• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.2
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• pp.152-158
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• 2022

The purpose of this study is to investigate experimentally the compressive strength and tensile behavior of cement composites reinforced by selvedge short fiber from high performance fabric. Four types of mixtures according to the types of selvedge short fibers were prepared and compressive strength and tension tests were performed. Test results showed that the compressive strength values of composites investigated in this study ranged from 64 MPa to 66 MPa and all composites showed strain-hardening behavior. The tensile strain capacity values of composites ranged from 2.6 % to 2.8 % and multiple cracking behavior was observed in all composites.

• Steel and Composite Structures
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• v.47 no.5
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• pp.645-661
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• 2023

Concrete-filled steel tubular columns with double inner steel tubes (CFST-DIST) are a novel type of composite members developed from conventional concrete-filled steel tubular (CFST) columns. This paper investigates the structural performance of circular CFST-DIST stub columns using nonlinear finite element (FE) analysis. A numerical model was developed and verified against existing experimental test results. The validated model was then used to compare circular CFST-DIST stub columns' behavior with their concrete-filled double skin steel tubular (CFDST) and CFST counterparts. A parametric study was performed to ascertain the effects of geometric and material properties on the axial performance of CFST-DISTs. The FE results and the available test data were used to assess the accuracy of the European and American design regulations in predicting the axial compressive capacity of circular CFST-DIST stub columns. Finally, a new design model was recommended for estimating the compressive capacity of CFST-DISTs. Results clarified that circular CFST-DIST columns had the advantages of their CFST counterparts but with better ductility and strength-to-weight ratio. Besides, the investigated design codes led to conservative predictions of the compressive capacity of circular CFST-DIST columns.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.83-84
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• 2022

Zero-energy construction is being emphasized due to environmental pollution. However, in the case of external insulation using organic insulation with good insulation performance, there are many studies on inorganic insulation as it shows limitations on fire stability. In addition, as the demand for stone for exterior walls increases, Cellular Light -weight Concrete(CLC) with polymer is used to supplement fire stability and insulation performance, and the construction of stone is complemented by combining organic insulation, inorganic insulation, and stone. In this study, the compressive strength and adhesion in tension of CLC are studied. As a result of the experiment, the compressive strength of 28 days according to the polymer addition rate did not change. The adhesion in tension according to the polymer addition rate tends to increase as the addition rate increases. The target adhesion in tension is 0.8 MPa, but the maximum value of the experiment did not reach the target value, and further research was needed to combine to maintain the density and improve the adhesion in tension.

• Steel and Composite Structures
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• v.44 no.6
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• pp.867-882
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• 2022

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.177-178
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• 2023

In this study, the mass production process was simulated using a 1m³ batcher plant to evaluate the application of high-strength fire resistance concrete. The strength ranges of concrete were set to 50, 60, 70, and 80 MPa, and each concrete mix proportions was selected through preliminary experiments in the laboratory. For the selected concrete mix proportions, after the mixer load value was stabilized in the batcher plant, the slump flow and air content of the fresh concrete were evaluated, and the compressive strength was evaluated up to 56 days. As a result of the experiment, both the slump flow and air content of the fresh concrete satisfied the target performance, and in the case of compressive strength, 50 and 60 MPa satisfied the target performance at 28 days and 70 and 80 MPa at 56 days.

• Geomechanics and Engineering
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• v.36 no.4
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• pp.381-390
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• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• Computers and Concrete
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• v.21 no.4
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.122-130
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• 1997

An experimental study was conducted to evaluate the bond performance of reinforcing bars embedded in high-strength concrete. Four bond specimens and ten beam splice specimens using concrete with compressive strength of 246kgf/$cm^2$ and 64lkgf/$cm^2$ were tested. The effect of several variables on basic development length and compressive strength of concrete is discussed in splice specimens. The test results showed that the current trend in concrete specification of making the splice length longer to compensate for having smaller cover and spacing may not be an effective approach.

• Journal of the Korea Concrete Institute
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• v.14 no.2
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• pp.249-256
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• 2002

High performance concrete is prone to large autogenous shrinkage due to its low water to binder ratio (W/B). The autogenous shrinkage of concrete is caused by self-desiccation as a result of water consumption by the hydration of cement. In this study, the autogenous shrinkage of high performance concrete with and without fly ash was Investigated. The properties of fresh concrete, slump loss, air content, and flowability as well as the mechanical properties, compressive strength and modulus of elasticity, were also measured. Test results was shown that the autogenous shrinkage of concrete increased as the W/B decreased. For the same W/B, the autogenous shrinkage of high strength concrete with fly ash was considerably reduced although the development of its compressive strength was delayed at early ages. Furthermore, the autogenous shrinkage and compressive strength of high strength concrete were more rapidly developed than those of normal strength concrete. It was concluded that fly ash could improve the quality of high strength concrete with respect to the workability and autogenous shrinkage.

• International Journal of Highway Engineering
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• v.18 no.1
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• pp.43-55
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• 2016

PURPOSES : This study aims to develop a repair material that can enhance pavement performance, inducing rapid traffic opening through early strength development and fast setting time by utilizing MgO-based patching materials for repairing road pavements. METHODS : To consider the applicability of MgO-based patching materials for repairing domestic road pavements, first, strength development and setting time of the materials were evaluated, based on MgO to $KH_2PO_4$ ratio, water to binder ratio, and addition ratio of retarder (Borax), by which the optimal mixture ratio of the developed material was obtained. To validate the performance of the developed material as a repair material, the strength(compressive strength and bonding strength) and durability (freezing, thawing, and chloride ion penetration resistance) was checked through testing, and its applicability was evaluated. RESULTS : The results showed that when an MgO-based patching material was used, the condensation time was reduced by 80%, and the compressive strength was enhanced by approximately 300%, as compared to existing cement-based repair materials. In addition, it was observed that the strength (compressive strength and bonding strength) and durability (freezing and thawing, and chloride ion penetration resistance) showed an excellent performance that satisfied the regulations. CONCLUSIONS : The results imply that an emergent repair/restoration could be covered by a rapid-hardening cement to meet the traffic limitation (i.e. the traffic restriction is only several hours for repair treatment). Furthermore, MgO-based patching materials can improve bonding strength and durability compared to existing repair materials.