• Proceedings of the Korea Concrete Institute Conference
• /
• 2008.04a
• /
• pp.293-296
• /
• 2008

Creep and shrinkage in concrete structures are very complex phenomena in which various uncertainties exist with regard to inherent material variations as well as modeling uncertainties. The creep and shrinkage models which are capable of predicting long-term structural response are specified in design codes such as ACI 209-92, CEB-FIP Model Code 90, etc. However, in the prediction formulas of creep and shrinkage effects of concrete, various kinds of parameters are involved to express the characteristics of concrete under consideration (i.e. the proportion of concrete, the shape of the structure, relative humidity, etc.). And the predicted values from each design code under same environment differ from each other. To predict the characteristics of concrete, the long-term experiments of creep and shrinkage is necessary but this is not suitable for a construction field. In this study, adjustment method of creep coefficient using sensitivity analysis is proposed to predict creep coefficient of concrete exactly and it is checked up on the validity of the predicting method by comparing to the assumed value and predicted one.

• Korean Journal of Radiology
• /
• v.22 no.3
• /
• pp.334-343
• /
• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• Steel and Composite Structures
• /
• v.18 no.3
• /
• pp.547-563
• /
• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• Computers and Concrete
• /
• v.13 no.6
• /
• pp.799-827
• /
• 2014

In this paper, a theoretical and experimental study of the sectional behaviour of reinforced concrete beams subjected to short-term loads is carried out. The pure bending behaviour is analysed with moment-curvature diagrams. Thus, the experimental results obtained from 24 beams tested by the authors and reported in literature are compared with theoretical results obtained from a layered model, which combines the material parameters defined in Model Code 2010 with some of the most recognized tensions-tiffening models. Although the tests were carried out for short-term loads, the analysis demonstrates that rheological effects can be important and must be accounted to understand the experimental results. Another important conclusion for the beams tested in this work is that the method proposed by EC-2 tends to underestimate the tension-stiffening effects, leading to inaccuracies in the estimations of deflections. Thus, the actual formulation is analysed and a simple modification is proposed. The idea is the separation of the deflection prediction in two parts: one for short-term loads and other for rheological effects (shrinkage). The results obtained are in fairly good agreement with the experimental results, showing the feasibility of the proposed modification.

• Structural Engineering and Mechanics
• /
• v.46 no.1
• /
• pp.53-73
• /
• 2013

Prediction of prestressed concrete girder integral abutment bridge (IAB) load effect requires understanding of the inherent uncertainties as it relates to thermal loading, time-dependent effects, bridge material properties and soil properties. In addition, complex inelastic and hysteretic behavior must be considered over an extended, 75-year bridge life. The present study establishes IAB displacement and internal force statistics based on available material property and soil property statistical models and Monte Carlo simulations. Numerical models within the simulation were developed to evaluate the 75-year bridge displacements and internal forces based on 2D numerical models that were calibrated against four field monitored IABs. The considered input uncertainties include both resistance and load variables. Material variables are: (1) concrete elastic modulus; (2) backfill stiffness; and (3) lateral pile soil stiffness. Thermal, time dependent, and soil loading variables are: (1) superstructure temperature fluctuation; (2) superstructure concrete thermal expansion coefficient; (3) superstructure temperature gradient; (4) concrete creep and shrinkage; (5) bridge construction timeline; and (6) backfill pressure on backwall and abutment. IAB displacement and internal force statistics were established for: (1) bridge axial force; (2) bridge bending moment; (3) pile lateral force; (4) pile moment; (5) pile head/abutment displacement; (6) compressive stress at the top fiber at the mid-span of the exterior span; and (7) tensile stress at the bottom fiber at the mid-span of the exterior span. These established IAB displacement and internal force statistics provide a basis for future reliability-based design criteria development.

• Journal of Korea Foundry Society
• /
• v.9 no.1
• /
• pp.39-48
• /
• 1989

A basic three dimensional thermal model has been developed to simulate the solidification sequence for gravity die casting process. The finite difference method was used to analyze the solidification process during all the casting cycles. The prediction of die temperature in the quasi-steady state was analyzed by the boundary element method. The influence of die cooling on the heat flow in the cast/mold system was also investigated. Predictions of the computer simulation on temperature profiles and location of shrinkage defects were in good agreement with those observed in experimental die castings. Models of computer simulation which is developed by this work can be useful for the design and process control of die casting.

• Computers and Concrete
• /
• v.5 no.4
• /
• pp.295-328
• /
• 2008

A previously published multiscale model for early-age cement-based materials [Pichler, et al.2007. "A multiscale micromechanics model for the autogenous-shrinkage deformation of early-age cement-based materials." Engineering Fracture Mechanics, 74, 34-58] is extended towards upscaling of viscoelastic properties. The obtained model links macroscopic behavior, i.e., creep compliance of concrete samples, to the composition of concrete at finer scales and the (supposedly) intrinsic material properties of distinct phases at these scales. Whereas finer-scale composition (and its history) is accessible through recently developed hydration models for the main clinker phases in ordinary Portland cement (OPC), viscous properties of the creep active constituent at finer scales, i.e., calcium-silicate-hydrates (CSH) are identified from macroscopic creep tests using the proposed multiscale model. The proposed multiscale model is assessed by different concrete creep tests reported in the open literature. Moreover, the model prediction is compared to a commonly used macroscopic creep model, the so-called B3 model.

• Journal of the Korea Concrete Institute
• /
• v.14 no.6
• /
• pp.813-822
• /
• 2002

The purpose of the present study is first to refine the mathematical material models for moisture and temperature distributions in early-age concrete and then to incorporate those models into finite element procedure. The three dimensional finite element program developed in the present study can determine the degree of hydration, temperature and moisture distribution in hardening concrete. It is assumed that temperature and humidity fields are fully uncoupled and only the degree of hydration is coupled with two state variables. Mathematical formulation of degree of hydration Is based on the combination of three rate functions of reaction. The effect of moisture condition as well as temperature on the rate of reaction is considered in the degree of hydration model. In moisture transfer, diffusion coefficient is strongly dependent on the moisture content in pore system. Many existing models describe this phenomenon according to the composition of mixture, especially water to cement ratio, but do not consider the age dependency. Microstructure is changing with the hydration and thus transport coefficients at early ages are significantly higher because the pore structure in the cement matrix is more open. The moisture capacity and sink are derived from age-dependent desorption isotherm. Prediction of a moisture sink due to the hydration process, i.e. self-desiccation, is related to autogenous shrinkage, which may cause early-age cracking in high strength and high performance concrete. The realistic models and finite element program developed in this study provide fairly good results on the temperature and moisture distribution for early-age concrete and correlate very well with actual test data.

• Korean Journal of Radiology
• /
• v.24 no.5
• /
• pp.395-405
• /
• 2023

Objective: This study aimed to develop and validate models using radiomics features on a native T1 map from cardiac magnetic resonance (CMR) to predict left ventricular reverse remodeling (LVRR) in patients with nonischemic dilated cardiomyopathy (NIDCM). Materials and Methods: Data from 274 patients with NIDCM who underwent CMR imaging with T1 mapping at Severance Hospital between April 2012 and December 2018 were retrospectively reviewed. Radiomic features were extracted from the native T1 maps. LVRR was determined using echocardiography performed ≥ 180 days after the CMR. The radiomics score was generated using the least absolute shrinkage and selection operator logistic regression models. Clinical, clinical + late gadolinium enhancement (LGE), clinical + radiomics, and clinical + LGE + radiomics models were built using a logistic regression method to predict LVRR. For internal validation of the result, bootstrap validation with 1000 resampling iterations was performed, and the optimism-corrected area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CI) was computed. Model performance was compared using AUC with the DeLong test and bootstrap. Results: Among 274 patients, 123 (44.9%) were classified as LVRR-positive and 151 (55.1%) as LVRR-negative. The optimism-corrected AUC of the radiomics model in internal validation with bootstrapping was 0.753 (95% CI, 0.698-0.813). The clinical + radiomics model revealed a higher optimism-corrected AUC than that of the clinical + LGE model (0.794 vs. 0.716; difference, 0.078 [99% CI, 0.003-0.151]). The clinical + LGE + radiomics model significantly improved the prediction of LVRR compared with the clinical + LGE model (optimism-corrected AUC of 0.811 vs. 0.716; difference, 0.095 [99% CI, 0.022-0.139]). Conclusion: The radiomic characteristics extracted from a non-enhanced T1 map may improve the prediction of LVRR and offer added value over traditional LGE in patients with NIDCM. Additional external validation research is required.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.4
• /
• pp.12-22
• /
• 2021

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F₁-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.