• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.253-258
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• 2000

Recently, the area of design optimization, especially structural optimization, has been and to be a continuous active area of research. And the design optimizations of port facilities have been achieved by many other civil engineers. But the design optimization of port facilities were limited to the design optimization of the breasting dolphin. This paper invested the design optimization of mooring pier and the foundations of mooring pier was suggested considering the convenience of repair and reinforcement work. The mooring pier devised with prestressed precast concrete panel and rigid frame welded wide flange beam to steel pipe pile. To accomplish the design optimization of mooring pier, the Augmented Lagrangian Multiplier Method(ALM) of ADS(Garret N. Vanderplaats) optimization routine, BFGS method as optimizer and Golden Section Method as one dimensional search were utilized. As a result, thirty percent of material cost for construction was reduced by design optimization. The tensile stress of concrete panel and bottom flage was critical constraints under service load. So, using high strength concrete and steel will be economical. And lots of initial values must be invested to accomplish the design optimization in design procedures.

• Transactions of Materials Processing
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• v.18 no.5
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• pp.392-400
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• 2009

Clinching is a method of joining sheet metals together. This process can be substituted for the resistance spot welding on the joining of aluminum alloys. However, the joining strength of the clinching is lower than that of welding and riveting. The objective of this paper is to evaluate the effect of shape parameters of tools on the joining strength of the clinching and to optimize clinching tools. Twelve parameters have been selected as shape parameters on the clinching tools such as punch and die. The design of experiments (DOE) method is employed to investigate the effect of the shape parameters of tools on the joining strength of the clinching. The neck thickness and undercut of the clinched sheet metal after the clinching, and the separation load at detaching are estimated from the result of FEA using DEFORM. Optimal combination of shape parameters to maximize the joining strength of clinching is determined on the basis of the result of DOE and FEA. In order to validate the result of DOE and FEA, the experiment of clinching is performed for the optimal combination of shape parameters. It is shown from the result of the experiment that optimization of shape parameters improves the joining strength of clinching.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• Computational Structural Engineering
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• v.17 no.3
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• pp.57-64
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• 2004

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.799-807
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• 2020

This paper deals with identification of probabilistic design using reliability based robust optimization in structure design of automatic salt collector. The thickness sizing variables of main structure member in the automatic salt collector were considered the random design variables including the uncertainty of corrosion that would be an inevitable hazardousness in the saltern work environment. The probabilistic constraint functions were selected from the strength performances of the automatic salt collector. The reliability based robust optimum design problem was formulated such that the random design variables were determined by minimizing the weight of the automatic salt collector subject to the probabilistic strength performance constraints evaluating from reliability analysis. Mean value reliability method and adaptive importance sampling method were applied to the reliability evaluation in the reliability based robust optimization. The three sigma level quality was considered robustness in side constraints. The probabilistic optimum design results according to the reliability analysis methods were compared to deterministic optimum design results. The reliability based robust optimization using the mean value reliability method showed the most rational results for the probabilistic optimum structure design of the automatic salt collector.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.20-33
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• 2010

The paper deals with strength design of a riser support installed on floating production storage and offloading (FPSO) vessel under various loading conditions - operation, extreme, damaged, one line failure case (OLFC) and installation. The design problem is formulated such that thickness sizing variables are determined by minimizing the weight of a riser support structure subject to stresses constraints. The initial design model is generated based on an actual FPSO riser support specification. The finite element analysis (FEA) is conducted using MSC/NASTRAN, and optimal solutions are obtained via moving least squares method (MLSM) in the context of response surface based approximate optimization. For the meta-modeling of inequality constraint functions of stresses, a constraint-feasible moving least squares method (CF-MLSM) is used in the present study. The method of CF-MLSM, compared to a conventional MLSM, has been shown to ensure the constraint feasibility in a case where the approximate optimization process is employed. The optimization results present improved design performances under various riser operating conditions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.401-409
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• 2014

This research develops tools and strategies for optimizing RC column sections applied in tall buildings. Optimization parameters are concrete strength and section shape, the objective function for which is subject to several predefined constraints drawn from the original structural design. For this purpose, we developed new components for StrAuto, a parametric modeling and optimization tool for building structure. The components receive from external analysis solvers member strengths calculated from the original design model, and output optimized column sections satisfying the minimum cost. Using these components, optimized sections are firstly obtained for each predefined concrete strength applied to the whole floors in the project building. The obtained results for each concrete strength are comparatively examined to determine the fittest sections which will also result in the fittest vertical zoning for concrete strength. The main optimization scenario for this is to search for the vertical levels where the identical optimized sections coincide for the two different concrete strengths in concern, and select those levels for the boundaries where a concrete strength will be changed to another. The optimization process provided in this research is a product of an intensive development designed for a specific member in a specific project. Thus, the algorithm suggested takes on a microscopic and mathematical approach. However, the technique has a lot of potential that it can further be extensively developed and applied for future projects.

• Korean Journal of Computational Design and Engineering
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• v.21 no.4
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• pp.389-396
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• 2016

The necessity for environment-friendly material development has emerged in the recent automotive field due to stricter regulations on fuel economy and environmental concerns. Accordingly, the automotive industry is paying attention to carbon fiber reinforced plastic (CFRP) material with high strength and stiffness properties while the lightweight. In this study, we determine a shape of lower control arm (LCA) for maximizing the strength and stiffness by optimizing the thickness of each layer when the stacking angle is fixed due to the CFRP manufacturing problems. Composite materials are laminated in the order of $0^{\circ}$, $90^{\circ}$, $45^{\circ}$, and $-45^{\circ}$ with a symmetrical structure. For the approximate optimal design, we apply a sequential two-point diagonal quadratic approximate optimization (STDQAO) and use a process integrated design optimization (PIDO) code for this purpose. Based on the physical properties calculated within a predetermined range of laminate thickness, we perform the FEM analysis and verify whether it satisfies the load and stiffness conditions or not. These processes are repeated for successive improved objective function. Optimized CFRP LCA has the equivalent stiffness and strength with light weight structure when compared to conventional aluminum design.

• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.