Automatic Identification and Location of Tunnel Lining Cracks

Wang, Pengyu; Wang, Shuhong; Jierula, Alipujiang

doi:10.1155/2021/8846442

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…P. Wang et al. (2021) used the SSD algorithm to extract the characteristics of tunnel lining cracks and subsequently avoid the influence of noise. Luo et al.…”

Section: Related Workmentioning

confidence: 99%

“…With the emergence of the CNN-based target detection algorithm, the accuracy of tunnel defect identification is further improved. P. Wang et al (2021) used the SSD algorithm to extract the characteristics of tunnel lining cracks and subsequently avoid the influence of noise. Luo et al (2021) proposed a panoramic surface damage detection network to achieve the detection of the damage to multiple steel surfaces.…”

Section: Related Workmentioning

confidence: 99%

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Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Zhou

Zhang

Gong

2022

Computer aided Civil Eng

121

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Aiming to solve the challenges of low detection accuracy, poor anti-interference ability, and slow detection speed in the traditional tunnel lining defect detection methods, a novel deep learning-based model, named You Only Look Once network v4 enhanced by EfficientNet and depthwise separable convolution (DSC; YOLOv4-ED), is proposed. In the YOLOv4-ED, EfficientNet is used as the backbone to improve the identification accuracy of indistinguishable defect targets in complex tunnel background and light conditions. Furthermore, DSC block is introduced to reduce the storage space of the model and thereby enhance the detection efficiency. The experimental results indicate that the mean average precision, F1 score, Model Size, and FPS of YOLOv4-ED are 81.84%, 81.99%, 49.3 MB, and 43.5 f/s, respectively, which is superior to the comparison models in both detection accuracy and efficiency. Based on robust and cost-effective YOLOv4-ED, a tunnel lining defect detection platform (TLDDP) with the capacity of automated inspection of various lining defects (i.e., water leakage, crack, rebar-exposed) is built. The established TLDDP can realize the high-precision and automatic detection of multiple tunnel lining defects under different lighting and complex background conditions of the practical in-service tunnel.

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“…P. Wang et al. (2021) used the SSD algorithm to extract the characteristics of tunnel lining cracks and subsequently avoid the influence of noise. Luo et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Zhou

Zhang

Gong

2022

Computer aided Civil Eng

121

View full text Add to dashboard Cite

show abstract

“…e crack skeleton usually has a certain degree of discontinuity, and the crack image after the edge is extracted by the Sobel edge detection operator does not conform to the actual situation, so the crack needs to be filled. e direction vector crack connection algorithm proposed in the literature [3] is used to connect the broken points of the processed image, and the final result is shown in Figure 7.…”

Section: Breakpoint Connection and Image Fillingmentioning

confidence: 99%

“…en, according to the obtained tunnel lining image, a detection method based on computer vision is used to identify the cracks in the image. Compared with the artificial tunnel crack detection method, the crack detection method based on computer vision has the advantages of fast recognition speed and high detection accuracy and has been widely used in tunnel crack detection [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Tunnel Lining Crack Recognition Based on Improved Multiscale Retinex and Sobel Edge Detection

Wang

Zhang

Jiang

et al. 2021

Mathematical Problems in Engineering

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China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.

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“…Among these problems, cracking of road tunnel lining caused by high-water pressure has become one of the most serious factors affecting tunnel safety and has also attracted the attention of the industry [1,2]. Among the tunnel lining cracking include, arch wall cracking, high pressure water injection and elevation arch bulging [3][4][5][6]. The bulging roadbed of the road tunnel will not only seriously affect the service life of the tunnel, but also is a great safety hazard for tra c safety.…”

Section: Introductionmentioning

confidence: 99%

FEM analysis of a new three-way drainage and pressure reduction system for road tunnels

Teng

Liu

Mei

et al. 2023

Preprint

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For the water-rich area tunnel elevation arch in the high-water pressure often causes elevation arch cracking and leakage, bulging and other disasters. When the drainage system is not designed properly, such disasters occur more frequently, and conventional road tunnel drainage cannot effectively reduce the water pressure at the elevation arch. Therefore, this paper proposes a new concept of "three-way drainage". The three-way drainage system is based on the conventional drainage system with a new drainage inlet at the elevation arch. On this basis, a series of numerical simulation studies are conducted to verify the pressure-reducing performance of the three-way drainage system on the lining. After demonstration and analysis, the three-way drainage concept can not only effectively reduce the water pressure at the elevation arch of the tunnel, but also have a significant effect on the overall drainage effect of the tunnel. The factors affecting the performance of the three-way drainage system are discussed by varying the model parameters. It was found that the hydraulic conduction coefficient of the surrounding rock and initial support, the number of reverse diversion holes in the elevation arch, the change in head height and the change in secondary lining parameters all had a significant effect on the water pressure outside the tunnel.

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Automatic Identification and Location of Tunnel Lining Cracks

Cited by 10 publications

References 22 publications

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network

Tunnel Lining Crack Recognition Based on Improved Multiscale Retinex and Sobel Edge Detection

FEM analysis of a new three-way drainage and pressure reduction system for road tunnels

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