• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.19 no.5
• /
• pp.637-644
• /
• 2010

This paper presents a reliability-based shape optimization (RBSO) using the growth-strain method. An actual design involves uncertain conditions such as material property, operational load, Poisson's ratio and dimensional variation. The purpose of the RBSO is to consider the variations of probabilistic constraint and performances caused by uncertainties. In this study, the growth-strain method was applied to shape optimization of reliability analysis. Even though many papers for reliability-based shape optimization in mathematical programming method and ESO (Evolutionary Structural Optimization) were published, the paper for the reliability-based shape optimization using the growth-strain method has not been applied yet. Growth-strain method is applied to performance measure approach (PMA), which has probabilistic constraints that are formulated in terms of the reliability index, is adopted to evaluate the probabilistic constraints in the change of average mises stress. Numerical examples are presented to compare the DO with the RBSO. The results of design example show that the RBSO model is more reliable than deterministic optimization. It was verified that the reliability-based shape optimization using growth-strain method are very effective for general structure. The purpose of this study is to improve structure's safety considering probabilistic variable.

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

• Transactions of the Korean hydrogen and new energy society
• /
• v.32 no.4
• /
• pp.212-218
• /
• 2021

In this study, we conducted a design optimization of the Type 4 composite pressure vessels to enhance the pressure-resistant performance of the vessels while keeping the thickness of the composite layer. The design variables for the optimization were the stacking angles of the helical layers of the vessels to improve the performance. Since the carbon fibers are expensive material, it is desirable to reduce the use of the carbon fibers by applying an optimal design of the composite pressure vessel. The structural analysis and optimization process for the design of Type 4 composite pressure vessels were carried out using a commercial finite element analysis software, Abaqus and a plug-in for automated simulation, Isight, respectively. The optimization results confirmed the performance and safety of the optimized Type 4 composite pressure vessels was enhanced by 12.84% compared to the initial design.

• Smart Structures and Systems
• /
• v.16 no.2
• /
• pp.243-262
• /
• 2015

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2004.04a
• /
• pp.135-142
• /
• 2004

The apparent advantages of FRP (fiber reinforced plastics) composites over the conventional structural materials may be attributed to their high specific strength and stiffness. Other affordable properties of FRPs including an excellent durability make them particularly attractive for the structures in severe service conditions. Therefore, the material and sectional properties of a FRP structural component should be designed to meet its specific requirements and service conditions. This paper is performed the material property optimization under optimum design of pultruded FRP bridge deck section. In the problem formulation, an objective function is selected to minimize the maximum R(strength ratio). The thickness of layers, volumes of fibers and matrix fiber orientation, and stacking sequence of FRPs are used as the design variables. Strength ratio in the design code, material failure criteria and pultruded manufacture thickness are selected as the design constraints to enhance the material performance of FRP decks. From the results of the numerical investigation, we obtained the optimum deck section profile for conventional using object.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.14 no.2
• /
• pp.213-224
• /
• 2001

확률론적 구조설계 최적화는 구조물의 역학적 특성이나 하중의 불확실성이나 임의성과 같은 변동성을 정량적이고 합리적으로 고려할 수 있다는 점에서 기존의 전통적인 확정론적 최적화와 비교된다. 확률론적 최적화의 방법론으로는 개선된 일계이차모멘트법을 이용하는 신뢰도지수에 기반한 접근법(MPFP search)이 널리 알려져 있으며, 최근 목표성능치에 기반한 접근법(MPTP search)이 새롭게 제안되었다. 본 논문에서는 이들 두 가지 접근법에 대한 정식화를 수행하고, 특히 탐색과정에서 소모적인 반복계산을 발견하고 제거하는 알고리즘을 제시하였다. 예제에서 두 접근법에 의한 확률론적 최적화를 수행하고 구조설계 최적화의 관점에서 두 접근법의 장단점을 비교·검토하였다.

• Structural Monitoring and Maintenance
• /
• v.1 no.4
• /
• pp.427-449
• /
• 2014

A hybrid optimization method for the identification of state-space models is presented in this study. Hybridization is succeeded by combining the advantages of deterministic and stochastic algorithms in a superior scheme that promises faster convergence rate and reliability in the search for the global optimum. The proposed hybrid algorithm is developed by replacing the original stochastic mutation operator of Evolution Strategies (ES) by the Levenberg-Marquardt (LM) quasi-Newton algorithm. This substitution results in a scheme where the entire population cloud is involved in the search for the global optimum, while single individuals are involved in the local search, undertaken by the LM method. The novel hybrid identification framework is assessed through the Monte Carlo analysis of a simulated system and an experimental case study on a shear frame structure. Comparisons to subspace identification, as well as to conventional, self-adaptive ES provide significant indication of superior performance.

• Computers and Concrete
• /
• v.19 no.5
• /
• pp.589-597
• /
• 2017

Strut-and-tie modeling method, which evolved on truss-model approach, has generally been preferred for the design of complex reinforced concrete structures and structural elements that have critical shear behavior. Some structural members having disturbed regions require exceptional detailing for all support and loading conditions, such as the beam-column connections, deep beams, short columns or corbels. Considering the general expectation of exhibiting brittle behavior, corbels are somewhat dissimilar to other shear critical structures. In this study, reinforcement layout of a corbel model was determined by the participation of structural optimization and strut-and-tie modeling methods, and an experimental comparison was performed against a conventionally designed model.

• Proceedings of the KSR Conference
• /
• 2007.05a
• /
• pp.138-143
• /
• 2007

The leading-cab (high energy absorption area) of rolling stock directly is impacted on the frontal crash unlike other cabs. Thus, leading-cab has a structurally complex shape to solve getting concentrated loads. However, in order to enhance structural performance and to achieve the weight reduction of cab, changing the sizes and adjusting the distance of members do not take an effective result. Therefore, in design phase, to find the material arrangement which helps structural capacity be better should be done. This research applies the topology optimization to concept design of protective shell frame on strategy of crush energy absorption with considering pressure and vertical loads acting on the principal part of leading-cab. In this research, topology optimization method focuses on structural design, and which yields optimal material arrangement under given loads and boundary conditions using density method which has the density of material as design variables. Finally, this research presents optimal material arrangement and structure of protective shell frame on given loads with applying topology optimization.

• Smart Structures and Systems
• /
• v.14 no.1
• /
• pp.55-70
• /
• 2014

The paper presents a multi-objective optimization strategy for a multi-type sensor placement for Structural Health Monitoring (SHM) of long span bridges. The problem is formulated for simultaneous placement of strain sensors and accelerometers (heterogeneous network) based on application demands for SHM system. Modal Identification (MI) and Accurate Mode Shape Expansion (AMSE) were chosen as the application demands for SHM. The optimization problem is solved through the use of integer Genetic Algorithm (GA) to maximize a common metric to ensure adequate MI and AMSE. The performance of the joint optimization problem solved by GA is compared with other established methods for homogenous sensor placement. The results indicate that the use of a multi-type sensor system can improve the quality of SHM. It has also been demonstrated that use of GA improves the overall quality of the sensor placement compared to other methods for optimization of sensor placement.