• Computers and Concrete
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• 제24권4호
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• pp.295-302
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• 2019

Evolutionary algorithms based on conventional statistical methods such as regression and classification have been widely used in data mining applications. This work involves application of gene expression programming (GEP) for predicting compressive strength of fly ash geopolymer concrete, which is gaining increasing interest as an environmentally friendly alternative of Portland cement concrete. Based on 56 test results from the existing literature, a model was obtained relating the compressive strength of fly ash geopolymer concrete with the significantly influencing mix design parameters. The predictions of the model in training and validation were evaluated. The coefficient of determination ($R^2$), mean (${\mu}$) and standard deviation (${\sigma}$) were 0.89, 1.0 and 0.12 respectively, for the training set, and 0.89, 0.99 and 0.13 respectively, for the validation set. The error of prediction by the model was also evaluated and found to be very low. This indicates that the predictions of GEP model are in close agreement with the experimental results suggesting this as a promising method for compressive strength prediction of fly ash geopolymer concrete.

• Computers and Concrete
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• 제20권6호
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• pp.645-654
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• 2017

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.

• Computers and Concrete
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• 제8권1호
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• pp.59-70
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• 2011

A theoretical model known as the modified rotating-angle softened-truss model (MRA-STM), which is a modification of Rotating-Angle Softened-Truss Model and Modified Compression Field Theory, is presented for the analysis of reinforced concrete membranes in shear. As an application, shear strength and behaviour of reinforced concrete exterior beam-column joints are analysed using the MRA-STM combining with the deep beam analogy. The joints are considered as RC panels and subjected to vertical and horizontal shear stresses from adjacent columns and beams. The strut and truss actions in a beam-column joint are represented by the effective transverse compression stresses and a softened concrete truss in the proposed model. The theoretical predictions of shear strength of reinforced concrete exterior beam-column joints from the proposed model show good agreement with the experimental results.

• Computers and Concrete
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• 제5권1호
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• pp.21-36
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• 2008

Steel fiber reinforced concrete is increasingly used day by day in various structural applications. An extensive experimentation was carried out with w/cm ratio ranging from 0.25 to 0.40, and fiber content ranging from zero to1.5 percent by volume with an aspect ratio of 80 and silica fume replacement at 5%, 10% and 15%. The influence of steel fiber content in terms of fiber reinforcing index on the compressive strength of high-performance fiber reinforced concrete (HPFRC) with strength ranging from 45 85 MPa is presented. Based on the test results, equations are proposed using statistical methods to predict 28-day strength of HPFRC effecting the fiber addition in terms of fiber reinforcing index. A strength model proposed by modifying the mix design procedure, can utilize the optimum water content and efficiency factor of pozzolan. To examine the validity of the proposed strength model, the experimental results were compared with the values predicted by the model and the absolute variation obtained was within 5 percent.

• Earthquakes and Structures
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• 제7권4호
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• pp.527-552
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• 2014

The development of modern concrete technology makes it much easier to produce high-strength concrete (HSC) or ultra-high-strength concrete (UHSC) with high workability. However, the application of this concrete is limited in practical construction of traditional reinforced concrete (RC) structures due to low-ductility performance. To further push up the limit of the design concrete strength, concrete-filled-steel-tube (CFST) columns have been recommended considering its superior strength and ductility performance. However, the beneficial composite action cannot be fully developed at early elastic stage as steel dilates more than concrete and thereby reducing the elastic strength and stiffness of the CFST columns. To resolve this problem, external confinement in the form of steel rings is proposed in this study to restrict the lateral dilation of concrete and steel. In this paper, a total of 29 high-strength CFST (HSCFST) columns of various dimensions cast with concrete strength of 75 to 120 MPa concrete and installed with external steel rings were tested under uni-axial compression. From the results, it can be concluded that the proposed ring installation can further improve both strength and ductility of HSCFST columns by restricting the column dilation. Lastly, an analytical model calculating the uni-axial strength of ring-confined HSCFST columns is proposed and verified based on the Von-Mises and Mohr-Coulomb failure criteria for steel tube and in-filled concrete, respectively.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 봄 학술발표회 논문집
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• pp.947-952
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• 2001

The prediction model is proposed to estimate the variation of compressive strength of fly ash concrete with aging. After analyzing the experimental result with the model, the regression results are presented according to fly ash replacement content and water/cement ratio. Based on the regression results, the influence of fly ash replacement content and water/cement ratio on apparent activation energy was investigated. According to the analysis, the model provides a good estimate of compressive strength development of fly ash concrete with aging. As the fly ash replacement content increases, the limiting relative compressive strength and initial apparent activation energy become greater. The concrete with water/cement ratio smaller than 0.40 shows that the limiting relative compressive strength and apparent activation energy are nearly constant according to water/cement ratio. But, the concrete with water/cement ratio greater than 0.40 has the increasing limiting relative compressive strength and apparent activation energy with increasing water/cement ratio.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 봄 학술발표회 논문집
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• pp.923-928
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• 2002

Experimental and analytical study were conducted to develop the confined model of reinforced high strength concrete tied columns subjected to monotonically increasing concentric axial compression. Twenty-one large-scale columns(260$\times$260$\times$1200mm) used high strength concrete of 50 and 85MPa were fabricated to simulate an actual structural members size. Test results indicated that gains of strength and ductility of high strength concrete columns could be increased, if efficient arrangements and volumetric ratios of transverse reinforcements were provided. The proposed model satisfactorily predicted the experimental stress-strain curves for high strength concrete up to 100MPa.

• Computers and Concrete
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• 제23권4호
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

• Computers and Concrete
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• 제32권3호
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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.

• Computers and Concrete
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• 제19권5호
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.