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Concrete strength and factors affecting its development during early concrete curing are important research topics. Avoiding uncertain incidents during construction and in service life of structures requires an appropriate monitoring system. Therefore, numerous techniques are used to monitor the health of a structure. This paper presents a nondestructive testing technique for monitoring the strength development of concrete at early curing ages. Dispersed carbon nanotubes (CNTs) were used with cementitious materials and piezoelectric (PZT) material, a PZT ceramic, owing to their properties of intra electromechanical effects and sensitivity to measure the electromechanical impedance (EMI) signatures and relevant properties related to concrete strength, such as the elastic modulus, displacement, acceleration, strength, and loading effects. Concrete compressive strength, hydration temperature, mixture ratio, and variation in data obtained from the impedance signatures using fuzzy logic were utilized in the comparative result prediction method for concrete strength. These results were calculated using a fuzzy logic-based model considering the maturity method, universal testing machine (UTM) data, and analyzed EMI data. In the study, for data acquisition, a hybrid PZT–CNT sensor and a temperature sensor (Smart Rock) were embedded in the concrete to obtain the hydration temperature history to utilize the concrete maturity method and provide data on the EMI signatures. The dynamic changes in the medium caused during the phase in the concrete strengthening process were analyzed to predict the strength development process of concrete at early curing ages. Because different parameters are considered while calculating the concrete strength, which is related to its mechanical properties, the proposed novel method considers that changes in the boundary condition occurring in the concrete paste modify the resonant frequency response of the structure. Thus, relating and analyzing this feature can help predict the concrete strength. A comprehensive comparison of the results calculated using the proposed module, maturity method, and cylindrical specimens tested using the UTM proved that it is a cost-effective and fast technique to estimate concrete strength to ensure a safe construction of reinforced cement concrete infrastructures.
Najeebullah Tareen; Junkyeong Kim; Won-Kyu Kim; Seunghee Park. Fuzzy Logic-Based and Nondestructive Concrete Strength Evaluation Using Modified Carbon Nanotubes as a Hybrid PZT–CNT Sensor. Materials 2021, 14, 2953 .
AMA StyleNajeebullah Tareen, Junkyeong Kim, Won-Kyu Kim, Seunghee Park. Fuzzy Logic-Based and Nondestructive Concrete Strength Evaluation Using Modified Carbon Nanotubes as a Hybrid PZT–CNT Sensor. Materials. 2021; 14 (11):2953.
Chicago/Turabian StyleNajeebullah Tareen; Junkyeong Kim; Won-Kyu Kim; Seunghee Park. 2021. "Fuzzy Logic-Based and Nondestructive Concrete Strength Evaluation Using Modified Carbon Nanotubes as a Hybrid PZT–CNT Sensor." Materials 14, no. 11: 2953.
As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.
Sehwan Park; Jinpyung Kim; KyoYoung Jeon; Junkyeong Kim; Seunghee Park. Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3. Remote Sensing 2021, 13, 2011 .
AMA StyleSehwan Park, Jinpyung Kim, KyoYoung Jeon, Junkyeong Kim, Seunghee Park. Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3. Remote Sensing. 2021; 13 (10):2011.
Chicago/Turabian StyleSehwan Park; Jinpyung Kim; KyoYoung Jeon; Junkyeong Kim; Seunghee Park. 2021. "Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3." Remote Sensing 13, no. 10: 2011.
Forest fires are severe disasters that cause significant damage in the Republic of Korea and the entire world, and an effort is being made to prevent forest fires internationally. The Republic of Korea budgets 3.38 million USD every year to prevent forest fires. However, an average of 430 wildfires occur nationwide annually. Thirty-eight percent of the forest fire budget is used for forest restoration. Restoring afforestation in the affected areas is a top priority. This study aimed to estimate the degree of vegetative regeneration using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjustment Vegetation Index (SAVI), and Normalized Burn Ratio (NBR). Although many studies have used NBR with NDVI to extract plant regeneration regions, they suffer from atmospheric effects and soil brightness. Thus, this study utilizes NBR with NDVI, EVI, and SAVI to accurately select areas for targeted forest restoration. Furthermore, this study applies clustering analysis to extract the spatial boundary of vegetative regenerative regions. The proposed method suggests a pixel range of vegetation indices. These ranges can be used as an indicator, such as the NBR’s Fire Severity Level, which reflects the mountain’s local characteristics, meaning that it can be useful after forest fires. Using the three vegetation indices can extract more accurate vegetation areas than using NBR with NDVI and can help determine a forest restoration target area.
Yunhee Kim; Myeong-Hun Jeong; Minkyo Youm; Junkyeong Kim; Jinpyung Kim. Recovery of Forest Vegetation in a Burnt Area in the Republic of Korea: A Perspective Based on Sentinel-2 Data. Applied Sciences 2021, 11, 2570 .
AMA StyleYunhee Kim, Myeong-Hun Jeong, Minkyo Youm, Junkyeong Kim, Jinpyung Kim. Recovery of Forest Vegetation in a Burnt Area in the Republic of Korea: A Perspective Based on Sentinel-2 Data. Applied Sciences. 2021; 11 (6):2570.
Chicago/Turabian StyleYunhee Kim; Myeong-Hun Jeong; Minkyo Youm; Junkyeong Kim; Jinpyung Kim. 2021. "Recovery of Forest Vegetation in a Burnt Area in the Republic of Korea: A Perspective Based on Sentinel-2 Data." Applied Sciences 11, no. 6: 2570.
Recently, the early-age strength prediction for RC (reinforced concrete) structures has been an important topic in the construction industry, relating to project-time reduction and structural safety. To address this, numerous destructive and NDTs (non-destructive tests) are applied to monitor the early-age strength development of concrete. This study elaborates on the NDT techniques of ultrasonic wave propagation and concrete maturity for the estimation of compressive strength development. The results of these comparative estimation approaches comprise the concrete maturity method, penetration resistance test, and an ultrasonic wave analysis. There is variation of the phase transition in the concrete paste with the changing of boundary limitations of the material in accordance with curing time, so with the formation of phase-transition changes, changes in the velocities of ultrasonic waves occur. As the process of hydration takes place, the maturity method produces a maturity index using the time-feature reflection on the strength-development process of the concrete. Embedded smart temperature sensors (SmartRock) and PZT (piezoelectric) sensors were used for the data acquisition of hydration temperature history and wave propagation. This study suggests a novel relationship between wave propagation, penetration tests, and hydration temperature, and creates a method that relies on the responses of resonant frequency changes with the change of boundary conditions caused by the strength-gain of the concrete specimen. Calculating the changes of these features provides a pattern for estimating concrete strength. The results for the specimens were validated by comparing the strength results with the penetration resistance test by a universal testing machine (UTM). An algorithm used to relate the concrete maturity and ultrasonic wave propagation to the concrete compressive strength. This study leads to a method of acquiring data for forecasting in-situ early-age strength of concrete, used for secure construction of concrete structures, that is fast, cost effective, and comprehensive for SHM (structural health monitoring).
Najeebullah Tareen; Kim; Seunghee Park; Park; Junkyeong Kim; Won-Kyu Kim. Comparative Analysis and Strength Estimation of Fresh Concrete Based on Ultrasonic Wave Propagation and Maturity Using Smart Temperature and PZT Sensors. Micromachines 2019, 10, 559 .
AMA StyleNajeebullah Tareen, Kim, Seunghee Park, Park, Junkyeong Kim, Won-Kyu Kim. Comparative Analysis and Strength Estimation of Fresh Concrete Based on Ultrasonic Wave Propagation and Maturity Using Smart Temperature and PZT Sensors. Micromachines. 2019; 10 (9):559.
Chicago/Turabian StyleNajeebullah Tareen; Kim; Seunghee Park; Park; Junkyeong Kim; Won-Kyu Kim. 2019. "Comparative Analysis and Strength Estimation of Fresh Concrete Based on Ultrasonic Wave Propagation and Maturity Using Smart Temperature and PZT Sensors." Micromachines 10, no. 9: 559.
It has been proposed that pre-stressed concrete bridges improve load performance by inducing axial pre-stress using pre-stress tendons. However, the tensile force of the pre-stress tendons could not be managed after construction, although it directly supports the load of the structure. Thus, the tensile force of the pre-stress tendon should be checked for structural health monitoring of pre-stressed concrete bridges. In this study, a machine learning–based tensile force estimation method for a pre-stressed concrete girder is proposed using an embedded elasto-magnetic sensor and machine learning method. The feedforward neural network and radial basis function network were applied to estimate the tensile force of the pre-stress tendon using the area ratio of the magnetic hysteresis curve measured by the embedded elasto-magnetic sensor. The feedforward neural network and radial basis function network were trained using 213 datasets obtained in laboratory experiments, and trained feedforward neural network and radial basis function network were applied to a 50-m real-scale pre-stressed concrete girder test for estimating tensile force. Nine embedded elasto-magnetic sensors were installed on the sheath, and the magnetic hysteresis curves of the pre-stress tendons were measured during tensioning. The area ratio was extracted and inputted to the trained feedforward neural network and radial basis function network to estimate the tensile force. The estimated tensile force was compared with the reference tensile force measured by the load cell. According to the result, the estimated tensile force can represent the actual tensile force of the pre-stress tendon without calibrating tensile force estimation algorithms at the site. In addition, it can measure the actual friction loss by estimating the tensile force at the maximum eccentric part. Based on the results, the proposed method might be a solution for the structural health monitoring of pre-stressed concrete bridges with field applicability.
Junkyeong Kim; Seunghee Park. Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor. Structural Health Monitoring 2019, 19, 281 -292.
AMA StyleJunkyeong Kim, Seunghee Park. Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor. Structural Health Monitoring. 2019; 19 (1):281-292.
Chicago/Turabian StyleJunkyeong Kim; Seunghee Park. 2019. "Field applicability of a machine learning–based tensile force estimation for pre-stressed concrete bridges using an embedded elasto-magnetic sensor." Structural Health Monitoring 19, no. 1: 281-292.
This study investigates the applicability of an embedded EM sensor using a series of experimental studies. To verify the embedded EM sensor, the magnetic hysteresis of various types of PS tendons is measured. After that, the embedded EM sensor is embedded into the concrete and the possibility of obtaining measurements is verified. Finally, the downscaled PSC girder specimen having a sheath with a different curvature is fabricated and the influence of the sheath curvature is investigated. The magnetic hysteresis was changed constantly even though the type of PS tendon was changed, and the embedded EM sensor can measure the magnetic hysteresis, even in the concrete and curved sheath. The area of magnetic hysteresis was decreased according to the increase in the tension force, but the actual values were different according to the number and cross-sectional area of tendons and the initial state of sensors. To compensate for the measured data, the tensile force was converted to the tensile stress and the area ratio was used to compensate for the initial value of the EM sensor. According to the test results, the embedded EM sensor could be applied to the actual PS girder and it can measure the actual tension, which includes the friction loss.
Junkyeong Kim; Ju-Won Kim; Seunghee Park. Investigation of Applicability of an Embedded EM Sensor to Measure the Tension of a PSC Girder. Journal of Sensors 2019, 2019, 1 -12.
AMA StyleJunkyeong Kim, Ju-Won Kim, Seunghee Park. Investigation of Applicability of an Embedded EM Sensor to Measure the Tension of a PSC Girder. Journal of Sensors. 2019; 2019 ():1-12.
Chicago/Turabian StyleJunkyeong Kim; Ju-Won Kim; Seunghee Park. 2019. "Investigation of Applicability of an Embedded EM Sensor to Measure the Tension of a PSC Girder." Journal of Sensors 2019, no. : 1-12.
Yong-Soo Lee; Junkyeong Kim; Changgil Lee; Seunghee Park. Applicability investigation of piezoelectric sensor-based damage detection technique for membrane. Desalination and Water Treatment 2019, 143, 24 -28.
AMA StyleYong-Soo Lee, Junkyeong Kim, Changgil Lee, Seunghee Park. Applicability investigation of piezoelectric sensor-based damage detection technique for membrane. Desalination and Water Treatment. 2019; 143 ():24-28.
Chicago/Turabian StyleYong-Soo Lee; Junkyeong Kim; Changgil Lee; Seunghee Park. 2019. "Applicability investigation of piezoelectric sensor-based damage detection technique for membrane." Desalination and Water Treatment 143, no. : 24-28.
Junkyeong Kim; Seunghee Park; Hwanwoo Lee. Magnetic Hysteresis Monitoring according to the Change of Tensile Force and Steel Class of PS Tendons. Journal of the Computational Structural Engineering Institute of Korea 2018, 31, 115 -120.
AMA StyleJunkyeong Kim, Seunghee Park, Hwanwoo Lee. Magnetic Hysteresis Monitoring according to the Change of Tensile Force and Steel Class of PS Tendons. Journal of the Computational Structural Engineering Institute of Korea. 2018; 31 (2):115-120.
Chicago/Turabian StyleJunkyeong Kim; Seunghee Park; Hwanwoo Lee. 2018. "Magnetic Hysteresis Monitoring according to the Change of Tensile Force and Steel Class of PS Tendons." Journal of the Computational Structural Engineering Institute of Korea 31, no. 2: 115-120.
This study estimates the strength of a special mixture of high-strength concrete (HSC) with admixtures for use in a nuclear power plant (NPP). Nuclear power plant structures need a HSC with some additional qualities to operate the safe options. For this purpose, the experimented concrete was specially designed to fulfill the required qualities of NPP. For gaining these desirable qualities, it needs to monitor the concrete strength development process. Here, the PZT materials were used as sensors to acquire data by measuring the electromechanical impedance (EMI), and then cross correlation (CC) was calculated to look at changes according to strength development. Data were measured for 28 days, and over this period concrete can gain up to 96% of its design strength. This technique is based on a single sensor. After casting concrete, the PZT material starts vibrating as an actuator to produce vibrations. At the same time, it also works as a sensor to measure the dynamic response of the structure to the vibrations. With strength development, the resonant frequencies of the EMI start changing. To estimate the strength development, a fuzzy logic tool was used to analyze the parameters, allowing for us to estimate and predict the concrete strength. For cross-checking, the estimated strength was compared with the actual strength of concrete; this was determined by examining cuboid cores taken from specimens during experiments at the 1st, 3rd, 7th, 14th, and 28th days. According to the results, this approach of strength estimation and monitoring the strength development is useful for forecasting the stability of structures.
Sang-Ki Choi; Najeebullah Tareen; Junkyeong Kim; Seunghee Park; Innjoon Park. Real-Time Strength Monitoring for Concrete Structures Using EMI Technique Incorporating with Fuzzy Logic. Applied Sciences 2018, 8, 75 .
AMA StyleSang-Ki Choi, Najeebullah Tareen, Junkyeong Kim, Seunghee Park, Innjoon Park. Real-Time Strength Monitoring for Concrete Structures Using EMI Technique Incorporating with Fuzzy Logic. Applied Sciences. 2018; 8 (1):75.
Chicago/Turabian StyleSang-Ki Choi; Najeebullah Tareen; Junkyeong Kim; Seunghee Park; Innjoon Park. 2018. "Real-Time Strength Monitoring for Concrete Structures Using EMI Technique Incorporating with Fuzzy Logic." Applied Sciences 8, no. 1: 75.
Junkyeong Kim; Jaemin Kim; Kyung-Joon Shin; Hwanwoo Lee; Seunghee Park. ANN-based tensile force estimation for pre-stressed tendons of PSC girders using FBG/EM hybrid sensing. Insight - Non-Destructive Testing and Condition Monitoring 2017, 59, 544 -552.
AMA StyleJunkyeong Kim, Jaemin Kim, Kyung-Joon Shin, Hwanwoo Lee, Seunghee Park. ANN-based tensile force estimation for pre-stressed tendons of PSC girders using FBG/EM hybrid sensing. Insight - Non-Destructive Testing and Condition Monitoring. 2017; 59 (10):544-552.
Chicago/Turabian StyleJunkyeong Kim; Jaemin Kim; Kyung-Joon Shin; Hwanwoo Lee; Seunghee Park. 2017. "ANN-based tensile force estimation for pre-stressed tendons of PSC girders using FBG/EM hybrid sensing." Insight - Non-Destructive Testing and Condition Monitoring 59, no. 10: 544-552.
The tensile force of pre-stressed concrete (PSC) girders is the most important factor for managing the stability of PSC bridges. The tensile force is induced using pre-stressing (PS) tendons of a PSC girder. Because the PS tendons are located inside of the PSC girder, the tensile force cannot be measured after construction using conventional NDT (non-destructive testing) methods. To monitor the induced tensile force of a PSC girder, an embedded EM (elasto-magnetic) sensor was proposed in this study. The PS tendons are made of carbon steel, a ferromagnetic material. The magnetic properties of the ferromagnetic specimen are changed according to the induced magnetic field, temperature, and induced stress. Thus, the tensile force of PS tendons can be estimated by measuring their magnetic properties. The EM sensor can measure the magnetic properties of ferromagnetic materials in the form of a B (magnetic density)-H (magnetic force) loop. To measure the B-H loop of a PS tendon in a PSC girder, the EM sensor should be embedded into the PSC girder. The proposed embedded EM sensor can be embedded into a PSC girder as a sheath joint by designing screw threads to connect with the sheath. To confirm the proposed embedded EM sensors, the experimental study was performed using a down-scaled PSC girder model. Two specimens were constructed with embedded EM sensors, and three sensors were installed in each specimen. The embedded EM sensor could measure the B-H loop of PS tendons even if it was located inside concrete, and the area of the B-H loop was proportionally decreased according to the increase in tensile force. According to the results, the proposed method can be used to estimate the tensile force of unrevealed PS tendons.
Junkyeong Kim; Ju-Won Kim; Chaggil Lee; Seunghee Park. Development of Embedded EM Sensors for Estimating Tensile Forces of PSC Girder Bridges. Sensors 2017, 17, 1989 .
AMA StyleJunkyeong Kim, Ju-Won Kim, Chaggil Lee, Seunghee Park. Development of Embedded EM Sensors for Estimating Tensile Forces of PSC Girder Bridges. Sensors. 2017; 17 (9):1989.
Chicago/Turabian StyleJunkyeong Kim; Ju-Won Kim; Chaggil Lee; Seunghee Park. 2017. "Development of Embedded EM Sensors for Estimating Tensile Forces of PSC Girder Bridges." Sensors 17, no. 9: 1989.
Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.
Junkyeong Kim; Chaggil Lee; Seunghee Park. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals. Sensors 2017, 17, 1319 .
AMA StyleJunkyeong Kim, Chaggil Lee, Seunghee Park. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals. Sensors. 2017; 17 (6):1319.
Chicago/Turabian StyleJunkyeong Kim; Chaggil Lee; Seunghee Park. 2017. "Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals." Sensors 17, no. 6: 1319.
Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since con-crete is susceptible to fractures, it is essential to confirm the strength development of concrete during the curing process, in order to prevent unexpected collapse. To address this issue, this study proposes an artificial neural network (ANN)-based strength estimation technique using several kinds of strength related factors of concrete materials. In particular, the variations in mechanical properties of concrete were measured through electro-mechanical impedance (EMI) change using an embedded piezoelectric sensor. The ANN was trained to estimate the strength of concrete by using watercement ratio, curing time and temperature, maturity from internal temperature, and 1-CC of the EMI signals. The trained ANN was verified with conventional strength estimation models throughout a series of experimental studies. According to the comparison results, it is noted that the proposed technique could be very effectively applied to estimate the strength of concrete.
Tae-Keun Oh; Junkyeong Kim; Changgil Lee; Seunghee Park. Nondestructive Concrete Strength Estimation based on Electro-Mechanical Impedance with Artificial Neural Network. Journal of Advanced Concrete Technology 2017, 15, 94 -102.
AMA StyleTae-Keun Oh, Junkyeong Kim, Changgil Lee, Seunghee Park. Nondestructive Concrete Strength Estimation based on Electro-Mechanical Impedance with Artificial Neural Network. Journal of Advanced Concrete Technology. 2017; 15 (3):94-102.
Chicago/Turabian StyleTae-Keun Oh; Junkyeong Kim; Changgil Lee; Seunghee Park. 2017. "Nondestructive Concrete Strength Estimation based on Electro-Mechanical Impedance with Artificial Neural Network." Journal of Advanced Concrete Technology 15, no. 3: 94-102.
This article reports the application of a nonlinear impedance technique under a low-frequency vibration to detect contact-type structural defects such as fatigue cracks. If the contact-type damage is developed within the structure due to the low-frequency dynamic load, the vibration can cause a nonlinear fluctuation of the structural impedance because of the contact acoustic nonlinearity (CAN). This nonlinear effect can lead to amplitude modulation and phase modulation of the current flow. The nonlinear characteristics of the structural impedance can be extracted by observing the coupled electromechanical impedance of a piezoelectric active sensor and utilizing nonlinear wave modulation spectroscopy. Experimentally, a low-frequency vibration was applied to a notched coupon at a certain natural frequency by a shaker, so that a nonlinear fatigue crack can be artificially formed at the notch tip. Then, the nonlinear features are extracted based on a self-sensing impedance measurement from a host structure under a low-frequency vibration. The damage metric was established based on the nonlinear fluctuation of the impedance due to the CAN.
Changgil Lee; Junkyeong Kim; Seunghee Park; Dae-Hyun Kim; Ju-Won Kim. Advanced Fatigue Crack Detection Using Nonlinear Self-Sensing Impedance Technique for Automated NDE of Metallic Structures. Research in Nondestructive Evaluation 2014, 26, 107 -121.
AMA StyleChanggil Lee, Junkyeong Kim, Seunghee Park, Dae-Hyun Kim, Ju-Won Kim. Advanced Fatigue Crack Detection Using Nonlinear Self-Sensing Impedance Technique for Automated NDE of Metallic Structures. Research in Nondestructive Evaluation. 2014; 26 (2):107-121.
Chicago/Turabian StyleChanggil Lee; Junkyeong Kim; Seunghee Park; Dae-Hyun Kim; Ju-Won Kim. 2014. "Advanced Fatigue Crack Detection Using Nonlinear Self-Sensing Impedance Technique for Automated NDE of Metallic Structures." Research in Nondestructive Evaluation 26, no. 2: 107-121.
It is highly necessary to evaluate strength development during the curing process to ensure the quality of concrete in construction using concrete. In particular, curing strength monitoring at early age is very important to reduce the construction cost and time, because it can provide the information required for the decision-making to safely progress to the next process. In this study, a guided wave-based non-destructive curing strength gain monitoring method that can be used even for early-age concrete is proposed. A steel plate-type piezoelectric sensor module was embedded in the concrete media at the same time as concrete placement to measure the signal from early-age concrete. The guided wave signals were measured continuously using the pitch-catch method at regular intervals. The wavelet transform process was performed to improve the quality of the signal. The guided wave's velocity of each measurement time was varied by extracting the time of flight. The wave velocity hysteresis curve according to the curing age was traced to analyse the variation patterns. Finally, a specific equation to estimate the curing strength without destructive test was derived using regression analysis based on the wave velocity hysteresis and the results from the compression test.
Ju-Won Kim; Junkyeong Kim; Seunghee Park; Tae Keun Oh. Integrating embedded piezoelectric sensors with continuous wavelet transforms for real-time concrete curing strength monitoring. Structure and Infrastructure Engineering 2014, 11, 897 -903.
AMA StyleJu-Won Kim, Junkyeong Kim, Seunghee Park, Tae Keun Oh. Integrating embedded piezoelectric sensors with continuous wavelet transforms for real-time concrete curing strength monitoring. Structure and Infrastructure Engineering. 2014; 11 (7):897-903.
Chicago/Turabian StyleJu-Won Kim; Junkyeong Kim; Seunghee Park; Tae Keun Oh. 2014. "Integrating embedded piezoelectric sensors with continuous wavelet transforms for real-time concrete curing strength monitoring." Structure and Infrastructure Engineering 11, no. 7: 897-903.
Junkyeong Kim; Ju-Won Kim; Seunghee Park. Early-age concrete strength estimation based on piezoelectric sensor using artificial neural network. SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring 2014, 906316 -906316-7.
AMA StyleJunkyeong Kim, Ju-Won Kim, Seunghee Park. Early-age concrete strength estimation based on piezoelectric sensor using artificial neural network. SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring. 2014; ():906316-906316-7.
Chicago/Turabian StyleJunkyeong Kim; Ju-Won Kim; Seunghee Park. 2014. "Early-age concrete strength estimation based on piezoelectric sensor using artificial neural network." SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring , no. : 906316-906316-7.
Recently, demands for the construction of nuclear power plants(NPP) using high strength concrete(HSC) has been increased. However, HSC might be susceptible to brittle fracture if the curing process is inadequate. To prevent unexpected collapse during and after the construction of HSC structures, it is essential to confirm the strength development of HSC during the curing process. In this study, a novel method to estimate the strength development of HSC based on internal harmonic wave measurements using an embedded piezoelectric sensor is proposed. The amplitude of propagated harmonic wave along the concrete media was tracked to monitor the strength development of NPP concrete. In addition, the strength estimation equation was derived using regression method. The results confirmed that the proposed technique can be applied successfully monitoring of the strength development during the curing process of HSC structures.
Junkyeong Kim; Ju-Won Kim; Changgil Lee; Seunghee Park. Concrete Strength Development Monitoring Technique for Automatic Construction Management of Nuclear Power plants. IABSE Symposium, Madrid 2014: Engineering for Progress, Nature and People 2014, 1 .
AMA StyleJunkyeong Kim, Ju-Won Kim, Changgil Lee, Seunghee Park. Concrete Strength Development Monitoring Technique for Automatic Construction Management of Nuclear Power plants. IABSE Symposium, Madrid 2014: Engineering for Progress, Nature and People. 2014; ():1.
Chicago/Turabian StyleJunkyeong Kim; Ju-Won Kim; Changgil Lee; Seunghee Park. 2014. "Concrete Strength Development Monitoring Technique for Automatic Construction Management of Nuclear Power plants." IABSE Symposium, Madrid 2014: Engineering for Progress, Nature and People , no. : 1.