• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2005년도 추계 학술발표회 제17권2호
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• pp.367-370
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• 2005

In this study, air meter method and capacitance measurement method to apply economically at quality control of ready-mixed concrete among various unit water content measurement technique was selected. Then, it was evaluated estimating performance of unit water content according to the change of water-binder ratio and unit water content. Also, it was examined influence about error occurrence of unit water content by change of properties of used materials. Finally, based on this study, it was proposed fundamental data to utilize measurement technique of unit water content to quality control. of ready-mixed concrete in construction field.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 봄 학술논문 발표대회
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• pp.33-34
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• 2020

Unit-water content is an element directly related to durability and unit-water content of concrete used at construction site has a great effect on the durability of construction structure. Many methods are being discussed for more convenient and accurate measurements of unit-water content. Therefore, an experimental study was conducted on the prediction of unit-water content using ultrasonic equipment. Depending on the amount of cement in cement paste, the speed of ultrasonic waves varies and the experiment will be carried out using the same reception sensitivity in the future.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.59-60
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• 2021

The unit-water content has a major problem in concrete structures which leads to micro cracks on the concrete during drying time. Thus, the compressive strength and durability of the concrete structures are significantly reduced. Several techniques have been developed to measure the unit-water content in concrete structures such as heating drying, unit volume mass, and capacitance measurements. However, these techniques have problems in during measurement such as longer time, expensive and difficult in analysis of data. Frequency Domain Reflectivity (FDR) is one of the sensors which used to measure the water content. This method has several advantages including easy to measure, inexpensive, and capable of measuring moisture in real time. In this study, an attempt has been made to evaluate the unit-water content in concrete using the FDR sensor and interpret the data with deep learning method.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 봄 학술논문 발표대회
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• pp.201-202
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• 2023

Recently, interest in concrete quality has been increasing. It is important to manage these factors due to unit-water content and aggregate quality that affect concrete quality. In this study, the unit-water content of concrete was measured through an economical, easy-to-measure, and portable Frequency Domain Reflecmetry sensor among micro-methods that compensated for the shortcomings of existing concrete unit-water content measurement methods. As a result of predicting the unit-water content, the accuracy within the ± 10 kg/m³ error range was confirmed to be more than 72% of all factors. In order to ensure high accuracy, it is considered necessary to conduct an experiment to evaluate the unit-water content by conducting additional experiments according to other variables and factors.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.29-30
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• 2022

The unit-water content has a very significant effect on the durability of the construction structure and the quality of concrete. Although there are various methods for measuring the unit-water content, there are problems of time required for measurement, precision, and reproducibility. Recently, there is an FDR sensor capable of measuring moisture content in real time through an apparent dielectric constant change of electromagnetic waves. In addition, various artificial intelligence techniques that can non-linearly supplement the accuracy of FDR sensors are being studied. In this study, the accuracy of unit-water content measurement was compared and evaluated using machine learning and deep learning techniques after normalizing the data secured in concrete using frequency domain reflectometry (FDR) sensors used to measure soil moisture at home and abroad. The result of comparing the accuracy of machine learning and deep learning is judged to be excellent in the accuracy of deep learning, which can well express the nonlinear relationship between FDR sensor data and concrete unit-water content.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.17-18
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• 2021

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2020년도 가을 학술논문 발표대회
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• pp.6-7
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• 2020

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 가을 학술논문 발표대회
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• pp.109-110
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• 2022

Currently, interest in the quality of concrete is increasing. Among the important factors for evaluating the quality of concrete, interest in unit-water content is also increasing. Currently, the air-meter method, the microwave oven drying method, the capacitance method, and the microwave penetration method are used to measure the unit-water content of concrete.. Among the above methods, except for the microwave method, the measurement method is complicated, portability is reduced, and economic efficiency is reduced. This research aims to measure a unit-water content by using a Frequency Domain Reflectometry(FDR) sensor that is economical, simple to measure, and portable among microwave methods. In addition, it is an experimental study to determine the accuracy of unit-water content using a single input residual model during deep learning to solve the limitations of the FDR sensor.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2013년도 춘계 학술논문 발표대회
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• pp.232-233
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• 2013

The unit-water content in fresh concrete determines consistency, and play an important role in condensing the structure of concrete and enhancing the durability of concrete. The capacitance measurement method measure quickly unit-water content and is the best way to apply to construction site. In this study, the unit-water content of capacitance measurement method is estimated according to types and replacemment ratio of admixture. and the field application of capacitance measurement method is reviewed.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.8-9
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• 2021

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data. The multi-input deep learning model is as accurate as 24.25% higher than the OLS linear regression model, which shows that deep learning can more effectively identify the nonlinear relationship between high-frequency moisture sensor data and unit quantity than linear regression.