A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

Han, Shuai; Li, Heng; Li, Mingchao; Rose, Timothy

doi:10.3390/app9173484

Cited by 12 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The function based on CNN algorithm was implemented in this robot by referring to the existing research [13,14] to judge the integrity of the pipe structure. It differed from the existing ones in that a linear actuator is attached to the end of the robot arm to enable hammering with constant intensity.…”

Section: Pipe Inspection With Echo Sound Analysismentioning

confidence: 99%

Development of quadruped robot for inspection of underground pipelines in nuclear power plants

Jang

Seol

Lee³

et al. 2021

Electronics Letters

View full text Add to dashboard Cite

Structural integrity of seawater pipelines in nuclear power plants is a very important issue. In accordance with the operating technical guidelines, the human operators directly enter the pipe and inspect it at every maintenance test. However, in this regard, safety issues such as narrow space and harmful gas are emerging every year. In response to these needs, a quadruped robot that can inspect underground pipes and assist workers has been developed. The robot has an articulated robotic arm that can receive an impact sound of hammering a pipe wall to test pipe integrity. The state of the pipe was examined using a Convolutional Neural Network algorithm. On the other hand, moving in a plumbing environment requires stable walking ability. To determine the gait sequence, a hierarchical gait controller is proposed. The hybrid controller, which consists of joint impedance and torque control, calculated from Model Predictive Control, can switch the gait modes comparing the reference and the current foot contact condition at each control cycle.

show abstract

Section: Pipe Inspection With Echo Sound Analysismentioning

confidence: 99%

Development of quadruped robot for inspection of underground pipelines in nuclear power plants

Jang

Seol

Lee³

et al. 2021

Electronics Letters

View full text Add to dashboard Cite

show abstract

“…In the field of crack detection, researchers have developed new models based on the ResNet module, which have yielded remarkable outcomes. Wang et al [35] proposed a detection method for bridge pavement crack detection based on the InceptionResNet-v2 [36] module. This method can capture multi-scale features of cracks, improving efficiency without pre-training.…”

Section: Introductionmentioning

confidence: 99%

Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network

Wang,

Zhou,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResNet-18 residual network was developed. This method was implemented by training a model with images to extract the cracks, where the image processing algorithms and deep learning were combined. The results show that the computational accuracy can meet the requirements by utilizing the established image dataset and appropriate model hyperparameters. The trained model had high recognition accuracy when the 256 × 256 resolution images were adopted, and the worst accuracy of crack recognition in the test set was over 90%. The average accuracy in the test set was 91.3% when considering environmental interference generated by processing the images with a brightness adjustment, salt-and-pepper noise, and localized interference. Then, it was demonstrated that the present model possesses good robustness for crack identification in different environments. The average recognition accuracy when dealing with images of a real bridge, which are outside the training dataset, was 99.7%. The residual network model developed in this study has the advantages of low cost, high efficiency, and practicality compared to traditional detection methods. Compared to the existing deep learning methods, the model created in this study requires less computational resources and storage space, and shows a faster training speed and higher accuracy.

show abstract

“…Many researchers are combining CNN with empirical methods or numerical simulation methods to address rock mechanics engineering problems, and the final results proved to be more scientific and optimized. Karimpouli et al [17][18][19][20] combined CNN to estimate rock physical properties. Chen et al [21][22][23] studied landslide automatic recognition with satellite imagery based on CNN.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks

Zhang

Liu

2022

Sensors

View full text Add to dashboard Cite

Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was adopted to guarantee the model inference efficiency. Four fundamental single class rock datasets: sandstone, shale, monzogranite, and tuff were collected. At the same time, multitype hybrid rock lithologies datasets were obtained based on data augmentation method. The proposed model was then trained on multitype hybrid rock lithologies datasets. Besides, for comparison purposes, the other three algorithms, were trained and evaluated. Experimental results revealed that our method exhibited the best performance in terms of precision, recall, and efficiency compared with the other three algorithms. Furthermore, the inference time of the proposed model is twice as fast as the other three methods. It only needs 11 milliseconds for single image detection, making it possible to be applied to the industry by transforming the algorithm to an embedded hardware device or Android platform.

show abstract

A Deep Learning Based Method for the Non-Destructive Measuring of Rock Strength through Hammering Sound

Cited by 12 publications

References 32 publications

Development of quadruped robot for inspection of underground pipelines in nuclear power plants

Development of quadruped robot for inspection of underground pipelines in nuclear power plants

Identification of the Surface Cracks of Concrete Based on ResNet-18 Depth Residual Network

A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks

Contact Info

Product

Resources

About