• Geomechanics and Engineering
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• 제18권5호
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• pp.493-502
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• 2019

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.

• Structural Engineering and Mechanics
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• 제63권2호
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• pp.147-160
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• 2017

In this study, the optimum parameters of Tuned Mass Dampers (TMDs) are proposed using Gravity Search Algorithm (GSA) and Particle Swarm Optimization (PSO) to reduce the responses of the structures. A MATLAB program is developed to apply the new approach to the benchmark 10 and 40-story structures. The obtained results are compared to those of other optimization methods used in the literature to verify the developed code. To show the efficiency and accuracy of the proposed methods, nine far-field and near-field worldwide earthquakes are applied to the structures. The results reveal that in the 40-story structure, GSA algorithm can reduce the Relative Displacement (RD) and Absolute Acceleration (AA) up to 43% and 21%, respectively while the PSO decreases them by 50% and 25%, respectively. In contrast, both GSA and PSO algorithms reduce the RD and AA about 29% and 21% for the 10-story structure. Furthermore, using the proposed approach the required TMD parameters reduce by 47% and 63% in the 40 and 10-story buildings in comparison with the referenced ones. Result evaluation and related comparison indicate that these methods are more effective even by using smaller TMD parameters resulting in the reduction of acting force from TMD, having smaller stiffness and damping factors while being more cost effective due to its decreased parameters. In other words, the TMD with optimum parameters can play a positive role in both tall and typical structures.

• Genomics & Informatics
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• 제10권2호
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• pp.123-127
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• 2012

Gene set analysis (GSA) is useful in interpreting a genome-wide association study (GWAS) result in terms of biological mechanism. We compared the performance of two different GSA implementations that accept GWAS p-values of single nucleotide polymorphisms (SNPs) or gene-by-gene summaries thereof, GSA-SNP and i-GSEA4GWAS, under the same settings of inputs and parameters. GSA runs were made with two sets of p-values from a Korean type 2 diabetes mellitus GWAS study: 259,188 and 1,152,947 SNPs of the original and imputed genotype datasets, respectively. When Gene Ontology terms were used as gene sets, i-GSEA4GWAS produced 283 and 1,070 hits for the unimputed and imputed datasets, respectively. On the other hand, GSA-SNP reported 94 and 38 hits, respectively, for both datasets. Similar, but to a lesser degree, trends were observed with Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets as well. The huge number of hits by i-GSEA4GWAS for the imputed dataset was probably an artifact due to the scaling step in the algorithm. The decrease in hits by GSA-SNP for the imputed dataset may be due to the fact that it relies on Z-statistics, which is sensitive to variations in the background level of associations. Judicious evaluation of the GSA outcomes, perhaps based on multiple programs, is recommended.

• Steel and Composite Structures
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• 제50권5호
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• pp.583-594
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• 2024

In this paper, a Guided Simulated Annealing (GSA) algorithm is presented to optimize 2D and 3D steel frames against Progressive Collapse. Considering the nature of structural optimization problems, a number of restrictions and improvements have been applied to the decision mechanisms of the algorithm without harming the randomness. With these improvements, the algorithm aims to focus relatively on the flawed variables of the analyzed frame. Besides that, it is intended to be more rational by instituting structural constraints on the sections to be selected as variables. In addition to the LRFD restrictions, the alternate path method with nonlinear dynamic procedure is used to assess the risk of progressive collapse, as specified in the US Department of Defense United Facilities Criteria (UFC) Design of Buildings to Resist Progressive Collapse. The entire optimization procedure was carried out on a C# software that supports parallel processing developed by the authors, and the frames were analyzed in SAP2000 using OAPI. Time history analyses of the removal scenarios are distributed to the processor cores in order to reduce computational time. The GSA produced 3% lighter structure weights than the SA (Simulated Annealing) and 4% lighter structure weights than the GA (Genetic Algorithm) for the 2D steel frame. For the 3D model, the GSA obtained 3% lighter results than the SA. Furthermore, it is clear that the UFC and LRFD requirements differ when the acceptance criteria are examined. It has been observed that the moment capacity of the entire frame is critical when designing according to UFC.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.332-343
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• 2022

To address the problems of the gravitational search algorithm (GSA) in which the population is prone to converge prematurely and fall into the local solution when solving the single-objective optimization problem, a sine map jumping gravity search algorithm based on asynchronous learning is proposed. First, a learning mechanism is introduced into the GSA. The agents keep learning from the excellent agents of the population while they are evolving, thus maintaining the memory and sharing of evolution information, addressing the algorithm's shortcoming in evolution that particle information depends on the current position information only, improving the diversity of the population, and avoiding premature convergence. Second, the sine function is used to map the change of the particle velocity into the position probability to improve the convergence accuracy. Third, the Levy flight strategy is introduced to prevent particles from falling into the local optimization. Finally, the proposed algorithm and other intelligent algorithms are simulated on 18 benchmark functions. The simulation results show that the proposed algorithm achieved improved the better performance.

• Structural Engineering and Mechanics
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• 제60권4호
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• pp.729-747
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• 2016

This study aims to present a novel optimization algorithm known as gravitational search algorithm (GSA) for structural damage detection. An objective function for damage detection is established based on structural vibration data in frequency domain, i.e., natural frequencies and mode shapes. The feasibility and efficiency of the GSA are testified on three different structures, i.e., a beam, a truss and a plate. Results show that the proposed strategy is efficient for determining the locations and the extents of structural damages using the first several modal data of the structure. Multiple damages cases in different types of structures are studied and good identification results can be obtained. The effect of measurement noise on the identification results is investigated.

• 한국인터넷방송통신학회논문지
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• 제18권4호
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• pp.149-154
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• 2018

안정된 결혼문제에 대해서는 Gale과 Shapley 알고리즘(GSA)이 유일하게 알려져 왔다. 이 알고리즘은 남성이 자신이 가장 선호하는 여성에게 청혼하면 여성이 수락/거절하는 방식(MP)으로 남성 최적-여성 최악의 결과이지만 항상 안정된 매칭 결과를 얻는다. 남성을 여성으로 바꾸어 여성 청혼-남성 수락 방식(WP)을 적용하면 전혀 다른 결과를 얻을 수 있다. 또한 MP나 WP로도 최적의 안정된 매칭 결과를 얻지 못하는 경우도 발생한다. 본 논문에서는 MP와 WP의 이러한 문제점을 해결하기 위해 어떠한 경우라도 최적의 안정된 매칭 결과를 얻는 방법을 제안한다. 제안된 알고리즘은 여성 최악인 MP 결과에 대해 여성을 보상하기 위해 여성이 보다 선호하는 남성들을 대상으로 k명의 여성이 짝을 상호 교환하는 k-opt를 수행하는 방식을 제안하였다. 다양한 사례에 대해 실험을 한 결과 제안된 알고리즘은 MP나 WP로도 얻지 못하는 최적의 안정된 매칭 결과를 얻을 수 있었다.

• 한국측량학회지
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• 제32권2호
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• pp.117-132
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• 2014

본 논문에서는 사용되는 센서의 특성, 특히 밴드 간 분광 응답함수의 차이에 따라 달라질 수 있는 영상융합 기법의 성능 차이에 대한 상대적 비교 분석을 주된 연구 목적으로 하였다. 이를 위해, CS 기반의 대표적 영상융합 기법인 GIHS, GS1, GSA, GSA-CA을 선정하였고, 거의 동일 시기/동일 지역을 촬영한 KOMPSAT-2/3 위성영상을 이용하였다. 또한, 융합 영상의 성능 평가는 정량적/정성적 방법을 혼용하여 진행하였다. 분석 결과, KOMPSAT-2/3 영상 모두에서 GSA 기법과 GSA-CA 기법이 다른 기법들에 비해 상대적으로 우수한 품질을 나타냈다. 이는 다른 위성영상 및 문헌을 통해 제시된 결과들과 일치하는 것으로, 선형 회귀식을 통해 전정색 영상과 intensity 영상 간 스펙트럴 불일치를 최소화한 기존 방법론의 효능을 뒷받침하는 것이다. 그러나 동일한 실험 조건을 적용하였을 때, KOMPSAT-2와 KOMPSAT-3에서 나타내는 융합 성능은 서로 다른 결과를 나타냈다. 이 같은 결과는 두 위성 센서 내 밴드별 분광응답함수가 서로 다른 것에 기인하는 것으로 판단할 수 있다. KOMPSAT-2의 경우, blue 밴드와 green 밴드의 분광 응답비가 전정색 밴드를 초과하는 것으로 알려져 있으며, 이는 영상 융합 과정에서 과도한 저주파 요소를 삽입하여, 최종적으로 제작된 융합 영상에서의 색상 왜곡을 유발하는 원인이 되는 것으로 알려져 있다. 반면, KOMPSAT-3에서는 KOMPSAT-2에서 발생되었던 상당부분의 관련 문제들을 보완 하였으며, 결과적으로 동일한 실험 조건에서도 상대적으로 향상된 융합 결과를 나타냈다.

• 한국해양공학회지
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• 제17권6호
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• pp.38-46
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• 2003

Aim of this article is to propose Micro-Genetic Simulated Annealing (${\mu}GSA$) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithm (${\mu}GAs$) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, ${\mu}GSA$ approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the ${\mu}GSA$, the five well-know benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimism the weight function. From the simulation results, it has been concluded that the proposed ${\mu}GSA$ approach is an effective optimisation tool for soloing continuous nonlinear global optimisation problems in suitable computational time frame.

• Structural Engineering and Mechanics
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• 제86권2호
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• pp.181-195
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• 2023

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L-box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.