• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.167-174
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• 2007

This study proposes a practical and realistic method to establish an optimal lifetime maintenance strategy for deteriorating bridges by considering the life-cycle performance as well as the life-cycle cost. The proposed method offers a set of optimal tradeoff maintenance scenarios among other conflicting objectives, such as minimizing cost and maximizing performance. A genetic algorithm is used to generate a set of maintenance scenarios that is a multi-objective combinatorial optimization problem related to the and the life-cycle cost and performance as separate objective functions. A computer program, which generates optimal maintenance scenarios, was developed based on the proposed method. The subordinate relation between bridge members has been considered to decide optimal maintenance sequence. The developed program has been used to present a procedure for finding an optimal maintenance scenario for steel-girder bridges on the Korean National Road. Through this bridge maintenance scenario analysis, it is expected that the developed method and program can be effectively used to allow bridge managers an optimal maintenance strategy satisfying various constraints and requirements.

• Journal of Korean Association for Spatial Structures
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• v.4 no.2 s.12
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• pp.89-97
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• 2004

The objective of this study is the development of a scheme and discrete optimum design algorithm, which is based on the genetic algorithm. The algorithm can perform both scheme and size optimum designs of plane trusses. The developed Scheme genetic algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of structures and the constraints are limits on loads and serviceability. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. However, its application to the complicated structures has been limited because of the extreme time need for a number of structural analyses. This study solves the problem by introducing the size & scheme genetic algorithm operators into the genetic algorithm. The genetic process virtually takes no time. However, the evolutionary process requires a tremendous amount of time for a number of structural analyses. Therefore, the application of the genetic algorithm to the complicated structures is extremely difficult, if not impossible. The scheme genetic algorithm operators was introduced to overcome the problem and to complement the evolutionary process. It is very efficient in the approximate analyses and scheme and size optimization of plane trusses structures and considerably reduces structural analysis time. Scheme and size discrete optimum combined into the genetic algorithm is what makes the practical discrete optimum design of plane fusses structures possible. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying the algorithm to various optimum design examples: plane pratt, howe and warren truss.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.4
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• pp.109-120
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• 2007

In this study, a bridge maintenance system is developed to generate performance-based optimum maintenance strategy by considering the life-cycle cost. A multi-objective combinatorial optimization problem is formulated to generate a tradeoff maintenance scenarios which satisfies the balance among the conflicting objectives such as the performance and cost during the bridge lifetime and a genetic algorithm is applied to the system. By using the developed program, this study proposes a process of optimum maintenance scenario applying to the steel girder bridge of national road. The developed system improves the current methods of establishing the bridge maintenance strategy and can be utilized as an efficient tool to provide the optimum bridge maintenance scenario corresponding to the various constraints and requirements of bridge agency.

• Journal of Information Science Theory and Practice
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• v.7 no.4
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• pp.56-64
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• 2019

Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2277-2298
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• 2019

Adaptive learning in e-learning has garnered researchers' interest. In it, learning resources could be recommended automatically to achieve a personalized learning experience. There are various ways to realize it. One of the realistic ways is adaptive learning path recommendation, in which learning resources are provided according to learners' requirements. This paper summarizes existing works and proposes an innovative approach. Firstly, a learner-centred concept map is created using graph theory based on the features of the learners and concepts. Then, the approach generates a linear concept sequence from the concept map using the proposed traversal algorithm. Finally, Learning Objects (LOs), which are the smallest concrete units that make up a learning path, are organized based on the concept sequences. In order to realize this step, we model it as a multi-objective combinatorial optimization problem, and an improved immune algorithm (IIA) is proposed to solve it. In the experimental stage, a series of simulated experiments are conducted on nine datasets with different levels of complexity. The results show that the proposed algorithm increases the computational efficiency and effectiveness. Moreover, an empirical study is carried out to validate the proposed approach from a pedagogical view. Compared with a self-selection based approach and the other evolutionary algorithm based approaches, the proposed approach produces better outcomes in terms of learners' homework, final exam grades and satisfaction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6A
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• pp.1023-1032
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• 2006

In this paper, a new method for the bridge maintenance is proposed to overcome the limit of the existing methods and to implement the preventive bridge maintenance system. The proposed method can establish the lifetime optimum maintenance strategy of the deteriorating bridges considering the life-cycle performance as well as the life-cycle cost. The lifetime performance of the deteriorating bridges is evaluated by the safety index based on the structural reliability and the condition index detailing the condition state. The life-cycle cost is estimated by considering not only the direct maintenance cost but also the user and failure cost. The genetic algorithm is applied to generate a set of maintenance scenarios which is the multi-objective combinatorial optimization problem related to the life-cycle cost and performance. The study examined the proposed method by establishing a maintenance strategy for the existing bridge and its advantages. The result shows that the proposed method can be effectively applied to deciding the bridge maintenance strategy.

• Geophysics and Geophysical Exploration
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• v.15 no.3
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• pp.129-135
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• 2012

The simultaneous-source full waveform inversion improves the applicability of full waveform inversion by reducing the computational cost. Since this technique adopts simultaneous multi-source for forward modeling, unwanted events remain in the residual seismograms when the receiver geometry of field acquisition is different from that of numerical modeling. As a result, these events impede the convergence of the full waveform inversion. In particular, the streamer-type data with limited offsets is the most difficult data to apply the simultaneous-source technique. To overcome this problem, the global-correlation-based objective function was suggested and it was successfully applied to the simultaneous-source full waveform inversion in time domain. However, this method distorts residual wavefields due to the modified objective function and has a negative influence on the inversion result. In addition, this method has not been applied to the frequency-domain simultaneous-source full waveform inversion. In this paper, we apply a timedamping function to the observed and modeled data, which are used to compute global correlation, to minimize the distortion of residual wavefields. Since the damped wavefields optimize the performance of the global correlation, it mitigates the distortion of the residual wavefields and improves the inversion result. Our algorithm incorporates the globalcorrelation-based full waveform inversion into the frequency domain by back-propagating the time-domain residual wavefields in the frequency domain. Through the numerical examples using the streamer-type data, we show that our inversion algorithm better describes the velocity structure than the conventional global correlation approach does.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.2
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• pp.73-84
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• 1993

In this research, configuration design optimization of plane truss structure has been tested by using decomposition technique. In the first level, the problem of transferring the nonlinear programming problem to linear programming problem has been effectively solved and the number of the structural analysis necessary for doing the sensitivity analysis can be decreased by developing stress constraint into member stress approximation according to the design space approach which has been proved to be efficient to the sensitivity analysis. And the weight function has been adopted as cost function in order to minimize structures. For the design constraint, allowable stress, buckling stress, displacement constraint under multi-condition and upper and lower constraints of the design variable are considered. In the second level, the nodal point coordinates of the truss structure are used as coordinating variable and the objective function has been taken as the weight function. By treating the nodal point coordinates as design variable, unconstrained optimal design problems are easy to solve. The decomposition method which optimize the section areas in the first level and optimize configuration variables in the second level was applied to the plane truss structures. The numerical comparisons with results which are obtained from numerical test for several truss structures with various shapes and any design criteria show that convergence rate is very fast regardless of constraint types and configuration of truss structures. And the optimal configuration of the truss structures obtained in this study is almost the identical one from other results. The total weight couldbe decreased by 5.4% - 15.4% when optimal configuration was accomplished, though there is some difference.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1217-1228
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• 2005

Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.