Non-destructive prediction techniques for asphalt mixture based on back propagation neural networks
ABSTRACTThe density of the asphalt mixture surface is an important indicator to evaluate the quality of construction and is of great importance to ensure the quality of construction and maintain the long-term serviceability of the pavement. The 3D ground-penetrating radar (3D GPR) and density prediction model can achieve rapid and continuous prediction of the density of asphalt pavement. In this study, a back propagation neural network (BPNN) density prediction model was established and combined with 3D GPR to conduct field experiments on a new asphalt concrete mid-surface layer to compare the density values obtained by the coring method in order to verify the model performance. The results show that the BPNN prediction model established in this study has good application to the overall density diagnosis of the road in the field. The average prediction error of the BPNN density prediction model is 0.184%, which is highly reproducible in practical applications.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/44544515
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Supplemental Notes:
- © 2023 Informa UK Limited, trading as Taylor & Francis Group 2023. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Meng, Yongjun
- Mo, Shuyi
- Li, Hui
- Yan, Tianyi
- Lei, Jiechao
- Fan, Liupeng
- Publication Date: 2023
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: 2253965
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Serial:
- International Journal of Pavement Engineering
- Volume: 24
- Issue Number: 1
- Publisher: Taylor & Francis
- ISSN: 1029-8436
- Serial URL: http://www.tandf.co.uk/journals/titles/10298436.html
Subject/Index Terms
- TRT Terms: Asphalt mixtures; Asphalt pavements; Backpropagation; Density; Ground penetrating radar; Neural networks; Paving; Predictive models; Quality control
- Subject Areas: Construction; Highways; Materials; Pavements;
Filing Info
- Accession Number: 01907961
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 13 2024 10:36AM