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Application of Multimedia Information Processing in the Prediction of Geotechnical Parameters

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Publicado en:Journal of Cases on Information Technology vol. 27, no. 1 (2025), p. 1-22
Autor principal: Ma, Lijuan
Publicado:
IGI Global
Materias:
Soils
Data processing
Particle swarm optimization
Information processing
Tomography
Deep learning
Poisson's ratio
Optimization
Function words
Void ratio
Machine learning
Medical imaging
Compression
Recurrent
Geology
Multimedia
Peat
Neural networks
Infrastructure
Artificial intelligence
Design engineering
Predictions
Computed tomography
Decision making
Engineering
Algorithms
Acoustic emission
Acoustics
Information technology
Parameters
X-rays
Acceso en línea:Citation/Abstract
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Resumen:Key performance parameters of geotechnical materials significantly impact engineering design and construction. To address challenges in measuring certain parameters, this study proposed a prediction method based on multimedia information processing and deep learning. Acoustic emission and computed tomography scan data was processed to extract features related to Poisson's ratio, the void ratio, the density, and the compression modulus of peat soil. A gated recurrent unit neural network optimized by the particle swarm optimization algorithm was employed for parameter prediction. The results showed that the “particle swarm optimization-gated recurrent unit” model effectively predicted these parameters, with the best performance in predicting the compression modulus and the weakest for the void ratio. This approach provides a novel and reliable method for acquiring and verifying geotechnical parameters.
ISSN:1548-7717
1548-7725
1098-8580
1537-937X
DOI:10.4018/JCIT.388930
Fuente:ABI/INFORM Global