• Structural Engineering and Mechanics
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• v.58 no.3
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• pp.459-473
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• 2016

Various vague and unstructured problems encountered the civil engineering/designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

• Korean Journal of Computational Design and Engineering
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• v.21 no.2
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• pp.186-195
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• 2016

The finite element modeling of a ship for hull structural analysis on the basis of new harmonized common structural rules (CSR-H) is to be extended to the cargo holds in fore and after body of a ship. Unlike the parallel middle-body where the external and internal features of hull are equal along to the longitudinal direction of a ship, in fore and after body, the external and internal features of hull vary linearly or even irregularly in forms of a surface or a curve along to the longitudinal direction of a ship. Thus, it needs lots of design man-hours for the modeling for structural analysis. In order to save man-hours in initial structural design phase of a ship, the specified 3D CAD system has been adopted in shipbuilding industry. Through the interface between CAD and CAE (rule scantling and direct strength assessment), design man-hour in initial design phase can be saved even under the environment of CSR-H.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.1 s.71
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• pp.27-37
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• 2006

A Metropolis genetic algorithm (MGA) is developed and applied for the structural design optimization. In MGA, favorable features of Metropolis criterion of simulated annealing (SA) are incorporated in the reproduction operations of simple genetic algorithm (SGA). This way, the MGA maintains the wide varieties of individuals and preserves the potential genetic information of early generations. Consequently, the proposed MGA alleviates the disadvantages of premature convergence to a local optimum in SGA and time consuming computation for the precise global optimum in SA. Performances and applicability of MGA are compared with those of conventional algorithms such as Holland's SGA, Krishnakumar's micro GA, and Kirkpatrick's SA. Typical numerical examples are used to evaluate the computational performances, the favorable features and applicability of MGA. The effects of population sizes and maximum generations are also evaluated for the performance reliability and robustness of MGA. From the theoretical evaluation and numerical experience, it is concluded that the proposed MGA Is a reliable and efficient tool for structural design optimization.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.89-90
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• 2015

Recently, Incidence of natural disasters are growing gradually. The need for a monitoring system for maintaining the structural integrity of the high-rise buildings against extreme weather events such as typhoons, earthquakes is increasing gradually. One of the most important features in the tall building is to guarantee structural safety during the structure's life time. Structural monitoring technologies might be needed to manage structural safety and to protect human life.

• Structural Engineering and Mechanics
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• v.62 no.5
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• pp.537-550
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• 2017

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Journal of Integrative Natural Science
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• v.9 no.2
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• pp.136-143
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• 2016

KiSS1-derived peptide receptor, a GPCR protein, binds with the hormone kiss peptin. They are important in the neuroendocrine regulation of reproduction and in the secretion of gonadotrophin-releasing hormone. Thus, analysing the structural features of the receptor becomes important. However, the three dimensional structure of the protein is unavailable. Hence in this study, we have performed the homology modelling of KiSS1-derived peptide receptor with 5 different templates. 30 models were constructed using two platforms - Easymodeller and ITasser. The optimal models were chosen based on the model validation. Two models were selected after validation. The developed models could provide useful for analysing the structural features of KiSS1-derived peptide receptor and their pathophysiological role in various disorders related to them.

• Journal of Institute of Convergence Technology
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• v.4 no.2
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• pp.1-9
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• 2014

This paper examines feature-based reconstruction algorithm using feature-based modeling and based on topology optimization technology, which aims to achieve a minimal volume weight and to satisfy user-defined constraints such as stress, deformation related conditions. The finite element model after topology optimization allows us to remove some region of a solid model for predefined volume requirement. The stress or deformation distribution resulted from finite element analysis enables us to add some material to the solid model for a robust structure. For this purpose, we propose a feature-based redesign algorithm which inserts negative features to the solid model for material removal and positive features for material addition, and we introduce a bisection method which searches an optimal structure by iteratively applying the feature-based redesign algorithm. Several examples are considered to illustrate the proposed algorithms and to demonstrate the effectiveness of the present approach.

• Journal of Integrative Natural Science
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• v.9 no.4
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• pp.234-240
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• 2016

Bradykinin receptor B2, a GPCR protein, binds with the inflammatory mediator hormone bradkynin. It plays an important role in cross-talk between the renin-angiotensin system (RAS) and the kinin-kallikrein system (KKS). Also, it is involved in many processes including vasodilation, edema, smooth muscle spasm and pain fiber stimulation. Hence, studuying the structural features of the receptor becomes important. But the unavailability of the three dimensional structure of the protein makes the analysis difficult. Hence we have performed the homology modelling of Bradykinin receptor B2 with 5 different templates. 25 different homology models were constructed. Two best models were selected based on the model validation. The developed models could be helpful in analysing the structural features of Bradykinin receptor B2 and in pathophysiology of various disorders related to them.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 1999.04b
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• pp.315-319
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• 1999

A kind of image analysis system is used to investigate the structural features of the traditional handmade papers made from Kenaf and Abutilon. The screen mark on the paper was identified and analyzed. The dusts, shives and fiber bundles were manifested and calculated. The relationship between basis weight of the traditional paper and mean gray level of the image was discussed in detail. Some methods to express the formation of traditional handmade paper were studied.