• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.43-51
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• 2018

As the inventory of aged apartments is expected to increase explosively, the importance of maintenance to improve the durability of concrete facilities is increasing. Concrete compressive strength is a representative index of durability of concrete facilities, and is an important item in the precision safety diagnosis for facility maintenance. However, existing methods for measuring the concrete compressive strength and determining the maintenance of concrete facilities have limitations such as facility safety problem, high cost problem, and low reliability problem. In this study, we proposed a model that can predict the concrete compressive strength through images by using deep convolution neural network technique. Learning, validation and testing were conducted by applying the concrete compressive strength dataset constructed through the concrete specimen which is produced in the laboratory environment. As a result, it was found that the concrete compressive strength could be learned by using the images, and the validity of the proposed model was confirmed.

• Computers and Concrete
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• v.23 no.2
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• pp.133-143
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• 2019

The paper presents the experimental investigations into the effect of ground granulated blast furnace slag (GGBFS) on the time-dependent tensile strength of concrete. The splitting and flexural tensile strength of concrete was determined at the ages of 3, 7, 28, 56, 90, 150 and 180 days using the cylindrical and prism specimens respectively for plain and GGBFS concrete. The amount of cement replacement by GGBFS was 0%, 40% and 60% on the weight basis. The maximum curing age was kept as 28 days. The results showed that the splitting and flexural tensile strength of concrete containing GGBFS has been found lower than the plain concrete at all ages and for all mixes. The tensile strength of 40 percent replacement has been found higher than the 60 percent at all ages and for all mixes. The rate of gain of splitting and flexural tensile strength of 40 percent GGBFS concrete is found higher than the plain concrete and 60 percent GGBFS concrete at the ages varying from 28 to 180 days. The experimental results of time-dependent tensile strength of concrete are compared with the available models. New models for the prediction of time-dependent splitting and flexural tensile strength of concrete containing GGBFS are proposed. The present experimental and analytical study will be helpful for the designers to know the time-dependent tensile properties of GGBFS concrete to meet the design requirements of liquid retaining reinforced and pre-stressed concrete structures.

• Earthquakes and Structures
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• v.11 no.4
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• pp.723-740
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• 2016

Reinforced concrete corbels are generally used to transfer loads within a structural system, such as buildings, bridges, and facilities in general. They commonly present low aspect ratio, requiring an accurate model for shear strength prediction in order to promote flexural behavior. The model described here, originally developed for walls, was adapted for corbels. The model is based on a reinforced concrete panel, described by constitutive laws for concrete and steel and applied in a fixed direction. Equilibrium in the orthogonal direction to the shearing force allows for the estimation of the shear stress versus strain response. The original model yielded conservative results with important scatter, thus various modifications were implemented in order to improve strength predictions: 1) recalibration of the strut (crack) direction, capturing the absence of transverse reinforcement and axial load in most corbels, 2) inclusion of main (boundary) reinforcement in the equilibrium equation, capturing its participation in the mechanism, and 3) decrease in aspect ratio by considering the width of the loading plate in the formulation. To analyze the behavior of the theoretical model, a database of 109 specimens available in the literature was collected. The model yielded an average model-to-test shear strength ratio of 0.98 and a coefficient of variation of 0.16, showing also that most test variables are well captured with the model, and providing better results than the original model. The model strength prediction is compared with other models in the literature, resulting in one of the most accurate estimates.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1075-1084
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• 2005

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Journal of the Korea Concrete Institute
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• v.11 no.4
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• pp.129-136
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• 1999

For the prediction of concrete-steel bond ability in reinforced concrete, many countries establish specifications for the pullout test. But these methods hardly to consider many parameters such as strength, shape, diameter and location of steel, concrete restrict condition by loading plate, strength of concrete and cover depth etc, and it is difficult to solve concentration and disturbance of stress. The purpose of this study is to propose a New Ring Test method which can be rational quantity evaluations of bond splitting mechanism. For this purpose, pullout test was carried out to assess the effect of several variables on bond splitting properties between reinforcing bar and concrete. Key variables are concrete compressive strength, concrete cover, bar diameter and rib spacing. Failure mode was examined and maximum bond stress-slip relationships were presented to show the effect of above variables. As the result, it appropriately expressed general characteristics of bond splitting mechanism, and it proved capability for standard test method.

• Proceedings of the Korea Concrete Institute Conference
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• 1994.10a
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• pp.77-82
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• 1994

The maturity concept proposes that concrete of the same mix at the same maturity has the same strength, whatever combination of temperature and time makes up that maturity. Maturity is the integral of time and temperature of concrete above a datum temperature. Tests are conducted in order to determine a datum temperature and to measure compressive strengths and maturity of test specimens. This study also proposes some appropriate functions to represent the relationship between maturity and strength development.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.279-282
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• 2005

Experimental study is performed to investigate the variation of shear strength of reinforced concrete beams according to design parameters. The major parameters are loading condition, shear span-to-depth ratio, ratio of tensile longitudinal reinforcement, prestress and boundary rigidity.14 reinforced concrete beams without web reinforcement are tested under monotonic downward loading. The shear strength of the tested specimens were compared with the prediction by design code and Choi's method.

• Computers and Concrete
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• v.29 no.3
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• pp.145-159
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• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.89-90
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• 2013

This study is to predict the compressive strength for the concrete of ground granulated blast-furnace slag, and use Plowman's, Gompertz's model. The results are as follows; The prediction compressive strength were simiar using Rastrup's equivalent age model. but The prediction compressive strength using Freiesleben's equivalent age model weren't simiar in bfs replacement Ratio of 50%, because it is analyzed as the activation energy.