Dr. Zhen Liu Profile

Dr. Zhen Liu

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 June 2024 Paper

Intelligent recognition of subgrade damages combining GPR signals and CNN models

Qian Liu, Zhen Liu

Proceedings Volume 13180, 131805H (2024) https://doi.org/10.1117/12.3033597

KEYWORDS: General packet radio service, Education and training, Convolution, Feature extraction, Roads, Image segmentation, Cavitation, Artificial neural networks, Signal to noise ratio, Ground penetrating radar

Read Abstract +

Road collapse incidents resulting from subgrade damages are recurrent occurrences. The implementation of periodic nondestructive testing and assessment of road subgrades, facilitated by ground penetrating radar (GPR) technology, proves instrumental in timely identification of subgrade damages during their nascent stages, thereby furnishing essential groundwork for prompt intervention. Conventional interpretation of GPR-generated roadbed damage images predominantly relies on manual scrutiny, entailing inherent drawbacks such as misjudgments and suboptimal efficiency. For the extraction of GPR subgrade damage image features, this study proposes a methodology using convolutional neural networks (CNNs) to overcome these limitations. The suggested network design includes convolutional, pooling, and fully connected layers. The process initiates with input images undergoing feature extraction through five convolutional layers and five pooling layers. Subsequently, categorization is performed on the gathered features using the fully connected layer. The conclusive recognition outcomes are then generated through the softmax layer. Upon subjecting the model to a simulated dataset, comprising prevalent roadbed afflictions such as subgrade cavity inflation, subgrade cavity water filling, subgrade voiding, and subgrade aquifer issues, the proposed method achieves a commendable recognition accuracy of 93.48%. This empirical validation attests to the efficacy of the proposed approach in automated subgrade damage identification, marking a notable advancement in the field.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years