• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.99-110
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• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.95-103
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• 2023

With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

• Steel and Composite Structures
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• v.43 no.4
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• pp.501-509
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• 2022

Cracks are common defects in concrete structures. Thus far, crack inspection has been manually performed using the contact inspection method. This manpower-dependent method inevitably increases the cost and work hours. Various non-contact studies have been conducted to overcome such difficulties. However, previous studies have focused on developing a methodology for non-contact inspection or local quantitative detection of crack width or length on concrete surfaces. However, crack depth can affect the safety of concrete structures. In particular, although macrocrack depth is structurally fatal, it is difficult to find it with the existing method. Therefore, an experimental investigation based on non-contact infrared thermography and multivariate machine learning was performed in this study to estimate the hidden macrocrack depth. To consider practical applications for inspection, an experiment was conducted that considered the simulated piloting of an unmanned aerial vehicle equipped with infrared thermography equipment. The crack depths (10-60 mm) were comparatively evaluated using linear regression, gradient boosting, and random forest (AI regression methods).

• Proceedings of the Korea Concrete Institute Conference
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• 2001.11a
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• pp.385-390
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• 2001

The thermal crack in mass concrete is mainly due to the difference of concrete temperature, which is generated by hydration heat of cement. As the thickness of mat foundation increases, the difference of temperature becomes bigger. The purpose of this study is to estimate the optimum placing depth. The temperature of real mat foundation was observed and the thermal analysis by Finite Element Method was executed. Finally, the crack index according to the placing depth was estimated.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.81-82
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• 2016

Cracks are inherent to concrete by its nature. The various size and shape of cracks induce deterioration of reinforced concrete structures including nuclear power plants. The wider and deeper the crack is, the concrete structures are more vulnerable to carbonization. Thus, it is essential to develop a reliable measurement technique of cracks inside concrete. In this study, an ultrasonic test method is applied to the crack measurements. The results can be used for evaluation of existing reinforced concrete structures.

• International Journal of Concrete Structures and Materials
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• v.10 no.4
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• pp.407-424
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• 2016

This study tests ten full-size simple-supported beam specimens with the high-strength reinforcing steel bars (SD685 and SD785) using the four-point loading. The measured compressive strength of the concrete is in the range of 70-100 MPa. The main variable considered in the study is the shear-span to depth ratio. Based on the experimental data that include maximum shear crack width, residual shear crack width, angle of the main crack and shear drift ratio, a simplified equation are proposed to predict the shear deformation of the high-strength reinforced concrete (HSRC) beam member. Besides the post-earthquake damage assessment, these results can also be used to build the performance-based design for HSRC structures. And using the allowable shear stress at the peak maximum shear crack width of 0.4 and 1.0 mm to suggest the design formulas that can ensure service-ability (long-term loading) and reparability (short-term loading) for shear-critical HSRC beam members.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.169-174
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• 2001

This study concerns crack effect on concrete anchor system and prediction of tensile capacity, as governed by concrete cone failure, of single anchors located at center of concrete specimen. To Investigate crack effect three different types of crack such as crack width of 0.2mm and 0.5nm, crack depth of loom and 20cm, and crack location of center and biased point were simulated. The static tensile load was subjected to 7/8 in. CIP anchor embedded in concrete of strength 280kg/$cm^{2}$. Tested pullout capacity was compared to prediction value by each current design method (such as ACI 349-97, ACI 349 revision and CEB-FIP which is based on CC Method), In these comparison CC Method and ACI revision showed almost same value in uncracked concrete specimen, however in cracked concrete CC Method showed conservativeness. Therefore the design by ACI 349 revision is recommended for the safe and economic design.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.247-253
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• 2007

Determination of crack depth in filed using the self-calibrating surface wane transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A Principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural networks (NNs) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

• Computers and Concrete
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• v.24 no.4
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• pp.283-293
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• 2019

The limited availability of raw materials and increasing service demands for pavements pose a unique challenge in terms of pavement design and concrete material selection. The self-compacting rubberized concrete (SCRC) can be used in pavement design. The SCRC pavement slab has advantages of excellent toughness, anti-fatigue and convenient construction. On the premise of satisfying the strength, the SCRC can increase the ductility of pavement slab. The aim of this investigation is proposing a new method to predict the crack growth and flexural capacity of large-scale SCRC slabs. The mechanical properties of SCRC are obtained from experiments on small-scale SCRC specimens. With the increasing of the specimen depth, the bearing capacity of SCRC beams decreases at the same initial crack-depth ratio. By constructing extended finite element method (XFEM) models, crack growth and flexural capacity of large-scale SCRC slabs with different fracture types and force conditions can be predicted. Considering the diversity of fracture types and force conditions of the concrete pavement slab, the corresponding test was used to verify the reliability of the prediction model. The crack growth and flexural capacity of SCRC slabs can be obtained from XFEM models. It is convenient to conduct the experiment and can save cost.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.133-137
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• 2012

Cracking may be used to help predict the cause of deterioration of concrete, since in many cases characteristic cracking patterns are produced. The purpose of this paper is an analysis of the crack cause occurred in concrete faced rockfill dams. We analyzed the concrete placement methods, cracking pattern, the inspection of crack depth by the ultrasonic pulse velocity method, and the measurement of heat of hydration, environmental condition, and so on. In this study, the crack cause of concrete faced rockfill dam is the wrong method of concrete placement, high temperature difference by cement of heat of hydration and concrete of drying shrinkage.