• Journal of the Korean Wood Science and Technology
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• v.32 no.4
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• pp.66-73
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• 2004

Three species of Italian poplar (Populus euramericana), red pine (Pinus densiflora) and oriental oak (Quercus variabilis) were selected for this study. They were cut so that the distances between each of tips and roots for a pair of fingers were 0, 0.15, 0.30 and 0.45 mm. Poly vinyl acetate (PVAc) and resorcinol-phenol resin (RPR) were used for finger-jointing. Compressive test parallel to the grain was conducted for the finger-jointed specimens. The results were as follows: The efficiency of compressive Young's modulus of finger-jointed timber to solid wood indicated low values, whereas the efficiency of compressive strength indicated high values of more than 90% in all species, especially, it was found that those of red pine indicated markedly high values of more than 97%. The efficiency of compressive displacement of Italian poplar finger-jointed timber was 2 times higher than solid wood, and it was 1.2 and 1.3 times higher than solid woods in red pine and oriental oak, respectively. Also, it was found that 0, the distance between each tip and root for the fingers, indicated the highest efficiency of compressive strength performance in Italian poplar finger-jointed timber, and for red pine and oriental oak finger-jointed timbers, the distances of 0.15 and 0.30 were found to indicate the highest efficiency.

• Journal of the Society of Disaster Information
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• v.7 no.1
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• pp.21-31
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• 2011

The objectives of this study is replaced Silicafume with Metakaolin that is used to lightweight concrete to better performance. So, this study made high-performance lightweight concrete using Metakaolin and characteristics of the fundamental properties and chloride ion diffusion. Consequently, it is compressive strength and chloride ion penetration resistance is lower than lightweight concrete using Silicafume, the performance of compressive strength contrast Silicafume is about 88 to 95%. Also, this study got a content result because the chloride ion penetration resistance showed the performance in around 80 to 90%. As a result, this study insist that replacement ratio of Metakaolin is suitable for 10 to 15%.Silicafume and Metakaolin have similar characteristics. In addition, it is similar to the performance of alternative materials is possible.

• Computers and Concrete
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• v.22 no.4
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• Computers and Concrete
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• v.8 no.5
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• pp.597-616
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• 2011

With respect to rehabilitation, strengthening and retrofitting of existing and deteriorated columns in buildings and bridges, CFRP sheets have been found effective in enhancing the performance of existing RC columns by wrapping and bonding CFRP sheets externally around the concrete. Concrete columns and piers that are confined by both lateral steel reinforcement and CFRP are sometimes referred to as "hybrid" concrete columns. With the availability of experimental data on concrete columns confined by steel reinforcement and/or CFRP, the study presents modeling using artificial neural networks (ANNs) to predict the compressive strength of hybrid circular RC columns. The prediction of the ultimate confined compressive strength of RC columns is very important especially when this value is used in estimating the capacity of structures. The present ANN model used as parameters for the confining materials the lateral steel ratio (${\rho}_s$) and the FRP volumetric ratio (${\rho}_{FRP}$). The model gave good predictions for three types of confined columns: (a) columns confined with steel reinforcement only, (b) CFRP confined columns, and (c) hybrid columns confined by both steel and CFRP. The model may be used for predicting the compressive strength of existing circular RC columns confined with steel only that will be strengthened or retrofitted using CFRP.

• Computers and Concrete
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• v.7 no.3
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• pp.271-283
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• 2010

Testing of ultrasonic pulse velocity (UPV) is one of the most popular and actual non-destructive techniques used in the estimation of the concrete properties in structures. In this paper, artificial neural network (ANN) approach has been proposed for the evaluation of relationship between concrete compressive strength, UPV, and density values by using the experimental data obtained from many cores taken from different reinforced concrete structures with different ages and unknown ratios of concrete mixtures. The presented approach enables to find practically concrete strengths in the reinforced concrete structures, whose records of concrete mixture ratios are not yet available. Thus, researchers can easily evaluate the compressive strength of concrete specimens by using UPV values. The method can be used in conditions including too many numbers of the structures and examinations to be done in restricted time duration. This method also contributes to a remarkable reduction of the computational time without any significant loss of accuracy. Statistic measures are used to evaluate the performance of the models. The comparison of the results clearly shows that the ANN approach can be used effectively to predict the compressive strength of concrete by using UPV and density data. In addition, the model architecture can be used as a non-destructive procedure for health monitoring of structural elements.

• Journal of the Korean Recycled Construction Resources Institute
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• v.8 no.2
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• pp.183-189
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• 2020

The purpose of this study is to investigate the compressive and tensile properties of high volume slag cement-based fiber-reinforced composite incorporating phase change material. Four mixtures were determined according to calcium hydroxide and expansive admixture, and the compressive strength and tension tests were performed. Test results showed that four mixtures showed a compressive strength over 51MPa and a tensile ductility over 3.2%. It was observed that calcium hydroxide and expansive admixture influenced the compressive and tensile performance, and the strength, ductility, and cracking patterns of composite could be improved by including proper amount of calcium hydroxide and expansive admixture.

• Advances in concrete construction
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• v.5 no.6
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• pp.563-573
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• 2017

Transparency, excellent toughness, thermal stability and a very good dimensional stability make Polycarbonate (PC) one of the most widely used engineering thermoplastics. Polycarbonate market include electronics, automotive, construction, optical media and packaging. One alternative for reducing the environmental pollution caused by polycarbonate from electronic waste (e-waste), is to use it in cement concretes. In this work, physical and chemical characterization of recycled polycarbonate from electronic waste was made, through the analysis by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), energy dispersive spectroscopy (EDS) and scanning electron microscope (SEM). Then cement concrete was made with Portland cement, sand, gravel, water, and this recycled polycarbonate. Specimens without polycarbonate were produced for comparison purposes. The effect of the particle sizes and concentrations of recycled polycarbonate within the concrete, on the compressive strength and density was studied. Results show that compressive strength values and equilibrium density of concrete depend on the polycarbonate particle sizes and its concentrations; particularly the highest compressive strength values were 20% higher than that for concrete without polycarbonate particles. Moreover, morphological, structural and crystallinity characteristics of recycled polycarbonate, are suitable for to be mixed into concrete.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.2
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• pp.127-136
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• 2020

Prism compressive strength is the most influential parameter to evaluate the seismic performance of non-structural concrete brick masonry walls, and is affected by the practice and workmanship of masonry workers. This study experimentally investigates the influence of workmanship on prism compressive strength throughout the compressive test with prism specimens constructed according to masonry workmanship. To do this, the workmanship is categorized into good, fair, and poor conditions which are statistically evaluated with thickness and indentation depth of bed-joints. Then, the effect of workmanship on the structural properties of masonry prisms is evaluated by investigating relations between properties such as their compressive strength, elastic modulus and numerical parameters such as thickness, filling of bed-joints. This study demonstrates that the indentation depth is more important parameter for structural properties of masonry prisms and masonry prisms with loss in bed-joint area less than of 7% can be in fair condition.

• Advances in materials Research
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• v.11 no.1
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• pp.75-90
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• 2022

In this paper, functionality of soft computing algorithms such as Group method of data handling (GMDH), Random forest (RF), Random tree (RT), Linear regression (LR), M5P, and artificial neural network (ANN) have been looked out to predict the compressive strength of concrete mixed with marble powder. Assessment of result suggests that, the overall performance of ANN based model gives preferable results over the different applied algorithms for the estimate of compressive strength of concrete. The results of coefficient of correlation were maximum in ANN model (0.9139) accompanied through RT with coefficient of correlation (CC) value 0.8241 and minimum root mean square error (RMSE) value of ANN (4.5611) followed by RT with RMSE (5.4246). Similarly, other evaluating parameters like, Willmott's index and Nash-sutcliffe coefficient value of ANN was 0.9458 and 0.7502 followed by RT model (0.8763 and 0.6628). The end result showed that, for both subsets i.e., training and testing subset, ANN has the potential to estimate the compressive strength of concrete. Also, the results of sensitivity suggest that the water-cement ratio has a massive impact in estimating the compressive strength of concrete with marble powder with ANN based model in evaluation with the different parameters for this data set.

• Computers and Concrete
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• v.32 no.3
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.