• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.3
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• pp.171-180
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• 2016

Earthquake event keeps increasing every year, and the recent cases of earthquake hazards invoke the necessity of seismic study in Korea, as geotechnical earthquake hazards, such as strong ground motion, liquefaction and landslides, are a significant threat to structures in industrial hub areas including coastal facilities. In this study, systemized framework of integrated assessment of earthquake-induced geotechnical hazard was established using advanced geospatial database. And a visible simulation of the framework was specifically conducted at two coastal facility areas in Incheon. First, the geospatial-grid information in the 3D domain were constructed with geostatistical interpolation method composed of multiple geospatial coverage mapping and 3D integration of geo-layer construction considering spatial outliers and geotechnical uncertainty. Second, the behavior of site-specific seismic responses were assessed by incorporating the depth to bedrock, mean shear wave velocity of the upper 30 m, and characteristic site period based on the geospatial-grid. Third, the normalized correlations between rock-outcrop accelerations and the maximum accelerations of each grid were determined considering the site-specific seismic response characteristics. Fourth, the potential damage due to liquefaction was estimated by combining the geospatial-grid and accelerations correlation grid based on the simplified liquefaction potential index evaluation method.

• Journal of Korean Association for Spatial Structures
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• v.8 no.3
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• pp.91-99
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• 2008

As number of the buildings increases, it shows new trends such as twisted, tilted, taperer shape. As a structural solution for the new trend buildings, diagonal grid (Diagrid) structure was developed. Though a few buildings was built using diagird system, the structural performance of the corresponding node was not clearly identified. Therefore, experimental evaluation is needed to apply diagrid for higher buildings. In this study, the node was tested depending on the amounts of welding materials. As a result, the partial welding can provide enough strength for the node as required in the full penetration welding under monotonin compressive loadings.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.29-37
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• 2022

The collapse of reinforced concrete (RC) frame buildings is mainly caused by the failure of columns. To prevent brittle failure of RC column, numerous studies have been conducted on the seismic performance of strengthened RC columns. Concrete jacketing method, which is one of the retrofitting method of RC members, can enhance strength and stiffness of original RC column with enlarged section and provide uniformly distributed lateral load capacity throughout the structure. The experimental studies have been conducted by many researchers to analyze seismic performance of seismic strengthened RC column. However, structures which have plan and vertical irregularities shows torsional behavior, and therefore it causes large deformation on RC column when subjected to seismic load. Thus, test results from concentric cyclic loading can be overestimated comparing to eccentric cyclic test results, In this paper, two kinds of eccentric loading pattern was suggested to analyze structural performance of RC columns, which are strengthened by concrete jacketing method with new details in jacketed section. Based on the results, it is concluded that specimens strengthened with new concrete jacketing method increased 830% of maximum load, 150% of maximum displacement and changed the failure modes of non-strengthened RC columns.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Steel and Composite Structures
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• v.31 no.1
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• pp.43-51
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• 2019

This paper is concerned with the numerical calculation of mixed-mode stress intensity factors (SIFs) of 2-D isotropic functionally graded materials (FGMs) by the natural element method (more exactly, Petrov-Galerkin NEM). The spatial variation of elastic modulus in non-homogeneous FGMs is reflected into the modified interaction integral ${\tilde{M}}^{(1,2)}$. The local NEM grid near the crack tip is refined, and the directly approximated strain and stress fields by PG-NEM are enhanced and smoothened by the patch recovery technique. Two numerical examples with the exponentially varying elastic modulus are taken to illustrate the proposed method. The mixed-mode SIFs are parametrically computed with respect to the exponent index in the elastic modulus and external loading and the crack angle and compared with the other reported results. It has been justified from the numerical results that the present method successfully and accurately calculates the mixed-mode stress intensity factors of 2-D non-homogeneous functionally graded materials.

• Journal of Korean Association for Spatial Structures
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• v.12 no.1
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• pp.59-68
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• 2012

The space frame structure is one of the large span structural system consisting of longitudinal and latitudinal members. The members are connected in three dimension. A space frame structure has high stiffness with a structure resisting external forces in steric conformation. According to many structural conditions, structural stability problems in the space frame are determined and considered very important. This study seeks to understand the space frame collapse mechanism using the 2-free nodes truss model in order to examine static structural instability characteristics of the latticed dome. According to geometrical shape, the star dome, parallel lamella dome and three way grid dome were selected as models. The models were examined for characteristics of instability under STEP Excitations behavior according to rise-span ratio(${\mu}$) and shape imperfection.

• Journal of Korean Association for Spatial Structures
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• v.12 no.2
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• pp.109-118
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• 2012

Few paper deal with the dynamic bucking under the load with periodic characteristics, and the behavior under periodic excitation is expected the different behavior against STEP excitation. A space frame structure has high stiffness with a structure resisting external forces in steric conformation. According to many structural conditions, structural stability problems in the space frame are determined and considered very important. This study seeks to understand the space frame collapse mechanism using the 2-free nodes truss model in order to examine static structural instability characteristics of the latticed dome. According to geometrical shape, the star dome, parallel lamella dome and three way grid dome were selected as models. The models were examined for characteristics of instability behavior according to rise-span ratio(${\mu}$) and shape imperfection.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.53-58
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• 2020

The purpose of this study is to build a database of Spatial information Bigdata of cities using satellite images and spatial information, and to examine the correlations with the surface temperature. Using architectural structure and usage in building information, DEM and Slope topographical information for constructed with 300 × 300 mesh grids for Busan. The satellite image is used to prepare the Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Bare Soil Index (BI), and Land Surface Temperature (LST). In addition, the building area in the grid was calculated and the building ratio was constructed to build the urban environment DB. In architectural structure, positive correlation was found in masonry and concrete structures. On the terrain, negative correlations were observed between DEM and slope. NDBI and BI were positively correlated, and NDVI was negatively correlated. The higher the Building ratio, the higher the surface temperature. It was found that the urban environment DB could be used as a basic data for urban environment analysis, and it was possible to quantitatively grasp the impact on the architecture and urban environment by adding local meteorological factors. This result is expected to be used as basic data for future urban environment planning and disaster prevention data construction.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.