• Structural Engineering and Mechanics
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• 제48권3호
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• pp.395-414
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• 2013

The present study fundamentally investigated the mechanical performance of the rib-stiffened super-wide bridge deck with twin box girders in concrete, which is a very popular application to efficiently widen the bridges with normal span. The shear lag effects of the specific cross-sections were firstly studied. The spatial stress distribution and local stiffness of the bridge deck with twin box girders were then investigated under several typical wheel load conditions. Meanwhile, a comparative study for the bridge deck with and without stiffening ribs was also carried out during the investigation; thereby, a design optimization for the stiffening ribs was further suggested. Finally, aiming at the preliminary design, an approximate methodology to manually calculate the bending moments of the rib-stiffened bridge deck was analytically proposed for engineers to quickly assess its performance. This rib-stiffened bridge deck with twin box girders can be widely applied for concrete (especially concrete cable-stayed) bridges with normal span, however, requiring a super-wide bridge width due to the traffic flow.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권4호
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• 한국자동차공학회논문집
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• 제12권2호
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• pp.123-130
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• 2004

The automobile suspension system is composed of parts that affect performances of a vehicle such as ride quality, handling characteristics, straight performance and steering effort, etc. Moreover, by using the finite element analysis the cost for the initial design step can be decreased. In the design of a suspension system, usually system vibration and structural rigidity must be considered simultaneously to satisfy dynamic and static requirements simultaneously. In this paper, we consider the weight reduction and the increase of the first eigen-frequency of a suspension part, the upper control arm, especially using topology optimization and size optimization. Firstly, we obtain the initial design to maximize the first eigen-frequency using topology optimization. Then, we apply the multi-objective parameter optimization method to satisfy both the weight reduction and the increase of the first eigen-frequency. The design variables are varying during the optimization process for the multi-objective. Therefore, we can obtain the deterministic values of the design variables not only to satisfy the terms of variation limits but also to optimize the two design objectives at the same time. Finally, we have executed reliability based optimal design on the upper control arm using the Monte-Carlo method with importance sampling method for the optimal design result with 98％ reliability.

• Earthquakes and Structures
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• 제18권2호
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• pp.185-199
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• 2020

A computationally-efficient method for multi-criteria optimisation is developed for performance-based seismic design of friction energy dissipation dampers in RC structures. The proposed method is based on the concept of Uniform Distribution of Deformation (UDD), where the slip-load distribution along the height of the structure is gradually modified to satisfy multiple performance targets while minimising the additional loads imposed on existing structural elements and foundation. The efficiency of the method is demonstrated through optimisation of 3, 5, 10, 15 and 20-storey RC frames with friction wall dampers subjected to design representative earthquakes using single and multi-criteria optimisation scenarios. The optimum design solutions are obtained in only a few steps, while they are shown to be independent of the selected initial slip loads and convergence factor. Optimum frames satisfy all predefined design targets and exhibit up to 48% lower imposed loads compared to designs using a previously proposed slip-load distribution. It is also shown that dampers designed with optimum slip load patterns based on a set of spectrum-compatible synthetic earthquakes, on average, provide acceptable design solutions under multiple natural seismic excitations representing the design spectrum.

• Earthquakes and Structures
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• 제8권1호
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• pp.57-76
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• 2015

The Performance-based Earthquake Engineering (PBEE) concept implies the definition of multiple target performance levels of damage which are expected to be achieved (or not exceeded), when the structure is subjected to earthquake ground motion of specified intensity. These levels are associates to different return period (RP) of earthquakes and structural behaviors quantified with adopted factors or indexes of control. In this work an 8-level PBEE study is carried out, finding different curves for control index or Engineering Demand Parameters (EDP) of levels that assess the structural behavior. The results and the curves for each index of control allow to deduce the structural behavior at an a priori unspecified RP. A general methodology is proposed that takes into account a possible optimization process in the PBEE field. Finally, an application to 8-level seismic performance assessment to structure in a Spanish seismic zone permits deducing that its behavior is deficient for high seismic levels (RP > 475 years). The application of the methodology to a low-to-moderate seismic zone case proves to be a good tool of structural seismic design, applying a more sophisticated although simple PBEE formulation.

• Structural Engineering and Mechanics
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• 제89권2호
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• pp.213-223
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• 2024

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.

• Structural Engineering and Mechanics
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• 제51권4호
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• pp.547-564
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• 2014

This study focuses on the application of an active tuned mass damper (ATMD) for controlling the seismic response of an 11-story building. The control action is achieved by combination of a fuzzy logic controller (FLC) and Particle Swarm Optimization (PSO) method. FLC is used to handle the uncertain and nonlinear phenomena while PSO is used for optimization of FLC parameters. The FLC system optimized by PSO is called PSFLC. The optimization process of the FLC system has been performed for an 11-story building under the earthquake excitations recommended by International Association of Structural Control (IASC) committee. Minimization of the top floor displacement has been used as the optimization criteria. The results obtained by the PSFLC method are compared with those obtained from ATMD with GFLC system which is proposed by Pourzeynali et al. and non-optimum FLC system. Based on the parameters obtained from PSFLC system, a global controller as PSFLCG is introduced. Performance of the designed PSFLCG has been checked for different disturbances of far-field and near-field ground motions. It is found that the ATMD system, driven by FLC with the help of PSO significantly reduces the peak displacement of the example building. The results show that the PSFLCG decreases the peak displacement of the top floor by about 10%-30% more than that of the FLC system. To show the efficiency and superiority of the adopted optimization method (PSO), a comparison is also made between PSO and GA algorithms in terms of success rate and computational processing time. GA is used by Pourzeynali et al for optimization of the similar system.

• 한국전기전자재료학회논문지
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• 제21권11호
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• pp.987-994
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• 2008

The optimal structure of the Tonpilz transducer was designed. First, the FE model of the transducer was constructed, that included all the details of the transducer which used practical environment. The validity of the FE model was verified through the impedance analysis of the transducer. Second, the resonance frequency, the sound pressure, the bandwidth, and the impulsive shock pressure of the transducer in relation to its structural variables were analyzed. Third, the design method of $2^n$ experiments was employed to reduce the number of analysis cases, and through statistical multiple regression analysis of the results, the functional forms of the transducer performances that could consider the cross-coupled effects of the structural variables were derived. Based on the all results, the optimal geometry of the Tonpilz transducer that had the highest sound pressure level at the desired working environment was determined through the optimization with the SQP-PD method of a target function composed of the transducer performance. Furthermore, for the convenience of a user, the automatic process program making the optimal structure of the acoustic transducer automatically at a given target and a desired working environment was made. The developed method can reflect all the cross-coupled effects of multiple structural variables, and can be extended to the design of general acoustic transducers.

• Structural Engineering and Mechanics
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• 제63권4호
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Advances in Computational Design
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• 제2권4호
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• pp.313-331
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• 2017

In this study, teaching-learning based optimization (TLBO) is improved by incorporating model of multiple teachers, adaptive teaching factor, self-motivated learning, and learning through tutorial. Modified TLBO (MTLBO) is applied for simultaneous topology, shape, and size optimization of space and planar trusses to study its effectiveness. All the benchmark problems are subjected to stress, displacement, and kinematic stability constraints while design variables are discrete and continuous. Analyses of unacceptable and singular topologies are prohibited by seeing element connectivity through Grubler's criterion and the positive definiteness. Performance of MTLBO is compared to TLBO and state-of-the-art algorithms available in literature, such as a genetic algorithm (GA), improved GA, force method and GA, ant colony optimization, adaptive multi-population differential evolution, a firefly algorithm, group search optimization (GSO), improved GSO, and intelligent garbage can decision-making model evolution algorithm. It is observed that MTLBO has performed better or found nearly the same optimum solutions.