• Computers and Concrete
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• v.6 no.3
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• pp.253-268
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• 2009

This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.735-741
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• 2011

In the early phase of vehicle development, CAE is conducted as tool for vehicle performance assessment. To maintain acceptable road noise performance, solution for reduced vehicle sensitivity is required. Chassis interface dynamic stiffness characteristics are key component to isolating vibration and noise of road from the vehicle interior. This research provide how to set up the optimized dynamic characteristics under noise effect through DFSS study. CAE-based DOE is performed to build prediction math model, CMS process involves DOE to achieve very fast run times while giving results very comparable. Minimized $95^{th}$ percentile of performance distribution is applied to minimize vehicle sensitivity and road noise levels variation during the optimization process. Finally, the results of optimization were reviewed for performance and robustness.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.2
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• pp.75-80
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• 2016

There are three types of dehumidification systems : refrigeration dehumidification method, desiccant dehumidification method and hybrid dehumidification method. The first method involves removing moisture by condensation below the dew point, the second method involves absorption by a desiccant material and the last is an integration method. However, the refrigeration dehumidification system consumes too much power and controlling the humidity ratio is difficult. The desiccant dehumidification system uses less power but it has problems of environmental pollution. The hybrid dehumidification system has the disadvantage of a high initial cost. On the other hand, the energy consumption of the membrane based dehumidification system is lower than for the refrigeration dehumidification system. Also, it is an environmentally friendly technology. In this study, the performance parameters are evaluated for the dehumidification system using a hollow fiber membrane. Available area, duct side dry-bulb temperature, sweep gas flux (flow rate) and LMPD (Log Mean Pressure Difference) were used as the performance parameters.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.7
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• pp.674-681
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• 2011

In the early phase of vehicle development, CAE is conducted as tool for vehicle performance assessment. To maintain acceptable road noise performance, solution for reduced vehicle sensitivity is required. Chassis interface dynamic stiffness characteristics are key component to isolating vibration and noise of road from the vehicle interior. This research provide how to set up the optimized dynamic characteristics under noise effect through DFSS study. CAE-based DOE is performed to build prediction math model, CMS process involves DOE to achieve very fast run times while giving results very comparable. Minimized 95th percentile of performance distribution is applied to minimize vehicle sensitivity and road noise levels variation during the optimization process. Finally, the results of optimization were reviewed for performance and robustness.

• Computers and Concrete
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• v.32 no.2
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• pp.185-206
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• 2023

This article aimed to explore the optimization of mineral admixtures and retarding admixture for high-performance concrete. In essence, fresh concrete can be regarded as a mixture in which both coarse and fine aggregates are suspended in a cement-based matrix paste. Based on this view, the test procedure was divided into three progressive stages of binder paste, mortar, and concrete to explore their rheological behavior and mechanical properties respectively. At each stage, there were four experimental control factors, and each factor had three levels. In order to reduce the workload of the experiment, the Taguchi method with an L9(3⁴) orthogonal array and four controllable three-level factors was adopted. The test results show that the use of the Taguchi method effectively optimized the composition of high-performance concrete. The slump of the prepared concrete was above 18 cm, and the slump flow was above 50 cm, indicating that it had good workability. On the other hand, the 28-day compressive strength of the hardened concretes was between 31.3-59.8 MPa. Furthermore, the analysis of variance (ANOVA) results showed that the most significant factor affecting the initial setting time of the fresh concretes was the retarder dosage, and its contribution percentage was 62.66%. On the other hand, the ANOVA results show that the most significant factor affecting the 28-day compressive strength of the hardened concretes was the water to binder ratio, and its contribution percentage was 79.05%.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.430-435
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• 2009

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.30 no.9
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• pp.105-111
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• 2012

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.240-249
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• 2000

An improved global optimization algorithm is developed by extending the domain elimination method. The concept of triangular patch consists of two or more trajectories of local minimizations is introduced to widen the attraction region of the domain elimination method. Using the an-]c between each of three vertices of the patch and a design point, we measure the proximity, between the design point and the patch. With the Gram-Schimidt orthonormalization, this method can be extended to general n-dimensional problems. We code the original domain elimination algorithm and a patch-based algorithm. Then we compare the performance of two algorithms. Through the well-known example problems. the algorithm using patch is shown to be superior to the original domain elimination algorithm in view of computational efficiency.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.565-571
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• 2001

As a method of structural optimization, a practical algorithm based on the Taguchi method is developed. The Taguchi method is applied iteratively updating the level values of design variables. The design region is translated or reduced during optimization and by appropriate choice of reduction factor and initial level intervals, a near-optimum solution can be found very efficiently. To treat inequality constraints, a variable penalty method is utilized. A software system named 'DS/Taguchi' is developed by integrating the proposed algorithm and commercial finite element analysis codes on the parametric CAD platform. Two examples are taken to examine the performance of the proposed algorithm and the developed software system.

• ETRI Journal
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• v.33 no.1
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• pp.39-49
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• 2011

A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary-based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.