• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.7_spc
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• pp.425-434
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• 2016

This study investigates important parameters used to determine an effective peak ground acceleration (EPGA) based on the characteristics of response spectra of historical earthquakes occurred at Korean peninsula. EPGAs are very important since they are implemented in the Korean Building Code for the seismic design of new structures. Recently, the Gyeongju earthquakes with the largest magnitude in earthquakes measured at Korea took place and resulted in non-structural and structural damage, which their EPGAs should need to be evaluated. This paper first describes the basic concepts on EPGAs and the EPGAs of the Gyeongju earthquakes are then evaluated and compared according to epicentral distances, site classes and directions of seismic waves. The EPGAs are dependant on normalizing factors and ranges of period on response spectrum constructed with the Gyeongju earthquake records. Using the normalizing factors and the ranges of period determined based on the characteristics of domestic response spectra, this paper draw a conclusion that the EPGAs are estimated to be about 30 % of the measured peak ground accelerations (PGA).

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.803-819
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• 2013

This paper addresses two main issues relevant to the structural assessment of buildings subjected to explosions. The first issue regards the robustness evaluation of steel frame structures: a procedure is provided for computing "robustness curves" and it is applied to a 20-storey steel frame building, describing the residual strength of the (blast) damaged structure under different local damage levels. The second issue regards the precise evaluation of blast pressures acting on structural elements using Computational Fluid Dynamic (CFD) techniques. This last aspect is treated with particular reference to gas explosions, focusing on some critical parameters (room congestion, failure of non-structural walls and ignition point location) which influence the development of the explosion. From the analyses, it can be deduced that, at least for the examined cases, the obtained robustness curves provide a suitable tool that can be used for risk management and assessment purposes. Moreover, the variation of relevant CFD analysis outcomes (e.g., pressure) due to the variation of the analysis parameters is found to be significant.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.102-110
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• 1998

The horizontal fillet welding is prevalently used in heavy and ship building industries to fabricate the large scale structures. A deep understanding of the horizontal fillet welding process is restricted, because the phenomena occurring in welding are very complex and highly non-linear characteristics. To achieve the satisfactory weld bead geometry in robot welding system, the seam tracking algorithm should be reliable. The number of seam tracker was developed for arc welding automation by now. Among these seam tracker, the arc sensor is prevalently used in industrial robot welding system because of its low cost and flexibility. However, the accuracy of arc sensor would be decreased due to the electrical noise and metal transfer. In this study, the signal processing algorithm based on the neural network was implemented to enhance the reliability of measured welding current signals. Moreover, the seam tracking algorithm in conjunction with the signal processing algorithm was implemented to trace the center of weld line. It was revealed that the neural network could be effectively used to predict the welding current signal at the end of weaving.

• Journal of Information Management
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• v.30 no.3
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• pp.44-54
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• 1999

A description of SGML(Standard Generalized Markup Language) is given together with a detailed description of WIPO Standard ST.32. The benefits of the use of SGML are highlighted-its system Independence and flexibility in building publication systems and full-text databases. A structure of WIPO Standard ST,32 based patent content is defined by DTD(document type definition) written in ST.32, and full-text itself is described with generalized markup depending on DTD. This article explains how to represent a document structure : a hierarchy structure like a entire document, a specific, sub-document, a paragraph, or non-hirarchy structure like a table drawings, or chemical structures. Merits of SGML In patent document processing are also discussed.

• Earthquakes and Structures
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• v.3 no.3_4
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• pp.611-628
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• 2012

The inelastic earthquake response of existing, reinforced concrete buildings with an open ground story, designed according to the old Greek codes, is investigated before and after their seismic strengthening with steel braces restricted to the open ground stories. The seismic performance evaluation is based on Part 3 of Eurocode 8 for assessment and retrofitting of buildings. Three and five-story, symmetric and non-symmetric buildings are subjected to a set of seven pairs of synthetic accelerograms, compatible with the design spectrum, and conclusions are drawn regarding the effectiveness of the strengthening solutions. Seismic behavior of the selected models confirms results of previous work regarding the insufficient capacity of the open ground stories for design level earthquakes. It is also shown that strengthening only the weak ground story, a choice having the substantial advantage of low cost and continued usage of the building during its seismic retrofitting, can remove the inherent weakness without shifting the problem to the stories above and thus making such buildings at least as strong as those without a weak first story. This partial strengthening is possible for symmetric as well as eccentric buildings, in which torsion plays a further detrimental role.

• Smart Structures and Systems
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• v.26 no.6
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• pp.753-763
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• 2020

The early prediction of Compressive Strength of Concrete (CSC) is a significant task in the civil engineering construction projects. This study, therefore, is dedicated to introducing two novel hybrids of neural computing, namely Shuffled Complex Evolution (SCE) and Teaching-Learning-Based Optimization (TLBO) for predicting the CSC. The algorithms are applied to a Multi-Layer Perceptron (MLP) network to create the SCE-MLP and TLBO-MLP ensembles. The results revealed that, first, intelligent models can properly handle analyzing and generalizing the non-linear relationship between the CSC and its influential parameters. For example, the smallest and largest values of the CSC were 17.19 and 58.53 MPa, and the outputs of the MLP, SCE-MLP, and TLBO-MLP range in [17.61, 54.36], [17.69, 55.55] and [18.07, 53.83], respectively. Second, applying the SCE and TLBO optimizers resulted in increasing the correlation of the MLP products from 93.58 to 97.32 and 97.22%, respectively. The prediction error was also reduced by around 34 and 31% which indicates the high efficiency of these algorithms. Moreover, regarding the computation time needed to implement the SCE-MLP and TLBO-MLP models, the SCE is a considerably more time-efficient optimizer. Nevertheless, both suggested models can be promising substitutes for laboratory and destructive CSC evaluative models.

• Steel and Composite Structures
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• v.39 no.1
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.259-270
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• 2022

This study explores the use of the recently developed "strain-sensing smart skin" (S⁴) method for noncontact strain measurements on cement-based samples. S⁴ sensors are single-wall carbon nanotubes dilutely embedded in thin polymer films. Strains transmitted to the nanotubes cause systematic shifts in their near-infrared fluorescence spectra, which are analyzed to deduce local strain values. It is found that with cement-based materials, this method is hampered by spectral interference from structured near-infrared cement luminescence. However, application of an opaque blocking layer between the specimen surface and the nanotube sensing film enables interference-free strain measurements. Tests were performed on cement, mortar, and concrete specimens with such modified S⁴ coatings. When specimens were subjected to uniaxial compressive stress, the spectral peak separations varied linearly and predictably with induced strain. These results demonstrate that S⁴ is a promising emerging technology for measuring strains down to ca. 30 𝜇𝜀 in concrete structures.

• Smart Structures and Systems
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• v.28 no.6
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• pp.727-735
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• 2021

Recently, non-destructive health monitoring methods such as magnetic flux leakage (MFL) method, have become popular due to their advantages over destructive methods. Currently, numerical study on this field has been limited to simplified studies by only obtaining MFL instead of induced voltage inside coil sensor. In this study, it was proposed to perform a novel numerical simulation of MFL's coil sensor by considering vital parameters including specimen's motion with constant velocity and saturation status of specimen in time domain. A steel-rod specimen with two stepwise cross-sectional changes (i.e., 21% and 16%) was fabricated using low carbon steel. In order to evaluate the results of numerical simulation, an experimental test was also conducted using a magnetic probe, with same size specimen and test parameters, exclusively. According to comparative results of numerical simulation and experimental test, similar signal amplitude and signal pattern were observed. Thus, proposed numerical simulation method can be used as a reliable source to check efficiency of sensor probe when different size specimens with different defects should be inspected.

• Smart Structures and Systems
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• v.30 no.4
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.