• Proceedings of the Korea Concrete Institute Conference
• /
• 2001.11a
• /
• pp.681-686
• /
• 2001

The purpose of this study is to provide the optimum mix design of concrete to be placed at the face slab concrete in CFRD(Concrete Faced Rockfill Dam) for pumped storage power plants. The basic performance tests including compressive strength, modulus of elasticity, flexural strength and the control properties of crack including plastic shrinkage, drying shrinkage were conducted for concrete using fly ash and polypropylene fiber. From this study, the fly ash concrete represented the better results in the aspects of basic performance, control properties of crack and economy than ordinary portland cement concrete. Especially the concrete mix design containing 20% of fly ash is recommended to be applied in the construction of the face slab concrete in CFRD for pumped storage power plants.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.3
• /
• pp.55-65
• /
• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• Structural Engineering and Mechanics
• /
• v.77 no.1
• /
• pp.89-102
• /
• 2021

Rapid construction of prefabricated bridges requires minimizing the field work of precast members and ensuring structural stability and constructability. In this study, we conducted experimental and analytical investigations of transverse joints of prefabricated T-girder bridge superstructures to verify the flexural performance and serviceability. In addition, we conducted parametric studies to identify the joint parameters. The results showed that both the segmented and continuous specimens satisfied the ultimate flexural strength criterion, and the segmented specimen exhibited unified behavior, with the flexural strength corresponding to that of the continuous specimen. The segmented specimens exhibited elastic behavior under service load conditions, and the maximum crack width satisfied the acceptance criteria. The reliability of the finite element model of the joint was verified, and parametric analysis of the convexity of the joint section and the compressive strength of the filler concrete showed that the minimum deflection and crack width occurred at a specific angle. As the strength of the filler concrete increased, the deflection and crack width decreased. However, we confirmed that the reduction in the crack width was hardly observed above a specific strength. Therefore, a design suitable for prefabricated bridges and accelerated construction can be achieved by improving the joint specifications based on the required criteria.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.8 no.4
• /
• pp.475-482
• /
• 2020

In this study, three levels of solid capsules were prepared according to the composition ratio of core materials for the crystal growth type self-healing solid capsule, and a cement mortar was prepared with the crystal growth type self-healing solid capsule. The prepared solid capsule was mixed with 3% of the cement mass to evaluate the healing properties according to the crack induction age of the cement mortar. As a result of test, the crack healing properties according to the crack induction age of cement mortar mixed of solid capsules, it was confirmed that the self-healing performance of the cement mortar with the solid capsules was increased self-healing performance of 7 days than 28 days. This is because the unhydrated binder remains.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.24 no.3
• /
• pp.237-248
• /
• 2011

Tensile softening characteristics play an important role in the structural behavior of steel fiber-reinforced ultra high performance concrete. Tension softening modeling and numerical analysis method are necessary for the prediction of structural performance of steel fiber-reinforced concrete. The numerical method to predict the flexural behavior is proposed in this study. Tension softening modeling is carried out by using crack equation based on fictitious crack and inverse analysis in which load-crack opening displacement relationship is considered. Thereafter material modeling is performed considering tension softening. The comparison of moment-curvature curves of the numerical analysis results with the test results indicates a reasonable agreement. Therefore, the present numerical results prove that good prediction of flexural behavior of steel fiber-reinforced ultra high performance concrete beams can be achieved by employing the proposed method.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.10 no.4
• /
• pp.569-576
• /
• 2022

In this study, the healing performance of hybrid self-healing concrete was investigated by mixing bacterial pellets(BP) and solid phase capsules(SC), respectively, based on organic-inorganic self-healing material(MC). Constant water head permeability test was applied as a method of evaluating the healing performance, and the healing rate and the healed crack width calculated by the equivalent crack width were used as evaluation indicies. As a result of the water permeability test, when the initial crack width was 0.3 mm, the healing rates of MC-BP and MC-SC were 2.1~3.0 %pt higher than that of MC, and the healed crack width of hybrid concrete increased by 0.017~0.018 mm. In conclusion, it was found that the self-healing performance was not significantly improved even if the two types of healing materials are used together.

• Korean Journal of Remote Sensing
• /
• v.39 no.4
• /
• pp.371-393
• /
• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• Journal of the Korean Institute of Educational Facilities
• /
• v.20 no.4
• /
• pp.35-43
• /
• 2013

Some results of experimental investigation conducted to assess the effect of cement composite strength and ductility on the shear behavior and crack-damage mitigation of stud connections between existing reinforced concrete frame in school buildings and seismic strengthening elements from cyclically direct shear tests are described. The cement composite strengths include 50 for medium strength and 70 MPa for high strength. Two types of cement composites, strain-hardening cement composite (SHCC) and non-shrinkage mortar, are used for stud shear connection specimens. The special SHCCs are reinforced with hybrid 0.2% polyethylene (PE) and 1.3% polyvinyl alcohol (PVA) fibers at the volume fraction and exhibits tensile strain capacity ranging from 0.2 to 0.5%. Test result indicates that SHCC improves the seismic performance and crack-damage mitigation of stud shear connections compared with stud connections with non-shrinkage mortar. However, the performance enhancement in SHCC stud connections with transverse and longitudinal reinforcements is less notable for those without additional reinforcement.

• Structural Engineering and Mechanics
• /
• v.53 no.6
• /
• pp.1183-1200
• /
• 2015

Reinforcement corrosion can cause serious safety deterioration to aging concrete structures exposed in aggressive environments. This paper presents an approach for reliability analyses of deteriorating reinforced concrete structures affected by reinforcement corrosion on the basis of the representative symptoms identified during the deterioration process. The concrete cracking growth and rebar bond strength evolution due to reinforcement corrosion are chosen as key symptoms for the performance deterioration of concrete structures. The crack width at concrete cover surface largely depends on the corrosion penetration of rebar due to the expansive rust layer at the bond interface generated by reinforcement corrosion. The bond strength of rebar in the concrete correlates well with concrete crack width and decays steadily with crack width growth. The estimates of cracking development and bond strength deterioration are examined by experimental data available from various sources, and then matched with symptom-based lifetime Weibull model. The symptom reliability and remaining useful life are predicted from the predictive lifetime Weibull model for deteriorating concrete structures. Finally, a numerical example is provided to demonstrate the applicability of the proposed approach for forecasting the performance of concrete structures subject to reinforcement corrosion. The results show that the corrosion rate has significant impact on the reliability associated with serviceability and load bearing capacity of reinforced concrete structures during their service life.

• International Journal of Highway Engineering
• /
• v.16 no.1
• /
• pp.31-39
• /
• 2014

PURPOSES : Surface treatment material for pavement preventive maintenance should be inspected field applicability. This study(Part II) aimed to checkup coating characteristics and performance analysis using lab and field tests. The hydrophobic - low viscosity filling material for pavement preventive maintenance is presented in Part I, which is a series of companion study. METHODS : Relative comparison between general asphalt mixtures and surface treatment asphalt mixtures are analyzed and measured for the field application such as indirect tensile strength ratio(TSR), abrasion resistance, crack propagation resistance, temperature resistance, coating thickness, permeability resistance and skid resistance in terms of british pendulum number(BPN). RESULTS : It is found that TSR, crack propagation resistance and permeability resistance is increased as against uncoated asphalt specimen. Abrasion resistance and temperature resistance is secured from the initial coating thickness point of view, which is about 0.2~0.3mm. Skid resistance on the surface treatment pavement is satisfied with the BPN criteria of national highway because of exposed aggregate and crack sill induced pavement deterioration and damage cracks. CONCLUSIONS : The hydrophobic - low viscosity surface treatment material for pavement preventive maintenance is validated on field applicability evaluation based on quantitative analysis of coating thickness and performance analysis using lab and field tests.