Research on Crack Detection Algorithm of the Concrete Bridge Based on Image Processing

Wang, Yun; Zhang, Ju Yong; Liu, Jingxin; Zhang, Yin; Chen, Zhi Ping; Li, Chun Guang; He, Kai; Yan, Rui Bin

doi:10.1016/j.procs.2019.06.096

Cited by 40 publications

(13 citation statements)

References 4 publications

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“…Next, the original image is converted to a grayscale image. e Otsu algorithm [38] is used to segment the image to obtain a binary image of the cracks, as shown in Figure 4. Using different mesh sizes ε to segment the binary image, MAT-LAB R2017a is employed to obtain the mesh number N of covering cracks corresponding to different mesh sizes.…”

Section: Fractal Characteristics Of Fracturesmentioning

confidence: 99%

Fracture Performance of a Large-Stone Asphalt Mixture Based on a Monotonic Tensile Overlay Test

Wei

Liang

2021

Advances in Civil Engineering

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A large-stone asphalt mixture (LSAM) is usually used as the base course of asphalt pavement to prevent the generation of cracks. However, there are few studies on the fracture performance and crack resistance of LSAMs. Under the monotonic tensile loading mode, the overlay test (OT) was explored to investigate the influence of different test factors on the cracking resistance and fracture performance. The study results indicated that a change in temperature and aging results in a variation in fractal dimension, the fracture energy of crack growth is higher than that of crack initiation, and fracture energy is increased to a certain extent by decreased temperature, an increased loading rate or increased aging. Finally, a constitutive model is established based on the disturbed state concept (DSC), and the proposed constitutive model is consistent with the test results.

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Section: Fractal Characteristics Of Fracturesmentioning

confidence: 99%

Fracture Performance of a Large-Stone Asphalt Mixture Based on a Monotonic Tensile Overlay Test

Wei

Liang

2021

Advances in Civil Engineering

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“…As nondestructive methods for SHM have become dominant in recent years, different methods have been investigated for concrete SHM such as ultrasonic [ 5 , 17 , 18 ], vibration [ 19 , 20 ], image processing [ 3 , 21 , 22 ], and acoustic emission (AE) [ 4 , 12 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Even though ultrasonic testing can detect internal defects and their sizes, it is susceptible to complicated part geometry and certain materials, such as austenitic steel, which can mask defects by causing attenuation.…”

Section: Introductionmentioning

confidence: 99%

A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

Nguyen

Ahmad

Kim

2022

Sensors

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This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen’s useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback–Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen’s work.

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“…For investigating concrete beams, visual monitoring is often used as the most basic technique; however, it cannot be efficiently performed if the location is not accessible/visible. Many studies have been proposed with different non-destructive approaches: the image processing technique [2][3][4], ultrasonic [5][6][7], vibration [8][9][10], acoustic emission [11][12][13][14], etc. Among them, the prominence of acoustic emission (AE) techniques has increased in recent years as a solution for concrete monitoring and for general prognosis and health management (PHM) frameworks.…”

Section: Introductionmentioning

confidence: 99%

A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

Nguyen

Ahmad

Kim

2021

Sensors

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In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.

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Research on Crack Detection Algorithm of the Concrete Bridge Based on Image Processing

Cited by 40 publications

References 4 publications

Fracture Performance of a Large-Stone Asphalt Mixture Based on a Monotonic Tensile Overlay Test

Fracture Performance of a Large-Stone Asphalt Mixture Based on a Monotonic Tensile Overlay Test

A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures

A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

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