• Computers and Concrete
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• 제16권3호
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• pp.399-414
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• 2015

Nowadays, fiber reinforced polymer (FRP) composites are widely used for rehabilitation, repair and strengthening of reinforced concrete (RC) structures. Also, recent advances in concrete technology have led to the production of high strength concrete, HSC. Such concrete due to its very high compression strength is less ductile; so in seismic areas, ductility is an important factor in design of HSC members (especially FRP strengthened members) under flexure. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple regression analysis are used to predict the curvature ductility factor of FRP strengthened reinforced HSC (RHSC) beams. Also, the effects of concrete strength, steel reinforcement ratio and externally reinforcement (FRP) stiffness on the complete moment-curvature behavior and the curvature ductility factor of the FRP strengthened RHSC beams are evaluated using the analytical approach. Results indicate that the predictions of ANFIS and multiple regression models for the curvature ductility factor are accurate to within -0.22% and 1.87% error for practical applications respectively. Finally, the effects of height to wide ratio (h/b) of the cross section on the proposed models are investigated.

• Computers and Concrete
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• 제31권2호
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• pp.85-95
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• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.

• 콘크리트학회논문집
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• 제22권1호
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• pp.85-91
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• 2010

이 연구에서는 24~60 MPa 범위의 콘크리트에 대하여 다양한 양생조건과 측정 방법을 적용하여 재령에 따른 조건별 건조수축량을 비교하였고, 이를 통해 실험 방법의 적합성 및 예측 방법의 적용성을 검토하였다. 연구 결과에 따르면, 28일 양생이 가장 적은 건조수축을 나타냈으며, 저강도 콘크리트 봉합양생의 경우 탈형 후부터의 변형률을 비교 해보면, 표준양생의 건조수축과 크게 변하지 않는 것으로 나타났으나, 고강도 콘크리트 봉합양생의 경우 자기수축이 크게 발생하여 더 큰 건조수축을 나타냈다. 매립 게이지를 사용해도 효율적인 건조수축량 측정이 가능하며, 접지(contact) 게이지로 측정된 값이 매립 게이지로 측정한 값보다 작게 나타났다. 실험 결과는 EC2 모델예측식과 가장 잘 일치하는 것으로 나타났다.

• Computers and Concrete
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• 제33권1호
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• Steel and Composite Structures
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• 제34권5호
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Advances in nano research
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• 제11권6호
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• pp.595-606
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• 2021

This paper conducted mainly for forecasting the behavior of the shear connectors in steel-concrete composite beams based on the different factors. The main goal was to analyze the influence of variable parameters on the shear strength of C-shaped and L-shaped angle shear connectors. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the mentioned shear strength forecasting. Five inputs are considered: height, length, thickness of shear connectors together with concrete strength and respective slip of the shear connectors after testing. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the shear strength of C-shaped and L-shaped angle shear connectors. The results show that the forecasting methodology developed in this research is useful for enhancing the multiple performances characterizing in the shear strength prediction of C and L shaped angle shear connectors analyzing.

• 콘크리트학회논문집
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• 제15권3호
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• pp.417-424
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• 2003

철근콘크리트 기둥-철골보(RCS)합성골조 접합부에서 지압 거동을 평가하고 지압설계를 위하여 단순화한 국부지압 실험을 수행하였다. 편심하중에 의해 국부지압을 받을 때 콘크리트의 쐐기작용과 띠철근의 횡구속 효과에 의해 저항된다. 실험결과 U형 지압보강근은 지압내력을 철근 강도만큼 증가시키면서 상세와 설치가 단순하여 효과적으로 나타났다. 띠철근 양에 따라 지압강도가 증가하며 이중 띠철근이 더욱 효과적으로 약20% 증가된다. 현행 ASCE설계지침에서 띠철근 규정은 지압내력이 2 $f_{ck}$ 가 되기 위해서는 다소 미흡하며, 지압강도는 띠철근과 이중 띠철근을 증가시킴에 따라 최대한 2.5 $f_{ck}$ 까지 사용할 수 있다. 본 연구의 결과로서 RCS 보기둥 접합부의 지압내력 추정을 위한 예측식이 제안되었으며 실험결과와 비교적 잘 일치되고 있다.

• 콘크리트학회논문집
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• 제18권1호
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• pp.101-108
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• 2006

초고층건물에서 발생하는 부등축소량은 기둥과 코어를 연결하는 보와 슬래브에서의 부가응력을 유발하거나 파티션과 커튼월의 균열과 같은 문제 등을 유발하므로, 부등축소량의 영향을 최소화하기 위해 기둥축소량의 예측 및 보정이 정확히 이루어져야 하며, 구조안전성과 사용성의 관점에서 시간변화에 따른 초고층건물 기둥축소량의 정확한 예측이 필요하다. 기둥축소량에 영향을 주는 콘크리트의 재료물성치 중 콘크리트강도, 크리프계수, 건조수축계수 등의 변동성을 고려하여 확률론적 해석을 이용한 기둥축소량 예측을 하여야 한다. 본 논문에서는 41층 초고층 내력벽식 구조물을 예제로 하여 몬테카를로 기법을 이용한 확률론적 축소량을 구하고 축소량의 분포도를 조사하여 신뢰수준별 기둥축소량을 분석하였다. 초고층 내력벽식 구조물예제에서 현장계측된 변형값은 해석에 의한 결과값보다 전체적으로 작으며, 확률론적으로 신뢰구간 ${\mu}-1.645{\sigma}$(신뢰수준 90.0% 하한치)이내의 값을 나타내었다.

• Computers and Concrete
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• 제6권3호
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• pp.253-268
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• 2009

This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.

• Computers and Concrete
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• 제15권5호
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• pp.759-770
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• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.