Automatic Recognition of Flow Plasticity of
Conditioned Soil&nbsp;Based
on Improved Faster R-CNN

LI Chen; LUO Hanbin; LIU Wenli; LIU Yang

doi:10.3973/j.issn.2096-4498.2022.02.012

Tunnel Construction >

2022 , Vol. 42 >Issue 2: 268 - 274

DOI: https://doi.org/10.3973/j.issn.2096-4498.2022.02.012

Automatic Recognition of Flow Plasticity of Conditioned Soil Based on Improved Faster R-CNN

LI Chen ,
LUO Hanbin ,
LIU Wenli ,
LIU Yang

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(School of Civil Engineering and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

Online published: 2022-03-03

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Abstract

The flow plasticity inspection by the worker is time-consuming and labor-intensive during earth pressure balance shield tunneling; thus, the results are heavily influenced by subjective state. As a result, an automatic method for detecting the flow plasticity of the conditioned soil is proposed. First, preprocessing techniques, such as cutting, recovering, and strengthening are used to construct a conditioned soil sample data set from real-time monitoring video images. Second, a conditioned soil detection network model is established to identify and classify the flow plasticity of the conditioned soil in the monitoring video, enabling automatic identification of the flow plasticity of the conditioned soil. Finally, the recommended method is applied in a project, and the proposed improved Faster R-CNN neural network method shows a good detection effect in three kinds of conditioned soils. The average accuracy, precision, and recall rates are 91.55%, 86.83%, and 87.42%, respectively, indicating that the recommended method should be widely used.

Key words： ; earth pressure balance shield; soil conditioning; flow plasticity; convolutional neural network

Cite this article

LI Chen , LUO Hanbin , LIU Wenli , LIU Yang .

Automatic Recognition of Flow Plasticity of Conditioned Soil Based on Improved Faster R-CNN

[J]. Tunnel Construction, 2022 , 42(2) : 268 -274 . DOI: 10.3973/j.issn.2096-4498.2022.02.012

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