• Steel and Composite Structures
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• 제39권3호
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• pp.319-335
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• 2021

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg-Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R² = 0.983, RMSE = 59.963 kN, a20 - index = 0.979) than the ANN-LM model (R² = 0.938, RMSE = 116.634 kN, a20 - index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.

• 콘크리트학회논문집
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• 제24권3호
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• pp.333-340
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• 2012

경량골재 콘크리트의 압축강도에 대한 크기 및 형상효과를 평가하기 위하여 9 배합의 실내 실험과 3 배합의 레미콘 배합을 수행하였다. 콘크리트 배합은 보통중량, 전경량 및 모래경량의 3그룹으로 분류되었다. 각 콘크리트 배합에서 원형 또는 사각형 단면을 갖는 시험체의 형상비는 1.0과 2.0이었다. 시험체의 단면 크기는 각 실내배합에서는 50~150mm, 각 레미콘 배합에서는 50~400mm 범위에 있었다. 실험 결과 경량골재 콘크리트의 균열진전과 국부 파괴영역은 보통중량 콘크리트와 상당히 달랐다. 경량골재 콘크리트에서 균열은 골재를 관통하였으며, 균열의 분포영역은 매우 국부적이었다. 이로 인해, 경량골재 콘크리트의 크기효과는 보통중량 콘크리트에 비해 더 크게 나타났으며, 이 현상은 형상비 1.0보다는 2.0인 시험체에서 더 뚜렷하게 나타났다. 김진근 등의 크기효과 예측모델은 경량골재 콘크리트에서 시험체 단면크기가 150mm 이상일 때 과대 평가하였다. 반면, 압축강도에 대한 시험체 형상의 영향을 보정하기 위해 ASTM 및 CEB-FIP에서 제시한 수정계수는 경량골재 콘크리트에서도 안전측에 있었다.

• Steel and Composite Structures
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• 제44권1호
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• pp.119-139
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• 2022

This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.

• Computers and Concrete
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• 제5권5호
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• pp.449-459
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• 2008

This paper focuses on the application of two dimensional orthogonal polynomials in the regression analysis for the relationship of product parameters viz. compressive strength, bulk density and water absorption of fly ash cement bricks with other process parameters such as percentages of fly ash, sand and cement. The method has been validated by linear and non-linear two parameter regression models. The use of two dimensional orthogonal system makes the analysis computationally efficient, simple and straight forward. Corresponding co-efficient of determination and F-test are also reported to show the efficacy and reliability of the relationships. By applying the evolved relationships, the product parameters of fly ash cement bricks may be approximated for the use in construction sectors.

• Computers and Concrete
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• 제21권5호
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• pp.559-567
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• 2018

In this study alkali activation of Electric Arc Furnace Slag (EAFS) is studied with a comprehensive test program. Three different silicate moduli (1-1,5-2), three different sodium concentrations (4%-6%-8%) for each silicate module, two different curing conditions (45%-98% relative humidity) for each sodium concentration, two different curing temperatures ($400^{\circ}C-800^{\circ}C$) for each relative humidity condition and two different curing time (6h-12h) for each curing temperature variables are selected and their effects on compressive strength was evaluated then regression equations using multiple linear regressions methods are fitted. And then to select the best regression models confirm with using the variables, the regression models compared between itself. An Artificial Neural Network (ANN) models that use silicate moduli, sodium concentration, relative humidity, curing temperature and curing time variables, are formed. After the investigation of these ANN models' results, ANN and multiple linear regressions based models are compared with each other. After that, an explicit formula is developed with values of the ANN model. As a result of this study, the fluctuations of data set of the compressive strength were very well reflected using both of the methods, multiple linear regression with quadratic terms and ANN.

• Structural Engineering and Mechanics
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• 제83권2호
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• pp.245-257
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• 2022

As the main lateral load resisting system in high-rise reinforced concrete structures, the mechanical performance of shear wall has a significant impact on the structure, especially for high-rise buildings. Steel corrosion has been recognized as an important factor affecting the mechanical performance and durability of the reinforced concrete structures. To investigate the effect on the seismic behaviour of corroded reinforced concrete shear wall induced by corrosion, analytical investigations and simulations were done to observe the effect of corrosion on the ultimate seismic capacity and drift capacity of shear walls. To ensure the accuracy of the simulation software, several validations were made using both non-corroded and corroded reinforced concrete shear walls based on some test results in previous literature. Thereafter, a parametric study, including 200 FE models, was done to study the influence of some critical parameters on corroded structural shear walls with boundary element. These parameters include corrosion levels, axial force ratio, aspect ratio, and concrete compressive strength. The results obtained would then be used to propose equations to predict the seismic resistance and drift capacity of shear walls with various corrosion levels.

• Steel and Composite Structures
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• 제46권2호
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• Steel and Composite Structures
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• 제45권2호
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• 콘크리트학회지
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• 제2권1호
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• pp.91-100
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• 1990

초기 균열을 갖는 대부분의 구조 부재의 있어서, 부재의 크기가 중가함에 따라 일반적으로 강도가 감소하는 현상을 보인다. 이를 크기효과라고 하며, 특히 콘크리트는 유리, 철과같은 구조 재료와는 달리 초기균열이 없는 경우에도 이러한 크기효과를 나타낸다는 것이 실험에 의해 나타나고 있다. 기존의 크기효과 법칙을 따르면 크기가 배우 큰 콘크리트 부재는 응력을 거의 받을 수 없는 것으로 나타나나, 실험에 의하면 강도의 감소율이 현저하게 감소되어 기존의 크기효과 법칙과 큰 차이를 보인다. 따라서, 본 논문에서는 콘크리트 구조물의 비선형 파괴역학에 근거하여 비유사 균열이 존재하는 경우에 대한 크기효과식을 유도하여 기존의 할열인장강도, 전단강도 및 압축강도 실험치에 대한 회귀분석을 통하여 보다 나은 모델식을 제시하였다.

• 한국항해항만학회지
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• 제37권2호
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• pp.155-163
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• 2013

이 연구에서는 전단하중을 받는 부유식 콘크리트 구조물 모듈 접합부의 구조거동 실험연구를 수행하였다. 모듈 접합부 전단키의 균열 양상, 전단거동 및 전단강도를 파악하였다. 전단강도의 영향을 파악하기 위해 전단키의 경사각도, 횡방향 구속응력 및 콘크리트의 압축강도 등을 실험변수로 고려하였다. 전단키의 경사각도가 증가함에 따라 접합부의 전단강도가 증가하였다. 또한, 구속응력이 증가함에 따라 전단키의 전단강도가 증가하였다. 실험변수에 따른 전단거동 실험결과를 토대로 접합부의 전단강도 평가식을 제안하였으며, 제안식에 의한 전단강도 예측값은 실험값에 근접하는 것으로 나타났다.