• Computers and Concrete
• /
• v.20 no.6
• /
• pp.645-654
• /
• 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
• /
• v.17 no.6
• /
• pp.739-760
• /
• 2016

This paper reports on punching shear behavior of reinforced concrete panels, investigated experimentally and through finite element simulation. The aim of the study was to examine the punching shear of high strength concrete panels incorporating different types of aggregate and silica fume, in order to assess the validity of the existing code models with respect to the role of compressive and tensile strength of high strength concrete. The variables in concrete mix design include three types of coarse aggregates and three water-cementitious ratios, and ten-percent replacement of silica fume. The experimental results were compared with the results produced by empirical prediction equations of a number of widely used codes of practice. The prediction of the punching shear capacity of high strength concrete using the equations listed in this study, pointed to a potential unsafe design in some of them. This may be a reflection of the overestimation of the contribution of compressive strength and the negligence of the role of flexural reinforcement. The overall findings clearly indicated that the extrapolation of the relationships that were developed for normal strength concrete are not valid for high strength concrete within the scope of this study and that finite element simulation can provide a better alternative to empirical code Equations.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2020.06a
• /
• pp.25-26
• /
• 2020

In this study, by applying the concrete compressive strength estimation system Concrete IoT Management System (hereinafter referred to as CIMS) to the concrete slab concrete in the domestic field, the purpose of this study is to confirm the practical use of CIMS and to verify the accuracy of estimating the initial strength of concrete. As a result, it shows a high correlation when the compressive strength and CIMS estimated strength of the specimen for structural management are converted and compared with the integrated temperature. However, in order to determine a more accurate experimental constant, it is necessary to consider the results up to 28 days.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.12
• /
• pp.7-13
• /
• 2018

The strength of concrete is affected significantly not only by the internal influence factors of cement, water, sand, aggregate, and admixture, but also by the external influence factors of concrete placement delay and curing temperature. The objective of this research was to predict the concrete strength considering both the internal and external influence factors when concrete is placed at the construction site. In this study, a concrete strength test was conducted on the 24 combinations of internal and external influence factors, and a neural network model was constructed using the test data. This neural network model can predict the concrete strength considering the external influence factors of the concrete placement delay and curing temperature when concrete is placed at the construction site. Contractors can use the concrete strength prediction neural network model to make concrete more robust to external influence factors during concrete placement at a construction site.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.19 no.3
• /
• pp.122-129
• /
• 2015

In this study, a prediction model for the effective compressive strength of corner columns with intervening normal strength concrete slabs was developed. A structural analogy between high-strength concrete column-normal strength concrete slab joint and brick masonry was used to develop the prediction model. In addition, the aspect ratio of slab thickness to column dimension was considered in the models. The reliability of the new prediction model was evaluated by comparison with experimental results and its superiority was demonstrated by comparison with previous models proposed by design codes and other researchers. As a result, with average test-to-predicted ratios of 1.09, a standard deviation of 0.15, the newly developed equation provided superior predictions in terms of accuracy and consistency over all of the existing effective strength prediction approaches including KCI structural concrete design code (2012).

• Journal of the Korea Concrete Institute
• /
• v.19 no.1
• /
• pp.75-81
• /
• 2007

Percentage that aggregate of materials that concrete composed about $70{\sim}80%$ of whole volume, therefore influence that quality of aggregate gets in concrete characteristics are very important. Schmidt hammer and ultra-sonic velocity method are commonly used for crushed sand concrete compressive strength test in a construction field. At present, various equations for prediction of strength are present, which have been used in a construction field. The purpose of this study is to evaluate the correlation between prediction strength by present equations and destructive strength to test specimen, and find out which is a suitable equation for the construction site, a strength test was carried out destructive test by means of core sampling and traditional test. The experimental parameters were concrete age, curing condition, and strength level. It is demonstrated that the correlation behavior of crushed sand concrete strength in this study good due to the perform analysis of correlation between core, destructive strength and non-destructive strength.

• Computers and Concrete
• /
• v.12 no.6
• /
• pp.831-850
• /
• 2013

The high strength materials have been more widely used in reinforced concrete structures because of the benefits of the mechanical and durable properties. Generally, it is known that the ductility decreases with an increase in the strength of the materials. In the design of a reinforced concrete beam, both the flexural strength and ductility need to be considered. Especially, when a reinforced concrete structure may be subjected an earthquake, the members need to have a sufficient ductility. So, each design code has specified to provide a consistent level of minimum flexural ductility in seismic design of concrete structures. Therefore, it is necessary to assess accurately the ductility of the beam sections with high strength materials in order to ensure the ductility requirement in design. In this study, the effects of concrete strength, yield strength of reinforcement steel and amount of reinforcement including compression reinforcement on the complete moment-curvature behavior and the curvature ductility factor of doubly reinforcement concrete beam sections have been evaluated and a newly prediction formula for curvature ductility factor of doubly RC beam sections has been developed considering the stress of compression reinforcement at ultimate state. Based on the numerical analysis results, the proposed predictions for the curvature ductility factor are verified by comparisons with other prediction formulas. The proposed formula offers fairly accurate and consistent predictions for curvature ductility factor of doubly reinforced concrete beam sections.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2002.05a
• /
• pp.997-1002
• /
• 2002

Traditional prediction models have been developed with a fixed equation form based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network (ANN) does not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this paper is to develop the I-PreConS (Intelligent system for PREdiction of CONcrete Strength using ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction.

• Computers and Concrete
• /
• v.4 no.4
• /
• pp.299-316
• /
• 2007

The compressive strength of concrete is a commonly used criterion in producing concrete. However, the test on the compressive strength is complicated and time-consuming. More importantly, since the test is usually performed 28 days after the placement of the concrete at the construction site, it is too late to make improvements if unsatisfactory test results are incurred. Therefore, an accurate and practical strength estimation method that can be used before the placement of concrete is highly desirable. In this study, the estimation of the concrete strength is performed using support vector regression (SVR) based on the mix proportion data from two ready-mixed concrete companies. The estimation performance of the SVR is then compared with that of neural network (NN). The SVR method has been found to be very efficient in estimation accuracy as well as computation time, and very practical in terms of training rather than the explicit regression analyses and the NN techniques.

• Smart Structures and Systems
• /
• v.16 no.3
• /
• pp.497-519
• /
• 2015

This study discusses the use of Adaptive-Network-Based-Fuzzy-Inference-System (ANFIS) in predicting the shear strength of reinforced-concrete deep beams. 139 experimental data have been collected from renowned publications on simply supported high strength concrete deep beams. The results show that the ANFIS has strong potential as a feasible tool for predicting the shear strength of deep beams within the range of the considered input parameters. ANFIS's results are highly accurate, precise and therefore, more satisfactory. Based on the Sensitivity analysis, the shear span to depth ratio (a/d) and concrete cylinder strength ($f_c^{\prime}$) have major influence on the shear strength prediction of deep beams. The parametric study confirms the increase in shear strength of deep beams with an equal increase in the concrete strength and decrease in the shear span to-depth-ratio.