• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.13 no.22
• /
• pp.25-34
• /
• 1990

This paper is to maximize the reliability subject to certain constraints on amounts of resources available for control of the parameters. The lagrange multiplier method is used to optimize t-th lognormal stress-lognormal strength problem. This optimization problem can be reduced to a search problem in one variable. A numerical example is presented to illustrate the optimization problem.

• Computers and Concrete
• /
• v.17 no.5
• /
• pp.629-638
• /
• 2016

Optimization of the concrete mixture design is a process of search for a mixture for which the sum of the cost of the ingredients is the lowest, yet satisfying the required performance of concrete. In this study, a statistical model was carried out to model a cost effective optimal mix proportioning of high strength self-compacting concrete (HSSCC) using the Response Surface Methodology (RSM). The effect of five key mixture parameters such as water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content on the properties and performance of HSSCC like compressive strength, passing ability, segregation resistance and manufacturing cost were investigated. To demonstrate the responses of model in quadratic manner Central Composite Design (CCD) was chosen. The statistical model showed the adjusted correlation coefficient R2adj values were 92.55%, 93.49%, 92.33%, and 100% for each performance which establish the adequacy of the model. The optimum combination was determined to be $439.4kg/m^3$ cement content, 35.5% W/B ratio, 50.0% fine aggregate, $49.85kg/m^3$ fly ash, and $7.76kg/m^3$ superplasticizer within the interest region using desirability function. Finally, it is concluded that multiobjective optimization method based on desirability function of the proposed response model offers an efficient approach regarding the HSSCC mixture optimization.

• Smart Structures and Systems
• /
• v.34 no.3
• /
• pp.181-201
• /
• 2024

This study presents a comparative analysis of three nature-inspired algorithms-Black Hole Algorithm (BHA), Earthworm Optimization Algorithm (EWA), and Future Search Algorithm (FSA)-for predicting the compressive strength of masonry structures. Each algorithm was integrated with a Multilayer Perceptron (MLP) model, using a structural dimension, rebound number, ultrasonic pulse velocity, and failure load dataset. The dataset was divided into training (70%) and testing (30%) subsets to evaluate model performance. Root Mean Square Error (RMSE) and the coefficient of determination (R²) were employed as statistical indices to measure accuracy. The BHA-MLP model achieved the best performance, with an RMSE of 0.04731 and an R² of 0.9995 for the training dataset and an RMSE of 0.06537 and an R² of 0.99877 for the testing dataset, securing the highest overall score. FSA-MLP ranked second, demonstrating strong predictive performance, followed by EWA-MLP, which performed with lower accuracy but still showed valuable results. The study highlights the potential of using these nature-inspired optimization algorithms to enhance the predictive accuracy of compressive strength in masonry structures, offering insights for engineering and policymaking to improve structural safety and performance.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.21 no.4
• /
• pp.625-632
• /
• 2012

Optimization of process parameters in polymer extrusion is an important task to reduce manufacturing cost. To determine the optimum values of the process parameters, it is essential to find their influence on the strength of polymer breathable thin film. The significance of six important process parameters namely, extruder cylinder temperature, extruder speed, extruder dies temperature, cooling roll temperature, stretching ratio, stretching roll temperature on breathable film strength of polymer extrusion was determined. Moreover, this paper presents the application of Taguchi method and analysis of variance (ANOVA) for maximization of the breathable film strength influenced by extrusion parameters. The optimum parameter combination of extrusion process was obtained by using the analysis of signal-to-noise ratio. The conclusion revealed that extruder speed and stretching ratio were the most influential factor on the film strength, respectively. The best results of film strength were obtained at higher extruder speed and stretching ratio.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.17 no.1
• /
• pp.190-196
• /
• 2009

This study proposes a structural design method for the upper control arm installed at the rear side of a SUV. The weight of control arm can be reduced by applying the design and material technologies. In this research, the former includes optimization technology, and the latter the technologies for selecting aluminum as a steel-substitute material. Strength assessment is the most important design criterion in the structural design of a control arm. At the proto design stage of a new control arm, FE (finite element) analysis is often utilized to predict its strength. This study considers the static strength in the optimization process. The inertia relief method for FE analysis is utilized to simulate the static loading conditions. According to the classification of structural optimization, the structural design of a control arm is included in the category of shape optimization. In this study, the kriging interpolation method is adopted to obtain the minimum weight satisfying the strength constraint. Optimum designs are obtained by ANSYS WORKBENCH and the in-house program, EXCEL-kriging program. The optimum results determined from the in-house program are compared with those of ANSYS WORKBENCH.

• Smart Structures and Systems
• /
• v.33 no.1
• /
• pp.65-91
• /
• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.12
• /
• pp.102-109
• /
• 2009

This study proposes the structural optimization of a manifold valve. FE analysis is performed to evaluate the strength of a manifold valve. In addition, the structural optimization technique is applied to reduce its weight. In this study, the optimization method using the kriging interpolation method is adopted to obtain the minimum weight satisfying the strength constraint. The maximum stress and the weight are replaced by the metamodels. In this process, tile sample points are generated by latin-hypercube design. Optimum designs are obtained by ANSYS Workbench and the in-house program.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.5 no.4
• /
• pp.48-69
• /
• 1997

The use of advanced composite materials in many engineering structures has steadily increased during the last decade. Advanced composite materials allow the design engineer to tailor the directional stiffness and the strength of materials as required for the structures. Design variables to the design engineer include multiple material systems. ply orientation, ply thickness, stacking sequence and boundary conditions, in addition to overall structural design parameters. Since the vibration and impact strength of composite cylindrical shell is an important consideration for composite structures design, the reliable prediction method and design methodology should be required. In this study, the optimum design of composite cylindrical shell for maximum natural frequency, buckling strength and impact strength are developed by analytic and numerical method. The effect of parameters such as the various composite material orthotropic properties (CFRP, GFRP, KFRP, Al-CFRP hybrid), the stacking sequences, the shell thickness, and the boundary conditions on structural characteristics are studied extensively.

• Computers and Concrete
• /
• v.22 no.4
• /
• pp.419-437
• /
• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• Structural Engineering and Mechanics
• /
• v.61 no.2
• /
• pp.283-293
• /
• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.