• 대한기계학회논문집A
• /
• 제31권1호
• /
• pp.26-35
• /
• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

• 한국산학기술학회논문지
• /
• 제17권6호
• /
• pp.26-32
• /
• 2016

일반적으로 구조물에는 하중, 재료상수, 부재크기와 구조해석 등의 오차에 대한 불확실성을 존재하고 이러한 불확실성은 구조물의 최적설계에 많은 영향을 준다. 확률론적 해석은 급속하게 발전하고 있고 여러 불확실성을 고려해야 하는 구조설계에서 중요한 기법으로 사용되고 있다. 본 논문에서는 구조물에서 발생하는 불확실성을 고려하기 위하여 신뢰성 해석을 통하여 신뢰도 지수를 산출하였으며 이 값을 최적설계의 제약조건으로 설정하여 확률론적 최적설계를 수행하였다. 최적설계 결과 기존의 불확실량이 고려되지 않은 확정론적 최적설계의 결과 값보다 불확실량이 고려된 최적설계 결과값이 더 크게 나타났으나 불확실성을 고려하는 경우가 구조물의 안정성이 더 확보되는 것으로 생각된다. 본 논문에서는 최적화 기법 중 가장 강력하다고 알려진 SQP(순차이차계획법)을 이용하여 최적화를 수행하였는데 SQP법은 최적화 문제의 정식화를 반복계산 하는 것에 바탕을 두고 각 반복계산에 있어서는 2차 프로그래밍 부속 문제의 해를 구하는데 그 기본을 두는 방법이다. 또 불확실량을 고려한 최적설계를 위해 신뢰성을 기초한 최적설계를 수행하여 신뢰도지수와 파괴확률을 계산하였다. 확정론적 최적설계와 달리 치수, 모양, 재료와 작용하중들의 양에 신뢰성해석을 수행하여 나온 신뢰도지수와 파괴확률을 앞에서 계산한 최적화 과정의 제약조건식에 가적으로 설정하여 최적설계를 수행하였다.T-stub 접합부를 예제로 적용하였으며 해석 결과의 기존문헌과 비교하였다.

• 한국전산구조공학회논문집
• /
• 제21권5호
• /
• pp.413-419
• /
• 2008

확정론적 최적설계에서는 설계변수의 변동이나 불확실성 등을 최적화 과정에서 고려하지 않는다. 신뢰성 최적설계는 설계변수의 임의성을 체계적인 확률 및 통계이론을 적용하여 생산품의 안정성을 보다 정밀하고 합리적으로 다룬다. 본 논문에서 설계변수를 확률변수로 취급하여 실제 제작시의 제작오차를 고려한 표준편차를 주었으며, 설계변수의 평균에 대한 표준편차를 기존의 고정된 값을 사용하지 앉고 평균과 표준편차의 관계가 오목함수로 나타나도록 하였다 즉, 설계변수의 평균이 달라짐에 따라 표준편차도 변동계수만큼 변하도록 하였다. 신뢰성해석은 불변 2차 모멘트 방법을 이용하고 신뢰성을 구하는 방법은 신뢰도 지수 접근방법의 개선된 일계 2차 모멘트 방법을 이용하여 신뢰성을 구하였다. 두 가지 예제를 통해 확정론적 최적설계, 신뢰성 최적설계와 표준편차의 변동을 고려한 신뢰성 최적설계의 값을 비교하였다.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2008년도 추계학술대회A
• /
• pp.571-575
• /
• 2008

An engine cradle is a quite important structural assembly for supporting the engine, suspension and steering parts of vehicle and absorbing the vibrations during the drive and the shock in the car crash. Recently, the engine cradle having structural stiffness enough to support the surrounding parts and absorbing the shock of collision has been widely used. The hydroforming technology may cause many advantages to automotive applications in terms of better structural integrity of parts, reduction of production cost, weight reduction, material saving, reduction in the number of joining processes and improvement of reliability. We focus on increasing the durability and the dynamic performance of engine cradle. For realizing this objective, several optimization design techniques such as shape, size, and topology optimization are performed. This optimization scheme based on the sensitivity can provide distinguished performance improvement in using hydroforming.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권4호
• /
• pp.1579-1602
• /
• 2020

In the design of production system, buffer capacity allocation is a major step. Through polymorphism analysis of production capacity and production capability, this paper investigates a buffer allocation optimization problem aiming at the multi-stage production line including unreliable machines, which is concerned with maximizing the system theoretical production rate and minimizing the system state entropy for a certain amount of buffers simultaneously. Stochastic process analysis is employed to establish Markov models for repairable modular machines. Considering the complex structure, an improved vector UGF (Universal Generating Function) technique and composition operators are introduced to construct the system model. Then the measures to assess the system's multi-state reliability and structural complexity are given. Based on system theoretical production rate and system state entropy, mathematical model for buffer capacity optimization is built and optimized by a specific genetic algorithm. The feasibility and effectiveness of the proposed method is verified by an application of an engine head production line.

• Structural Engineering and Mechanics
• /
• 제11권2호
• /
• pp.151-170
• /
• 2001

With increasing competition, the engineering industry is in need of optimization of designs that would lead to minimum cost or weight. Recent developments in Genetic Algorithms (GAs) makes it possible to model and obtain optimal solutions in structural design that can be put to use in industry. The main objective of this paper is to illustrate typical applications of GAs to practical design of structural systems such as steel trusses, towers, bridges, reinforced concrete frames, bridge decks, shells and layout planning of buildings. Hence, instead of details of GA process, which can be found in the reported literature, attention is focussed on the description of the various applications and the practical aspects that are considered in Genetic Modeling. The paper highlights scope and future directions for wider applications of GA based methodologies for optimal design in practice.

• Structural Engineering and Mechanics
• /
• 제45권1호
• /
• pp.111-127
• /
• 2013

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

• Structural Engineering and Mechanics
• /
• 제64권4호
• /
• pp.413-426
• /
• 2017

Probabilistic methods are used in engineering where a computational model contains random variables. The proposed method under development: Direct Optimized Probabilistic Calculation (DOProC) is highly efficient in terms of computation time and solution accuracy and is mostly faster than in case of other standard probabilistic methods. The novelty of the DOProC lies in an optimized numerical integration that easily handles both correlated and statistically independent random variables and does not require any simulation or approximation technique. DOProC is demonstrated by a collection of deliberately selected simple examples (i) to illustrate the efficiency of individual optimization levels and (ii) to verify it against other highly regarded probabilistic methods (e.g., Monte Carlo). Efficiency and other benefits of the proposed method are grounded on a comparative case study carried out using both the DOProC and MC techniques. The algorithm has been implemented in mentioned software applications, and has been used effectively several times in solving probabilistic tasks and in probabilistic reliability assessment of structures. The article summarizes the principles of this method and demonstrates its basic possibilities on simple examples. The paper presents unpublished details of probabilistic computations based on this method, including a reliability assessment, which provides the user with the probability of failure affected by statistically dependent input random variables. The study also mentions the potential of the optimization procedures under development, including an analysis of their effectiveness on the example of the reliability assessment of a slender column.

• 대한기계학회논문집A
• /
• 제38권4호
• /
• pp.379-384
• /
• 2014

가스터빈엔진의 가장 핵심 부품인 디스크와 블레이드는 고온, 고압축비, 고속 회전이라는 가혹한 환경에서 지속적으로 운용된다. 이러한 가혹한 환경과 디스크와 블레이드가 가지는 큰 회전 에너지로 인해 디스크 및 블레이드에 의해 유발되는 파손은 항공기 손상 혹은 탑승자의 피해로 이어지는 재해적 고장 혹은 한계 고장으로 이어진다. 그러므로 디스크와 블레이드의 구조적 건전성의 마진을 충분히 확보하기 위해서 본 연구에서는 디스크의 취약 부위인 도브테일의 형상을 최적화하고, 그 해의 강건성을 확인하기 위해 치수 공차와 피로 수명의 산포와 같은 불확실성에 대하여 신뢰도 해석을 수행하고자 한다. 이 결과를 통해 결정론적 방법인 최적설계의 필요성과 함께 한계를 확인하고, 향후 신뢰도 기반 최적설계의 필요성을 인지하고자 한다. 이를 위해 비선형 열-구조 연성해석과 접촉 해석을 포함한 유한요소해석을 수행하였다.

• Computational Structural Engineering : An International Journal
• /
• 제2권1호
• /
• pp.11-17
• /
• 2002

Cost-effectiveness in design is considered for determining the target reliability of concrete bridges under seismic actions. This objective can be achieved based on the economic optimization of the expected life-cycle cost of a bridge, which includes initial cost, direct losses, and indirect losses of a bridge due to strong earthquakes over its lifetime. A separating factor is defined to consider the redundancy of a transportation network. The Park-Ang damage model is employed to define the damage of a bridge under seismic action, and a Monte Carlo method based on the DRAIN-2DX program is developed to assess the failure probability of a bridge. The results for an example bridge analyzed in this paper show that the optimal target failure probability depends on the traffic volume carried by the bridge and is between 1.0×10/sup -3/ to 3.0×10/sup -3/ over a life of 50 years.