• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3797-3822
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• 2020

The inspection of cracks on the surface of tunnel linings is a common method of evaluate the condition of the tunnel. In particular, determining the thickness and shape of a crack is important because it indicates the external forces applied to the tunnel and the current condition of the concrete structure. Recently, several automatic crack detection methods have been proposed to identify cracks using captured tunnel lining images. These methods apply an image-segmentation mechanism with well-annotated datasets. However, generating the ground truths requires many resources, and the small proportion of cracks in the images cause a class-imbalance problem. A weakly annotated dataset is generated to reduce resource consumption and avoid the class-imbalance problem. However, the use of the dataset results in a large number of false positives and requires post-processing for accurate crack detection. To overcome these issues, we propose a crack detection method using a ternary classifier. The proposed method significantly reduces the false positive rate, and the performance (as measured by the F1 score) is improved by 0.33 compared to previous methods. These results demonstrate the effectiveness of the proposed method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.80-89
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• 2017

The classical approaches to detect cracks are performed by experienced inspection professionals by annotating the crack patterns manually. Because of each inspector's personal subjective experience, it is hard to guarantee objectiveness. To solve this issue, automated crack detection methods have been proposed however the methods are sensitive to image noise. Depending on the quality of image obtained, the image noise affect overall performance. In this paper, we propose crack detection method using a convolutional encoder-decoder network to overcome these weaknesses. Performance of which is significantly improved in terms of the recall, precision rate and F-measure than the previous methods.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.1142-1147
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• 2004

In this study, two damage detection techniques are applied to the railway tunnel liner based on the static deformation data. Models based on uniform reduction of stiffness and smeared crack concept are both employed, and the efficiency and relative advantage are compared with each other. Numerical analyses are performed on the idealized tunnel structure and the effect of white noise, common in most measurement data, is also investigated to better understand the suitability of the proposed models. As a result, model 1 based on uniform stiffness reduction method is shown to be relatively insensitive to the noise, while model 2 with the smeared crack concept is proven to be easily applied to the field situation since the effect of stiffness reduction is rather small. Finally, real deformation data of a rail tunnel in which health monitoring system is in operation are introduced to find the possible damage and it is shown that the prediction shows quite satisfactory result.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.549-558
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• 2021

In order to keep infrastructures such as tunnels and underground facilities safe, cracks of concrete lining in tunnel should be detected by regular inspections. Since regular inspections are accomplished through manual efforts using maintenance lift vehicles, it brings about traffic jam, exposes works to dangerous circumstances, and deteriorates consistency of crack inspection data. This study aims to provide methodology to automatically extract cracks from tunnel concrete lining images generated by the existing tunnel image acquisition system. Specifically, we train a deep learning based semantic segmentation model with open dataset, and evaluate its performance with the dataset from the existing tunnel image acquisition system. In particular, we compare the model performance in case of using all of a public dataset, subset of the public dataset which are related to tunnel surfaces, and the tunnel-related subset with negative examples. As a result, the model trained using the tunnel-related subset with negative examples reached the best performance. In the future, we expect that this research can be used for planning efficient model training strategy for crack detection.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.239-244
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• 2006

Borehole radar reflection surveys were carried out in the horizontal borehole to detect EDZ while constructing the tunnel for the research facility of the nuclear waste disposal in Korea. The horizontal borehole has been bored at a length of 35 m from shelter to be parallel with the tunnel which would be planed. While the tunnel has been constructing with the explosive excavation, the borehole radar reflection surveys carried out 5 times with the interval of 2 or 4 days for monitoring EDZ. The most typical change of the reflection event resulted from the face of the wall of tunnel which had been produced newly by the excavation of the tunnel daily, EDZ has been detected with constructing images of difference between two measurement stages, and also the change of EDZ through the time has been done, which is due to the generation of crack and weakening of the rock strength of the face of the tunnel's wall near previous portion of the face of a blind end of tunnel according to explosive excavation.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.39-52
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• 2021

Purpose: Recently, due to increasing usage of high-tech equipment for facility inspection, the need of verifying high-tech equipment is being emphasized. Therefore, the purpose of this paper is to develop performance evaluation methodology of tunnel scanners that inspect tunnel facilities. Method: This paper describes literature reviews regarding the performance evaluation methodology of high-tech based equipment for facility inspection. Based on these investigations and expert advisory meetings, this paper suggests a performance evaluation methodology of tunnel scanner. Result: First evaluation indicator states minimum performance standards of tunnel scanners. Second evaluation indicator is related to tunnel scanner quality. Conclusion: The performance evaluation methodology can provide reliable equipment performance catalogues, helping users to make a proper selection of equipment. Also, developers of equipment can get authorized verification of performances, preventing poor maintenance of facilities.

• Tunnel and Underground Space
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• v.29 no.3
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• pp.184-196
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• 2019

This study aims to extract a 3D image of micro-cracks generated by hydraulic fracturing tests, using the deep learning method and X-ray computed tomography images. The pixel-level cracks are difficult to be detected via conventional image processing methods, such as global thresholding, canny edge detection, and the region growing method. Thus, the convolutional neural network-based encoder-decoder network is adapted to extract and analyze the micro-crack quantitatively. The number of training data can be acquired by dividing, rotating, and flipping images and the optimum combination for the image augmentation method is verified. Application of the optimal image augmentation method shows enhanced performance for not only the validation dataset but also the test dataset. In addition, the influence of the original number of training data to the performance of the deep learning-based neural network is confirmed, and it leads to succeed the pixel-level crack detection.