Highway subgrade stability prediction model based on depth separation convolutional fusion network

The automatic data collection of monitoring collects abnormal information on the pavement in real time with the help of detection sensors. At present, it is impossible to grasp the quality monitoring of pavement in large-scale pavement construction and maintenance or to gradually form regular resear...

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Veröffentlicht in:	Journal of engineering and applied science (Online) Jg. 72; H. 1; S. 87 - 15
1. Verfasser:	Wang, Yubian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025 Springer Nature B.V SpringerOpen
Schlagworte:	Accuracy Artificial intelligence Asphalt pavements Civil Engineering Convolution Cracks Data collection Data structures Deep learning Defects Disease Efficiency Electrical Engineering Engineering Engineering Mathematics Errors Feature gradient algorithm Highway engineering Highway maintenance Image analysis Image feature processing Image processing Image quality Industrial Chemistry/Chemical Engineering Machine learning Mechanical Engineering Methods Monitoring Neural networks Object motion Optimization Pavement construction Pavements Prediction models Preventive maintenance Road beds Separated convolutional network Separation Subgrade defect Subgrades Vector spaces Subgrade defect Separated convolutional network Image feature processing Feature gradient algorithm Highway engineering
ISSN:	1110-1903, 2536-9512
Online-Zugang:	Volltext
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