• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.275-280
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• 2001

Recycling of coal combustion by-product(Ash) are becoming more improtant in the utilization business as a result of the increased use of NOx reduction technologies at coal-fired power plants. current disposal methods of these by-products create not only a loss of profit for the power industry, but also environmental concerns that breed negative public opinion. Since inherent characteristics make these by-product suitable for building materials, several types of artificial aggregates and construction bricks are manufactured and tested to verify the engineering properties.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.221-229
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• 2023

Urbanization and industrialization have significantly increased the amount of solid waste produced in recent decades, posing considerable disposal problems and environmental burdens. The practice of waste utilization in concrete has gained popularity among construction practitioners and researchers for the efficient use of resources and the transition to the circular economy in construction. This study employed Lytag aggregate, an environmentally friendly pulverized fuel ash-based lightweight aggregate, as a substitute for natural coarse aggregate. At the same time, fly ash, an industrial by-product, was used as a partial substitute for cement. Concrete mix M20 was experimented with using fly ash and Lytag lightweight aggregate. The percentages of fly ash that make up the replacements were 5%, 10%, 15%, 20%, and 25%. The Compressive Strength (CS), Split Tensile Strength (STS), and deflection were discovered at these percentages after 56 days of testing. The concrete cube, cylinder, and beam specimens were examined in the explorations, as mentioned earlier. The results indicate that a 10% substitution of cement with fly ash and a replacement of coarse aggregate with Lytag lightweight aggregate produced concrete that performed well in terms of mechanical properties and deflection. The cementitious composites have varying characteristics as the environment changes. Therefore, understanding their mechanical properties are crucial for safety reasons. CS, STS, and deflection are the essential property of concrete. Machine learning (ML) approaches have been necessary to predict the CS of concrete. The Artificial Fish Swarm Optimization (AFSO), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms were investigated for the prediction of outcomes. This work deftly explains the tremendous AFSO technique, which achieves the precise ideal values of the weights in the model to crown the mathematical modeling technique. This has been proved by the minimum, maximum, and sample median, and the first and third quartiles were used as the basis for a boxplot through the standardized method of showing the dataset. It graphically displays the quantitative value distribution of a field. The correlation matrix and confidence interval were represented graphically using the corrupt method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.257-264
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• 2002

This study presents a life-cycle cost (LCC) effectiveness of a concrete with lightweight aggregate. A number of researchers have made their efforts to develop a lightweight concrete, since it is difficult to apply conventional concrete using general aggregate to heavy self-weight structures such as long span bridges. In this study, an optimum design for minimizing the life-cycle cost of concrete slab bridges is performed to evaluate the life cycle cost effectiveness of the lightweight concrete relative to conventional one from the standpoint of the value engineering. The data of physical properties for new concrete can be obtained from basic experimental researches. The material properties of conventional one are acquired by various reports. This study presents a LCC effectiveness of newly developed concrete, which is made by artificial lightweight aggregate. A number of researchers have made their efforts to develop a lightweight concrete, since it is difficult to apply conventional concrete using general aggregate to heavy self-weight structures such as long span bridges. From the results of the numerical investigation, it may be positively stated that the new concrete lead to, the longer span length, the more economical slab bridges compared with structures using general concrete.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.171-172
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• 2015

Recently, it is the global warming phenomenon because of the greenhouse gas exhaustion caused by and the environment problem is serious. And it is the situation where the problem of the exhaustion of resource because of the indiscriminate picking of the that is the raw material of the cement, limestone and natural aggregate are emphasized. In addition, thus the cement reduction amount of use and substitute material research is the urgent actual condition with the gas emission, which here it is generated in conducting compression molding in the building stone manufacturing process performance degradation phenomenon and fire resistance, and problem of the durability. Therefore, in this research, the waste porcelain is applied to the artificial stone and the durability property of the artificial stone according to it tries to be investigated.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.3
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• pp.49-55
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• 2012

In this paper, the physical properties of lightweight aggregate such as density and water absorption according to addition ratio and type of flux were investigated. When using $Na_2CO_3$ as flux of lightweight aggregate, burnability was available at low burning temperature and water absorption increased. And as increasing addition ratio of $CaCO_3$, NaOH, $Fe_2O_3$, absorption decreased and $CaCO_3$, NaOH, $Fe_2O_3$ were considered improper to use flux of lightweight aggregate because of high dried density. $Na_2SO_4$ was proper to use flux of lightweight aggregate due to dried density $1.35{\sim}1.50g/cm^3$ and lower absorption. When using glass abrasive sludge as flux of lightweight aggregate, dried density and water absorption were in the range of $1.45{\sim}1.55g/cm^3$ and 9~12% respectively. It was indicated that as increasing addition ratio of blast furnace slag powder, density increased whereas absorption decreased. In use of oxidizing slag as flux, artificial lightweight aggregate which have dried density $1.46g/cm^3$, water absorption 8,5 % can be manufactured at 10 % of addition ratio.

• Journal of the Korean Recycled Construction Resources Institute
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• v.8 no.4
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• pp.498-505
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• 2020

Aggregate occupies about 70-85% of the concrete volume and is an important factor in reducing the drying shrinkage of concrete. However, when constructing high-rise buildings, it acts as a problem due to the high load of natural aggregates. If the load becomes large during the construction of a high-rise building, creep may occur and the ground may be eroded. Material costs increase and there are financial problems. In order to reduce the load on concrete, we are working to reduce the weight of aggregates. However, artificial lightweight aggregates affect the interface between the aggregate and the paste due to its higher absorption rate and lower adhesion strength than natural aggregates, affecting the overall strength of concrete. Therefore, in this study, in order to grasp the interface between natural aggregate and lightweight aggregate by type, we adopted a method of measuring electrical resistance using an EIS measuring device, which is a non-destructive test, and lightweight bone. The change in the state of the interface was tested on the outside of the material through a blast furnace slag coating. As a result of the experiment, it was confirmed that the electric resistance was about 90% lower than that in the air-dried state through the electrolyte immersion, and the electric resistance differs depending on the type of aggregate and the presence or absence of coating. As a result of the experiment, the difference in compressive strength depending on the type of aggregate and the presence or absence of coating was shown, and the difference in impedance value and phase angle for each type of lightweight aggregate was shown.

• Computers and Concrete
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• v.22 no.4
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• pp.419-437
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• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• Advances in concrete construction
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• v.13 no.6
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• pp.411-422
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• 2022

Aggregates mineralogical, and petrographic properties directly affect the mechanical properties of the produced high strength. This study is focused on the effects of magmatic, sedimentary, and metamorphic aggregates on the performance of high strength concrete. In this study, the effect of the mineralogical properties of aggregates on the compressive strength and modulus of elasticity of high-strength concrete was estimated by Artifical Neural Network (ANN). To estimate the compressive strength and elasticity modules, 96 test specimens were produced. After 28 days under suitable conditions, tests were carried out to determine the compressive strength and modulus of elasticity of the test specimens. This study also focused on the application of artificial neural networks (ANN) to predict the 28-day compressive strength and the modulus of elasticity of high-strength concrete. An ANN model is developed, trained, and tested by using the available test data obtained from the experimental studies. The ANN model is found to predict the modulus of elasticity, and 28 days compressive strength of high strength concrete well, within the ranges of the input parameters. These comparisons show that ANNs have a strong potential to predict the compressive strength and modulus of elasticity of high-strength concrete over the range of input parameters considered.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.19 no.4
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• pp.208-213
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• 2009

Artificial lightweight aggregates were produced by using bottom ashes and dredged soils from coal power plant. The amount of glassy phases on the aggregate surfaces, specific gravities, absorption rates, and observations of cross-sectional surfaces were compared according to the compositions, sintering temperatures, and the amount of coating. It is concluded that surface modification by 10 % $CaCO_3$ coating on the aggregate surfaces enhances the properties of aggregates as follows: Specific gravities were controlled by depressing formation of large pores in the aggregates. Sticking phenomena among aggregates during the sintering process was drastically decreased by reducing glassy phases on the aggregate surfaces. Pumping problems during the application of ready-mix concretes containing lightweight aggregates having high value of absorption rates could be solved by reducing the absorption rate.

• Structural Engineering and Mechanics
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• v.52 no.1
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• pp.97-113
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• 2014

In the present work appropriate concrete material models have been proposed to predict drying shrinkage and specific creep of High-performance concrete (HPC) using Artificial Neural Network (ANN). The ANN models are trained, tested and validated using 106 different experimental measured set of data collected from different literatures. The developed models consist of 12 input parameters which include quantities of ingredients namely ordinary Portland cement, fly ash, silica fume, ground granulated blast-furnace slag, water, and other aggregate to cement ratio, volume to surface area ratio, compressive strength at age of loading, relative humidity, age of drying commencement and age of concrete. The Feed-forward backpropagation networks with Levenberg-Marquardt training function are chosen for proposed ANN models and same implemented on MATLAB platform. The results shows that the proposed ANN models are more rational as well as computationally more efficient to predict time-dependent properties of drying shrinkage and specific creep of HPC with high level accuracy.