• Computers and Concrete
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• 제10권5호
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• pp.457-467
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• 2012

Using appropriate raw materials for cement is crucial for providing the required products. Monitoring relationships and analyzing distributions in a cement material quarry are important stages in the process. CaO, one of the substantial chemical components, is included in some raw materials such as limestone and marl; furthermore, appraising spatial assessment of this chemical component is also very critical. In this study, spatial evaluation and monitoring of CaO concentrations in a cement site are considered. For this purpose, two effective regression-based models were applied to a cement quarry located in Turkey. For the assessment, some spatial models were developed and performance comparisons were carried out. The results show that the regression-based spatial modelling is an efficient methodology and it can be employed to evaluate spatially varying relationships in a cement quarry.

• Steel and Composite Structures
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• 제23권5호
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• pp.501-516
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• 2017

Numerical simulations are prevalently used to evaluate the seismic behaviour of structures. The accuracy of the simulation results depends directly on the accuracy of the modelling techniques employed to simulate the behaviour of individual structural members. An empirical modelling technique is employed in this paper to simulate the behaviour of column members under cyclic and seismic loading. Despite the common modelling techniques, this technique is capable of simulating two important aspects of the cyclic and seismic behaviour of columns simultaneously. The proposed fiber-based modelling technique captures explicitly the interaction between the bending moment and the axial force in columns, and the cyclic deterioration of the hysteretic behaviour of these members is implicitly taken into account. The fiber-based model is calibrated based on the cyclic behaviour of square hollow steel sections. The behaviour of several column archetypes is investigated under a dual cyclic loading protocol to develop a benchmark database before the calibration procedure. The dual loading protocol used in this study consists of both axial and lateral loading cycles with varying amplitudes. After the calibration procedure, a regression analysis is conducted to derive an equation for predicting a varying calibrated modelling parameter. Finally, several nonlinear time-history analyses are conducted on a 6-story steel special moment frame in order to investigate how the results of numerical simulations can be affected by employing the intended modelling technique for columns instead of other common modelling techniques.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.995-1004
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• 2007

In this paper, we analyse online word-of-mouth and Korean box-office sales data based on kernel regression method. To do this, we consider the regression model with mixed-data and apply the least square cross-validation method proposed by Li and Racine (2004) to the model. We found the box-office sales can be explained by volume of online word-of-mouth and the characteristics of the movies.

• Earthquakes and Structures
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• 제12권1호
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• pp.55-65
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• 2017

Earthquake resilience of substations is essential for reliable and sustainable service of electrical grids. The majority of substation equipment consists of cylindrical porcelain components, which are vulnerable to earthquake shakings due to the brittleness of porcelain material. Failure of porcelain equipment has been repeatedly observed in recent earthquakes. Hence, proper seismic modelling of porcelain equipment is important for various limit state checks in both product manufacturing stage and detailed substation design stage. Sheds on porcelain core and cemented joint between porcelain component and metal cap have significant effects on the dynamic properties of the equipment, however, such effects have not been adequately parameterized in existing design guidelines. This paper addresses this critical issue by developing a method for taking these two effects into account in seismic modelling based on numerical and analytical approaches. Equations for estimating the effects of sheds and cemented joint on flexural stiffness are derived, respectively, by regression analyses based on the results of 12 pieces of full-scale equipment in 500kV class or higher. The proposed modelling technique has further been validated by shaking table tests.

• 응용통계연구
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• 제13권2호
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• pp.407-414
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• 2000

Carroll과 Ruppert(1988)는 준가능도(quasi-likelihood)를 이용하여 에스트라제 측정자료를 회귀분석하였다. Jung과 Lee(1997)는 준가능도을 이용한 회귀분석모형의 적합도정통계량을 제안하였으며 검정 별과 기각되지 않아 본 분석모형이 타당하다고 주장하였다. 그러나 Lee와 Nelder(1998)의 잔차그림을 검토한 결과, 상기 모형으로는 평균증가에 따른 분산증가를 충분히 반영할 수 없었다. 본 논문에서는 Lee와 Nelder(1998)의 평균과 분산의 동시모형으로 에스트라제 자료를 재분석하고 잔차그림을 이용하여 모형의 타당성을 재평가하였다. 또한 분산에서 산포모형에 대한 적합도검정에는 Lee와 Nelder(1998)의 제한가능도(restricted likelihood)에 근거한 검정법이 보다 적절함을 제시하였다.

• Computers and Concrete
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• 제24권1호
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• pp.7-17
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• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

• Steel and Composite Structures
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• 제42권6호
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• pp.733-745
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• 2022

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

• 대한원격탐사학회지
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• 제35권1호
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• pp.137-149
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• 2019

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.83-86
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• 2005

This paper presents a method for point-based 3D building reconstruction using LiDAR data and digital map. The proposed method consists of three processes: extraction of building roof points, identification of roof types, and 3D building reconstruction. After extracting points inside the polygon of building, the ground surface, wall and tree points among the extracted points are removed through the filtering process. The filtered points are then fitted into the flat plane using ODR(Orthogonal Distance Regression). If the fitting error is within the predefined threshold, the surface is classified as a flat roof. Otherwise, the surface is fitted and classified into a gable or arch roof through RMSE analysis. Based on the roof types identified in automated fashion, the 3D building reconstruction is performed. Experimental results showed that the proposed method classified successfully three different types of roof and that the fusion of LiDAR data and digital map could be a feasible method of modelling 3D building reconstruction.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2019년도 추계 학술논문 발표대회
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• pp.16-17
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• 2019

In order to measure the amount of carbon dioxide emitted from the construction sites, many literature which have been conducted have proposed methodologies for calculating coefficients based on actual data collections for estimating the emission formula. The existing data collected under controlled conditions not on site measurement were too limited to apply in actual sites. The purpose of this study is to conduct analysis based on the data measured in fields and to present predictive models using artificial neural network and nonlinear regression analysis for appropriate predictions and practical applications.