• Journal of the Korea Institute of Building Construction
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• v.15 no.2
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• pp.185-192
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• 2015

Expansion joint is the essential element of the bridge in many cases. When the bridge faces chloride of preventing freezing on the surface of the bridge, the expansion joints is damaged significantly, thus this reduces service life and increases maintenance cost of the bridge. As a solution of this problem, new technology using high ductile materials for the joint without expansion joint was developed and in this research, crack control performance, preventing leaking after the cracking, and chloride resistance were experimentally evaluated. As a result of the experiment, with PCM and FRC materials, the connecting joint suffered poor crack dispersion and severe damage by the chloride penetration while with high-ductile material, the connecting joint dispersed the tensile deformation to microcracks stably up to 7.5mm. Furthermore, under the sever conditions, the leaking was prevented and penetration of chloride ions was prevented after the crack occurred.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.197-207
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• 2023

Concrete is a widely used material due to its excellent compressive strength and durability. However, depending on the surrounding environment and the characteristics of the materials used in the construction, various defects may occur, such as cracks on the surface and subsidence of the structure. The detects on the surface of the concrete structure occur after completion or over time. Neglecting these cracks may lead to severe structural damage, necessitating regular safety inspections. Traditional visual inspections of concrete walls are labor-intensive and expensive. This research presents a deep learning-based semantic segmentation model designed to detect cracks in concrete walls. The model addresses surface defects that arise from aging, and an image augmentation technique is employed to enhance feature extraction and generalization performance. A dataset for semantic segmentation was created by combining publicly available and self-generated datasets, and notable semantic segmentation models were evaluated and tested. The model, specifically trained for concrete wall fracture detection, achieved an extraction performance of 81.4%. Moreover, a 3% performance improvement was observed when applying the developed augmentation technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1353-1358
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• 2010

In this paper, we propose a detection method that automatically detects concrete surface cracks using fuzzy method in the image of concrete surface cracks. First, the proposed method detecting concrete surface cracks detects the candidate crack areas by applying R, G, B channel values of the concrete crack image to fuzzy method. We finally detect cracks by the density information about the detected candidate areas after we remove the detailed noises on the image of the concrete surface cracks. The experiments using real concrete images showed that the proposed method is greatly improved of crack detection compared with the conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.35-42
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• 2020

In this study, we propose a crack detection method using limited data with a U-Net based image inpainting technique that is a modified unsupervised anomaly detection method. Concrete cracking occurs due to a variety of causes and is a factor that can cause serious damage to the structure in the long term. In general, crack investigation uses an inspector's visual inspection on the concrete surfaces, which is less objective in judgment and has a high possibility of human error. Therefore, a method with objective and accurate image analysis processing is required. In recent years, the methods using deep learning have been studied to detect cracks quickly and accurately. However, when the amount of crack data on the building or infrastructure to be inspected is small, existing crack detection models using it often show a limited performance. Therefore, in this study, an unsupervised anomaly detection method was used to augment the data on the object to be inspected, and as a result of learning using the data, we confirmed the performance of 98.78% of accuracy and 82.67% of harmonic average (F1_Score).

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.15-22
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• 2020

In this study, we propose an improvement method that can create U-Net model which detect fine concrete cracks by applying a weighted loss function. Because cracks in concrete are a factor that threatens safety, it is important to periodically check the condition and take prompt initial measures. However, currently, the visual inspection is mainly used in which the inspector directly inspects and evaluates with naked eyes. This has limitations not only in terms of accuracy, but also in terms of cost, time and safety. Accordingly, technologies using deep learning is being researched so that minute cracks generated in concrete structures can be detected quickly and accurately. As a result of attempting crack detection using U-Net in this study, it was confirmed that it could not detect minute cracks. Accordingly, as a result of verifying the performance of the model trained by applying the suggested weighted loss function, a highly reliable value (Accuracy) of 99% or higher and a harmonic average (F1_Score) of 89% to 92% was derived. The performance of the learning improvement plan was verified through the results of accurately and clearly detecting cracks.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.6
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• pp.38-44
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• 2019

A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

• Journal of the Korea Concrete Institute
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• v.16 no.1 s.79
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• pp.88-93
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• 2004

The objective of this study was to investigate applicability of epoxy injection method to cracked RC beams and structural behavior of repaired RC beams considering pre-loading conditions. For this purpose, five test beams were fabricated under two experimental variables. The main variables of this experimental study were pre-loading conditions and repair methods. The two pre-loading conditions were selected as $70\%$ and $90\%$ of nominal strength and the repair methods were to repair the cracked RC beams under free loading after crack and sustained loading. The comparative study was executed to evaluate effects of pre-loading conditions on the structural behavior of the cracked RC beams after crack-repair. The strains of reinforcement and concrete and deflections of beams at each loading step were measured and evaluated. As the results of this study, repair methods have much influence on structural behavior of epoxy injected RC beams and epoxy injection method for cracks of RC structures is appeared to be efficient.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.13-19
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• 2021

Cracks in asphalt pavement occur due to changes in weather or impact from vehicles, and if cracks are left unattended, the life of the pavement may be shortened, and various accidents may occur. Therefore, studies have been conducted to detect cracks through images in order to quickly detect cracks in the asphalt pavement automatically and perform maintenance activity. Recent studies adopt machine-learning models for detecting cracks in asphalt road pavement using a Convolutional Neural Network. However, their practical use is limited because they require high-performance computing power. Therefore, this paper proposes a framework for detecting cracks in asphalt road pavement by applying a small deep learning model applicable to mobile devices. The small deep learning model proposed through the case study was compared with general deep learning models, and although it was a model with relatively few parameters, it showed similar performance to general deep learning models. The developed model is expected to be embedded and used in mobile devices or IoT for crack detection in asphalt pavement.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.4
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• pp.40-47
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• 2022

The present study aimed to examine the effectiveness of different techniques for controlling the diagonal cracks at the corners of openings on the strength, deformation, and crack propagation in reinforced concrete walls. The crack control strip proposed in this study, the conventional diagonal steel reinforcing bars, and stress-dispersion curved plates were investigated for controlling the diagonal cracks at the opening corners. An additional crack self-healing function was also considered for the crack control strip. To evaluate the volume change ratio and crack width propagation around the opening, downsized wall specimens with a opening were tested under the diagonal shear force at the opening corner. Test result showed that the proposed crack control strip was more effective in reducing the volume change and controlling the crack width around the opening when compared to the conventional previous methods. The crack control strip with crack healing feature displayed the superior performance in improving the strength of the wall and reducing the crack width while healing cracks occurred in the previous tests.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.38-43
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• 2012

It is necessary to measure cracks on concrete slab surface accurately in concrete structure maintenance for the stability of the structure. However, in real world, the process is done by time consuming and ineffective manual inspection. Although there have been some studies to provide computerized inspection methods, they are vulnerable to rugged surface or noise due to the influence of the light or environmental reasons. In this paper, we propose a new method that extracts not only undistorted cracks but minute cracks that were often regarded as noise. We extract candidate crack areas by applying fuzzy method with R, G, and B channel values of concrete slab structure. Then further refinement processes are performed with SOM algorithm and density based cutoff to remove noise. Experiment verifies that the proposed method is sufficiently useful in various crack images.