Application of Multimedia Information Processing in the Prediction of Geotechnical Parameters

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Xuất bản năm:	Journal of Cases on Information Technology vol. 27, no. 1 (2025), p. 1-22
Tác giả chính:	Ma, Lijuan
Được phát hành:	IGI Global
Những chủ đề:	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
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Miêu tả
Bài tóm tắt:	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.
số ISSN:	1548-7717 1548-7725 1098-8580 1537-937X
DOI:	10.4018/JCIT.388930
Nguồn:	ABI/INFORM Global