Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network

Ding, Hao; Jiang, Xinghong; Ke, Li; Guo, Hongyan; Li, Wenfeng

doi:10.1155/2020/8838216

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…BP neural networks have powerful regression analysis capabilities and can predict quite accurate results after only a certain amount of data training [ 19 ].…”

Section: Machine Learning-based Approach To Teaching Physical Healthmentioning

confidence: 99%

Development of Machine Learning-Based Ideas for Teaching Physical Education and Health

Yin

Han

2022

BioMed Research International

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With various social pressures and the lack of knowledge about physical health, students have poor physical education quality and insufficient knowledge acquisition about physical health. Traditional physical health teaching is a process in which the teacher tells the theory of physical health and students passively accept it, which leads to physical health problems such as low learning efficiency of students’ physical health knowledge and low interest in learning physical health knowledge. With the emphasis on physical health teaching and the development of technologies such as machine learning, machine learning is used to analyze the problems of physical health teaching and help students to learn physical health better to improve the efficiency of physical health teaching. The results of this paper show that the machine learning-based physical education and traditional physical education can reduce the injury rate of students’ sports by 7.7% compared with traditional physical education, make students’ interest in physical education and health learning reach 53.3%, and improve the efficiency of physical education and health learning. There is a degree of students’ acquisition of physical health knowledge. The change from traditional physical health teaching ideology to machine learning-based physical education ideology can improve the teaching efficiency of physical health teaching, allow students to acquire more physical health knowledge, and effectively reduce the risk of students’ injuries in sports.

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“…BP neural networks have powerful regression analysis capabilities and can predict quite accurate results after only a certain amount of data training [ 19 ].…”

Section: Machine Learning-based Approach To Teaching Physical Healthmentioning

confidence: 99%

Development of Machine Learning-Based Ideas for Teaching Physical Education and Health

Yin

Han

2022

BioMed Research International

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“…Various hyperparameters were used in the training process of the SRGAN and segmentation network because the SRGAN has a structure wherein the generator and discriminator compete with each other for learning. Two types of functions, namely the discriminator loss function (L D ) and generator loss function (L G ), were used for the loss function, with the discriminator loss function defned as in equation (3). Furthermore, the binary cross-entropy equation was used for L d,real and L d,fake .…”

Section: Methodsmentioning

confidence: 99%

“…Tunnel cracks may lead to future leakage and damage, signifcantly degrading the durability of the tunnel [1,2]. In addition, they can be considerably afected by environmental changes [3] and can even cause accidents. Terefore, cracks require regular and precise maintenance.…”

Section: Introductionmentioning

confidence: 99%

Learning Structure for Concrete Crack Detection Using Robust Super-Resolution with Generative Adversarial Network

Kim

Shim

Kang

et al. 2023

Structural Control and Health Monitoring

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To develop countermeasures against the inevitable aging of tunnels, accurate inspection is critical for ensuring stable tunnel services. Tunnels are large-scale infrastructures, whereas the cracks in such tunnels are small-scale objects, necessitating the development of methods capable of rapidly detecting cracks in a wide field of view. Therefore, this study proposes a new learning structure based on a segmentation network. This structure includes super-resolution and generative adversarial networks, which contribute to improved detection performance with robustness to input data. Furthermore, a method for the effective construction of training data for the application of the proposed structure is presented. Subsequently, the performance of the method over 1,606 crack images with randomly degraded qualities is evaluated. The proposed structure presents improved crack intersection over union and F1-scores of 63.686% and 77.811%, respectively, and low variances of 0.9008 and 0.5015 compared to the original structure. The results presented herein indicate the possible application of the proposed accurate condition inspection technology to tunnel maintenance in the future.

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“…In 1990, Picard et al 27 found that the division ratio of the training set, validation set, and training set, although not mandatory but has a certain impact on the evaluation performance, as can be seen from a large number of training studies on neural networks in recent years, most researchers have used a ratio of 80% for the training set and 20% for the rest. 30,31 For BP-ANN and its derivative models, this paper uses the ratio of the training set, validation set, and test set 8:1:1 to divide them, and for other neural network models, this paper uses the ratio of the training set to test set 8:2 to divide them.…”

Section: Description Of the Poa-bp Methodology And Comparison Of Trai...mentioning

confidence: 99%

“…From researchers’ studies, 30,31 it can be seen that the number of layers of the hidden layer structure of the neural network affects the prediction accuracy of the overall model as well as the computing time, especially for the prediction of the processing parameters of a very complex mechanical structure such as a pure electric vehicle power pack, it is more necessary to increase the number of layers of the structure of the neural network to improve the accuracy of the prediction, but too many layers will increase the difficulty of the calculation. This paper set up a single input layer with an output layer and increase the single hidden layer in order according to a sequence of equal differences with a tolerance of 1.…”

Section: Description Of the Poa-bp Methodology And Comparison Of Trai...mentioning

confidence: 99%

Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

Gao

Liu

et al. 2022

Advances in Mechanical Engineering

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With the rapid development of artificial neural networks, more sophisticated network models and more accurate prediction results are provided for solving engineering applications. In this paper, the weights and thresholds of the feedforward neural network model were optimized using the pelican algorithm, and the optimal solution was output by simulating the pelican predation scheme and assigned as the new parameters of the neural network. A POA-BP network model was proposed, and its better prediction was demonstrated by comparing the fitting and prediction performance with 13 neural network models such as random forest, support vector machine, and wavelet basis by evaluating metrics such as RMSE, MSE, and MAE. To further improve the prediction accuracy, different hidden layer topologies of POA-BP were compared, and the Monte Carlo method was used to obtain seven design variables for the lithium battery shell size parameters, and parameter regression prediction was performed for the structure after the variable density topology optimization used the isotropic material interpolation model (SIMP) with the moving asymptote method by invoking the MinGW-w64 compiler, and the 1-3-1 neural network was selected model to predict each dimension of the battery shell structure, the final shell weight reduction ratio was 18.12% and the first-order intrinsic frequency was increased by 14.56%.

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Intelligent Classification Method for Tunnel Lining Cracks Based on PFC-BP Neural Network

Cited by 11 publications

References 21 publications

Development of Machine Learning-Based Ideas for Teaching Physical Education and Health

Development of Machine Learning-Based Ideas for Teaching Physical Education and Health

Learning Structure for Concrete Crack Detection Using Robust Super-Resolution with Generative Adversarial Network

Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

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