efficient optimization of the bridge, the IMLDS algorithm incorporates the various crucial approximation techniques such as constraints deletion, Automatic Differentiation (AD), stress reanalysis, and etc. In the case of orthotropic steel deck system, optimum design problems are characterized by mixed continuous discrete variables and discontinuous design space. Thus, a modified Genetic Algorithm (GA) is also applied to optimize discrete member design for orthotropic steel deck. From the numerical example, the efficiency and convergency of the IMLDS algorithm proposed in this paper is investigated. It may be positively stated that the IMLDS algorithm will lead to more effective and practical design compared with previous algorithms.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.32-43
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• 1997

Abstract A series of permeability tests was performed on the mixtures with specific mixing rates of sand and bentonite using modified rigid-wall permeameter. Sand-bentonite mixtures were permeated by organics, ethanol and TCE. Permeability of bentonite with several mixing rates had a tendency to decrease up to initial one pore volume and permeability was thereafter converged to a constant value. When sand-bentonite mixtures was permeated by water, permeability was decreased at the beginning but it was thereafter converged to a constant. Among several mixing rates, permeability was greatly decreased at 15% of mixing rate. When sand-bentonite mixtures with 15% mixing rate was permeated by ethanol, permeability was about 10 times larger value than permeability of water. Peameability was shown greater values when permeated by TCE (TrichloroEthylene) followed by ethanol. Suitable mixing rate of sand-bentonite for a liner of waste landfills was detected.

• Structural Engineering and Mechanics
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• v.62 no.5
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• pp.537-550
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• 2017

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.95-100
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• 1990

An optimization method has been developed to find the minimum weight design of steel building structures which consist of the commercially available discrete sections. In this study, an emphasis was particularly placed on the practical applicability of optimization algorithm in engineering practice. The structure Is optimized through element optimization under the element level constraints first and then, if there is any violation of structural level constraints, it is adequately compensated by the constraint error correction vector obtained through the sensitivity analysis. A scaling procedure is introduced for the problems of large violated displacement constraint. The oscillation control in the objective function is also discussed. By dividing the available H-sections into two groups based on their section characteristics, much improved relationships between section variables were obtained and used efficiently in searching the optimum section in the section table.

• Smart Structures and Systems
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• v.21 no.3
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• pp.263-278
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• 2018

Thermal Exchange Optimization (TEO) is a newly developed algorithm which mimics the thermal exchange between a solid object and its surrounding fluid. In this paper, an improved version of the TEO is developed to fix the shortcomings of the standard version. To demonstrate the viability of the new algorithm, the CEC 2016's single objective problems are considered along with the discrete size optimization of benchmark skeletal structures. Problem specific constraints are handled using a fly-back mechanism. The results show the validity of the improved TEO method compared to its standard version and a number of well-known algorithms.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.273-280
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algerian are searching methods for optimum values. The object of this reserch Is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic ome, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm.

• Proceeding of KASS Symposium
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• 2005.05a
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• pp.246-253
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algorithm are searching methods for optimum values. The object of this reserch is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic one, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.4
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• pp.1-9
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• 1986

In this paper research results of developing a method of selecting design variables of an optimization problem from a finite set of pre-specified numbers, which can be utilized for the structural optimization with standardized structural members, is presented. The method first finds a continuous optimum under the assumption that design variables can be varied continuously. Then a pseudo-optimum is determined by selecting numbers from the set that are near to the continuous optimum and do not violate constraints. The pseudo-optimum is further improved to obtain the final discrete optimum from the set which minimizes cost function of the problem. In this research, the method is combined with the gradient projection optimization algorithm. The method is applied to several minimum weight truss optimization problems with constraints on the stresses, displacements, and design variables. As the results, it is found that the method can be efficiently applied to various optimization problems of which design variables must be chosen from a standard.