• Magazine of the Korean Society of Agricultural Engineers
• /
• v.43 no.4
• /
• pp.97-105
• /
• 2001

The ultrasonic pulse velocity test has a strong potential to be developed into a very useful and relatively inexpensive in-place test for assuring the quality of concrete placed in structure. The main problem in realizing this potential is that the relationship between compressive strength ad ultrasonic pulse velocity is uncertain and concrete is an inherently variable material. The objective of this study is to improve the reliability of in-place concrete strength predictions by ultrasonic pulse velocity method. Experimental cement content, s/a rate, and curing condition of concrete. Accuracy of the prediction expressed in empirical formula are examined by multiple regression analysis and linear regression analysis and practical equation for estimation the concrete strength are proposed. Multiple regression model uses water-cement ratio cement content s/a rate, and pulse velocity as dependent variables and the compressive strength as an independent variable. Also linear regression model is used to only pulse velocity as dependent variables. Comparing the results of the analysis the proposed equation expressed highest reliability than other previous proposed equations.

• Computers and Concrete
• /
• v.32 no.6
• /
• pp.607-613
• /
• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.27 no.4
• /
• pp.305-312
• /
• 2014

The current American Concrete Institute(ACI), Canadian Standard Associate(CSA) and CEB-FIP Model Code 2010 provisions on the shear strength of a simply supported deep beam suggest that deep beams should be designed using the strut-and-tie model. Although this is a useful methodology to design members in disturbed regions, the quality of the design is highly dependent on the truss model that designers create. However, Hong et al. derived the shear strength equations of reinforced concrete deep beams. This thesis investigates the validity of the current ACI, CSA and CEB-FIP code provisions on the shear strength of simply supported reinforced concrete deep beams by comparing them with the shear strength equations proposed by Hong et al. The comparison shows that all of these code provisions provide reasonable estimates on the shear strength of concrete deep beam members and the selection of an internal truss model plays an important role on the estimation of shear strength.

• Computers and Concrete
• /
• v.19 no.4
• /
• pp.395-404
• /
• 2017

In this paper, the mechanical property of CFRP, BFRP, GFRP and their hybrid FRP was experimentally studied. The elastic modulus and tensile strength of CFRP, BFRP, GFRP and their hybrid FRP were tested. The experimental results showed that the elastic modulus of hybrid FRP agreed well with the theoretical rule of mixture, which means the property of hybrid composites are linear with the volumes of the corresponding components while the tensile strength did not. The bearing capacity, peak strain, stress-strain relationship of circular concrete columns confined by CFRP, BFRP, GFRP and hybrid FRP subjected to axial compression were recorded. And the confinement effect of hybrid FRP on concrete columns was analyzed. The test results showed that the bearing capacity and ductility of concrete columns were efficiently improved through hybrid FRP confinement. A strength model and a stress-strain relationship model of hybrid FRP confined concrete columns were proposed. The proposed stress-strain model was shown to be capable of providing accurate prediction of the axial compressive strength of hybrid FRP confined concrete compared with Teng et al. (2002) model, Karbhari and Gao (1997) model and Miyachi et al. (1999) model. The modified stress-strain model was also suitable for single FRP confinement cases and it was so concise in form and didn't have piecewise fitting, which would be easy for use in structural design.

• Structural Engineering and Mechanics
• /
• v.16 no.1
• /
• pp.83-99
• /
• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• Proceedings of the Korea Concrete Institute Conference
• /
• 1995.10a
• /
• pp.193-197
• /
• 1995

Computer-based methods have often been used in the structural analysis. But, regardless of the progress in the technique of structural analysis, there are inevitable limitations in consideration of the material and eometric nonlinearity and prediction of failure loads. Model analysis of concrete structure can supplement this kind of limitations to reasonably predict behavior of the structure. Similitude requirement in the reinforced concrete structure is often hard to be secured because of peculiar uncertainty of concrete. In this study, small scale model of subway box structure was constructed using strength model and results of model of subway box structure was constructed using strength model and results of model test and computer-based analysis were compared.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2021.11a
• /
• pp.30-31
• /
• 2021

This study aims to build a deep learning model that can predict the value of concrete mixing properties according to a given concrete strength value. A model was created for a total of 1,291 concrete data, including 8 characteristics related to concrete mixing elements and environment, and the compressive strength of concrete. As the deep learning model, DNN-3L-256N, which showed the best performance on the prior study, was used. The average value for each characteristic of the data set was used as the initial input value. In results, in the case of 'curing temperature', which had a narrow range of values in the existing data set, showed the lowest error rate with less than 1% error based on MAE. The highest error rate with an error of 12 to 14% for fly and bfs.

• Computers and Concrete
• /
• v.15 no.4
• /
• pp.607-642
• /
• 2015

The stress-strain relationship of concrete in flexure is one of the essential parameters in assessing the flexural strength and ductility of reinforced concrete (RC) columns. An overview of previous research studies revealed that the presence of strain gradient would affect the maximum concrete stress developed in flexure. However, no quantitative model was available to evaluate the strain gradient effect on concrete under flexure. Previously, the authors have conducted experimental studies to investigate the strain gradient effect on maximum concrete stress and respective strain and developed two strain-gradient-dependent factors k3 and ko for modifying the flexural concrete stress-strain curve. As a continued study, the authors herein will extend the investigation of strain gradient effects on flexural strength and ductility of RC columns to concrete strength up to 100 MPa by employing the strain-gradient-dependent concrete stress-strain curve using nonlinear moment-curvature analysis. It was evident from the results that both the flexural strength and ductility of RC columns are improved under strain gradient effect. Lastly, for practical engineering design purpose, a new equivalent rectangular concrete stress block incorporating the combined effects of strain gradient and concrete strength was proposed and validated. Design formulas and charts have also been presented for flexural strength and ductility of RC columns.

• Journal of the Korea Concrete Institute
• /
• v.13 no.5
• /
• pp.466-475
• /
• 2001

The potential shear strength of reinforced concrete beams decreases after flexural yielding due to the decrease of the effective compressive strength of concrete in plastic hinge zone. A truss model considering shear deterioration in the plastic hinge zone was proposed in order to evaluate the ductile capacity of reinforced concrete beams failing in shear after flexural yielding This model can determine the potential shear strength of the beam by using a truss model. The potential shear strength gradually decreases as the increase of the axial strain of member. When the calculated potential shear strength decreases up to the flexural yielding strength, the corresponding rotation angle is defined as the ductile capacity of the beam. The predicted ductile capacity of reinforced concrete beams is shown to be in a good agreement with experimental results.

• Steel and Composite Structures
• /
• v.39 no.2
• /
• pp.189-200
• /
• 2021

The compressive strength of circular concrete filled steel tubular (C-CFST) stubs strengthened with carbon fiber reinforced polymer (CFRP) is studied theoretically. According to previous experimental results, the failure process and mechanism of circular CFRP-concrete filled steel tubular (C-CFRP-CFST) stubs is analyzed, and the loading process is divided into 3 stages, i.e., elastic stage, elasto-plastic stage and failure stage. Based on continuum mechanics, the theoretical model of C-CFRP-CFST stubs under axial compression is established based on the assumptions that steel tube and concrete are both in three-dimensional stress state and CFRP is in uniaxial tensile stress state. Equations for calculating the yield strength and the ultimate strength of C-CFRP-CFST stubs are deduced. Theoretical predictions from the presented equations are compared with existing experimental results. There are a total of 49 tested specimens, including 15 ones for comparison of yield strength and 44 ones for comparison of ultimate strength. It is found that the predicted results of most specimens are within an error limit of 10%. Finally, simplified equations for calculating both yield strength and ultimate strength of C-CFRP-CFST stubs are proposed.