A novel tunnel-lining crack recognition system based on digital image technology

Lei, Mingfeng; Liu, Linghui; Shi, Chenghua; Tan, Yuan-Sin; Lin, Yuexiang; Wei-dong, Wang

doi:10.1016/j.tust.2020.103724

Cited by 70 publications

(31 citation statements)

References 34 publications

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“…Based on the defects of data dimensionality reduction algorithms, a new nonlinear dimensionality reduction method is proposed. Firstly, the linear supervised LDA algorithm is used to find the nearest neighbor of each sample point, and then the local embedding features of the training set in low-dimensional space are obtained by calculating the local reconstruction weight matrix in the LLE algorithm [16], and finally, according to the characteristics of high-dimensional nonlinear images, the low-dimensional features of new samples are obtained by calculating the training set and its low-dimensional features. In this way, the algorithm can well establish a clear mapping between new sample points and their lowdimensional features through the relationship between high-dimensional space and low-dimensional space [17].…”

Section: Commodity Price Recognition Based On Imagementioning

confidence: 99%

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Liu

Wang

Bai

2021

Advances in Mathematical Physics

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Dimensionality reduction of images with high-dimensional nonlinear structure is the key to improving the recognition rate. Although some traditional algorithms have achieved some results in the process of dimensionality reduction, they also expose their respective defects. In order to achieve the ideal effect of high-dimensional nonlinear image recognition, based on the analysis of the traditional dimensionality reduction algorithm and refining its advantages, an image recognition technology based on the nonlinear dimensionality reduction method is proposed. As an effective nonlinear feature extraction method, the nonlinear dimensionality reduction method can find the nonlinear structure of datasets and maintain the intrinsic structure of data. Applying the nonlinear dimensionality reduction method to image recognition is to divide the input image into blocks, take it as a dataset in high-dimensional space, reduce the dimension of its structure, and obtain the low-dimensional expression vector of its eigenstructure so that the problem of image recognition can be carried out in a lower dimension. Thus, the computational complexity can be reduced, the recognition accuracy can be improved, and it is convenient for further processing such as image recognition and search. The defects of traditional algorithms are solved, and the commodity price recognition and simulation experiments are carried out, which verifies the feasibility of image recognition technology based on the nonlinear dimensionality reduction method in commodity price recognition.

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Section: Commodity Price Recognition Based On Imagementioning

confidence: 99%

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Liu

Wang

Bai

2021

Advances in Mathematical Physics

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“…Increasing the surface roughness of the aggregate can also help to reduce the segregation phenomenon [ 30 ]. In recent years, image recognition technology has become an effective technique for identifying the surface and internal structure of structures and is widely used for rapid crack detection, damage monitoring [ 31 ], and safety assessment of bridge structures, highways [ 32 ], and subway tunnels [ 33 ]. Image recognition techniques can also be used for the detection of homogeneity in vitrified concrete wall panels and can help to determine the distribution of vitrified particles in strong panel material [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Ceramsite Granules in Lightweight Concrete Panels through an Image Analysis Technique

Yang

Лю

et al. 2022

Materials

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Ceramsite particles are an important component of lightweight ceramsite concrete wall panels, and the density of the aggregate is much lower than the density of the slurry. It is generally accepted that there are inhomogeneities in the distribution of ceramsite particles in wall panels. Ceramsite concrete wallboard material is a research hotspot in the field of fabricated building materials at home and abroad; however, there is no effective way to quantify their inhomogeneity. Based on the application of image recognition technology in concrete homogeneity, a method to quantitatively evaluate the distribution of light aggregates in wall panels was developed. Three commercial lightweight vitrified concrete wall panels were cut into 324 cubes. The four cut surfaces of each specimen were photographed to analyze the proportion of ceramsite particle area, while the density, ultrasonic pulse velocity, and compressive strength of the specimens were tested. The results demonstrated that the image analysis method could effectively describe the homogeneity of the panels. The proportion of particle area of aggregate in the section of the cube had a strong correlation with the compressive strength, ultrasonic pulse velocity, and density, and there was an obvious linear relationship with the height of the plate where the cube was located. Based on this, the correlation equations of the proportion of particle area of aggregate, density, ultrasonic pulse velocity, compressive strength, and the height where the specimen was located were proposed. The quantitative parameters of the relevant properties of the wall panels were also obtained: the maximum difference between the proportion of particle area of the aggregate was 24%, the maximum difference between the density at the top and bottom of the wall panels was 115 kg/m3, and the maximum difference in the strength reached 5 MPa.

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“…Focusing on tunnels, structural conditions may not match the original design, due to several factors including 1) structural deformations and damages, voids, material deterioration [1], 2) water leakages as many tunnels are not water-proof [2], 3) frost damage mechanisms [3][4][5][6][7][8], 4) cracks from earthquakes [9], and 5) construction defects.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

Chiaia

Marasco

Aiello

2022

Front. Struct. Civ. Eng.

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In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.

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A novel tunnel-lining crack recognition system based on digital image technology

Cited by 70 publications

References 34 publications

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Quantification of Ceramsite Granules in Lightweight Concrete Panels through an Image Analysis Technique

Deep convolutional neural network for multi-level non-invasive tunnel lining assessment

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