• Geomechanics and Engineering
• /
• 제34권6호
• /
• pp.597-609
• /
• 2023

One of the applicable methods for the stabilization of soil walls is the nailing system which consists of tensile struts. The stability and safety of soil nail wall systems are influenced by the geometrical parameters of the nailing system. Generally, the determination of nailing parameters in order to achieve optimal performance of the nailing system for the safety of soil walls is defined in the framework of optimization problems. Also, according to the various uncertainty in the mechanical parameters of soil structures, it is necessary to evaluate the reliability of the system as a probabilistic problem. In this paper, the optimal design of the nailing system is carried out in deterministic and probabilistic cases using meta-heuristic and reliability-based design optimization methods. The colliding body optimization algorithm and first-order reliability method are used for optimization and reliability analysis problems, respectively. The objective function is defined based on the total cost of nails and safety factors and reliability index are selected as constraints. The mechanical properties of the nailing system are selected as design variables and the mechanical properties of the soil are selected as random variables. The results show that the reliability of the optimally designed soil nail system is very sensitive to uncertainty in soil mechanical parameters. Also, the design results are affected by uncertainties in soil mechanical parameters due to the values of safety factors. Reliability-based design optimization results show that a nailing system can be designed for the expected level of reliability and failure probability.

• Structural Engineering and Mechanics
• /
• 제87권3호
• /
• pp.211-219
• /
• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Advances in Computational Design
• /
• 제9권2호
• /
• pp.115-136
• /
• 2024

Stochastic optimization methods have been extensively studied for structural optimization in recent decades. In this study, a novel algorithm named the CA-SA method, is proposed for topology optimization of steel double-layer grid structures. The CA-SA method is a hybridized algorithm combining the Simulated Annealing (SA) algorithm and the Cellular Automata (CA) method. In the CA-SA method, during the initial iterations of the SA algorithm, some of the preliminary designs obtained by SA are placed in the cells of the CA. In each successive iteration, a cell is randomly chosen from the CA. Then, the "local leader" (LL) is determined by selecting the best design from the chosen cell and its neighboring ones. This LL then serves as the leader for modifying the SA algorithm. To evaluate the performance of the proposed CA-SA algorithm, two square-on-square steel double-layer grid structures are considered, with discrete cross-sectional areas. These numerical examples demonstrate the superiority of the CA-SA method over SA, and other meta-heuristic algorithms reported in the literature in the topology optimization of large-scale skeletal structures.

• 한국컴퓨터정보학회논문지
• /
• 제19권9호
• /
• pp.161-168
• /
• 2014

공항에 불규칙한 시간간격으로 접근하는 항공기들을 최소의 비용으로 착륙시키는 항공기 착륙 문제 (ALP)는 최적 해를 구하기 어려워 다양한 메타휴리스틱 방법들이 제안되고 있다. 본 논문에서는 ALP에 대해 O(nlog n)의 다항시간으로 최적 해를 구하는 휴리스틱 알고리즘을 제안한다. 제안된 알고리즘은 착륙 목표시간 오름차순으로 정렬시키고, 항공기들 간의 분리 시간과 착륙 비용을 고려하여 착륙순서를 변경시킨 최적화 과정을 수행하는 방법을 적용하였다. ALP에 대한 예제 데이터인 Airland1 ~ Airland8에 대해 소요비용이 0이 되는 활주로 개수 m까지 25개 데이터를 실험한 결과 모든 데이터에 대해 최적 해를 구하였다. 특히, Airland8의 m = 1 데이터에 대해서는 기존에 알려진 최적 해를 개선하였다.

• Journal of Electrical Engineering and Technology
• /
• 제12권2호
• /
• pp.612-620
• /
• 2017

Relay coordination in power system is a complex problem and so far, meta-heuristic algorithms and other methods as an alternative approach may not properly deal with large scale relay coordination due to their huge time consuming computation. In some cases the relay coordination could be unachievable. As the urgency for a proper approach is essential, in this paper an innovative and simple relay coordination method is introduced that is able to be applied on optimization algorithms for relay protection coordination. The objective function equation of operating time of relays are divided into two separate functions with less constraints. As the analytical results show here, this equivalent method has a remarkable speed with high accuracy to coordinate directional relays. Two distribution systems including directional overcurrent relays are studied in DigSILENT software and the collected data are examined in MATLAB. The relay settings of this method are compared with particle swarm optimization and genetic algorithm. The analytical results show the correctness of this mathematical and practical approach. This fast coordination method has a proper velocity of convergence with low iteration that can be used in large scale systems in practice and also to provide a feasible solution for protection coordination in smart grids as online or offline protection coordination.

• 정보처리학회논문지C
• /
• 제14C권2호
• /
• pp.179-184
• /
• 2007

이동 통신 시스템의 설계에 있어서 기지국의 위치를 선정하는 문제는 기본적으로 셀 내부 및 외부의 간접전파에 의한 최소 SIR을 만족하면서 최대한의 사용자를 최소의 기지국에 할당하는 문제로서 NP-hard 이다. 기존에 주로 사용된 목적함수는 창고위치문제에서 사용하던 것으로 CDMA 이동통신 시스템으로 직접 이용하는 단계에서 문제점이 발생한다. 그 문제점들을 해결하는 목적함수와 최적해 및 근사해를 구하는 알고리즘을 제안하고, 그에 따른 시뮬레이션을 하여 본 논문의 제안이 타당성이 있는지 평가 및 분석하였다. 본 논문에서는 기지국의 위치문제를 경험적 탐색방법을 사용하지 않고 혼합정수계획법의 완전해를 이용하여 최적해 및 근사해를 구하였다.

• 제어로봇시스템학회논문지
• /
• 제16권8호
• /
• pp.761-765
• /
• 2010

This paper presents the design of optimal fuzzy classifier for human detection by using genetic algorithms, one of the best-known meta-heuristic search methods. For this purpose, encoding scheme to search the optimal sequential intersection points between adjacent fuzzy membership functions is originally presented for the fuzzy classifier design for HOG (Histograms of Oriented Gradient) descriptors. The intersection points are sequentially encoded in the proposed encoding scheme to reduce the redundancy of search space occurred in the combinational problem. Furthermore, the fitness function is modified with the true-positive and true-negative of the confusion matrix instead of the total success rate. Experimental results show that the two proposed approaches give superior performance in HOG datasets.

• Journal of Information Processing Systems
• /
• 제13권4호
• /
• pp.1000-1013
• /
• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• Structural Engineering and Mechanics
• /
• 제47권1호
• /
• pp.119-129
• /
• 2013

Nonlinear Static Procedures (NSPs) have been developed as a practical tool to estimate the seismic demand of structures. Several researches have accomplished to minimize errors of NSPs, namely pushover procedures, in the Nonlinear Time History Analysis (NTHA), as the most exact method. The most important issue in a typical pushover procedure is the pattern and technique of loading which are extracted based on structural dynamic fundamentals. In this paper, the coefficients of modal force combination is focused involving a meta-heuristic optimization algorithm to find the optimum load pattern which results in a response with minimum amount of errors in comparison to the NTHA counterpart. Other parameters of the problem are based on the FEMA recommendations for pushover analysis of building structures. The proposed approach is implemented on a high-rise 20 storey concrete moment resisting frame under three earthquake records. In order to demonstrate the effectiveness and robustness of the studied procedure the results are presented beside other well-known pushover methods such as MPA and the FEMA procedures, and the results show the efficiency of the proposed load patterns.

• Structural Engineering and Mechanics
• /
• 제90권2호
• /
• pp.189-208
• /
• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.