Spatio-temporal prediction of soil deformation in bucket excavation using machine learning

Saku, Yuki; Aizawa, Masanori; Ooi, Tatsuo; Ishigami, Genya

doi:10.1080/01691864.2021.1943521

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Findings through the present paper regarding neural-networkbased model order reduction for fluid flows can be applied to a wide range of problems in science and engineering, since fluid flows can be regarded as a representative example of complex nonlinear dynamical systems. Moreover, because the demand for model order reduction techniques can be found in various scientific fields including robotics [55], aerospace engineering [56,57], and astronomy [58], we can expect that the present paper can promote the unification of computer science and nonlinear dynamical problems from the perspective of application side. Our presentation is organized as follows.…”

Section: Introductionmentioning

confidence: 96%

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

et al. 2021

View full text Add to dashboard Cite

We investigate the capability of neural network-based model order reduction, i.e., autoencoder (AE), for fluid flows. As an example model, an AE which comprises of convolutional neural networks and multi-layer perceptrons is considered in this study. The AE model is assessed with four canonical fluid flows, namely: (1) two-dimensional cylinder wake, (2) its transient process, (3) NOAA sea surface temperature, and (4) a cross-sectional field of turbulent channel flow, in terms of a number of latent modes, the choice of nonlinear activation functions, and the number of weights contained in the AE model. We find that the AE models are sensitive to the choice of the aforementioned parameters depending on the target flows. Finally, we foresee the extensional applications and perspectives of machine learning based order reduction for numerical and experimental studies in the fluid dynamics community.

show abstract

Section: Introductionmentioning

confidence: 96%

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

et al. 2021

View full text Add to dashboard Cite

show abstract

“…With the rapid development of artificial intelligence, machine-learning methods have been used in geotechnical engineering. The long short-term memory (LSTM) model, an extension of the recurrent neural network, has been successively applied to deformation prediction [6,7]. Because the LSTM This research studied the soft clay from the fourth layer in Shanghai as a dynamic triaxial test object to observe the fundamental law of the dynamic strain response and obtain the training database for LSTM.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the axial strain development curve of soft clay under dynamic triaxial conditions based on LSTM

Xiao,

Wu,

Zhou

2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

The dynamic triaxial test results of soft clay show that a long-term cyclic traffic load lead to significant deformation of soft clay. Considering that the dynamic triaxial test data of soft clay under cyclic loading have apparent time series characteristics, the long short-term memory (LSTM) model can be used to predict the deformation law of soft clay. Through a dynamic triaxial test of Shanghai soft clay, time series data, such as axial stress, pore water pressure, and axial strain were obtained to establish the sample data of the LSTM model. The LSTM model is then used to predict the axial strain development curve. The results showed that the LSTM model had a good fitting precision for the sample data under different dynamic stresses and frequencies, and its accuracy was above 97% in all conditions. The prediction results can reproduce the fluctuation law of the axial strain under cyclic loads and can be used to study elastic and plastic strains. The research results can provide technical support for disaster prevention and control in soft clay.

show abstract

Generalization techniques of neural networks for fluid flow estimation

Morimoto

Fukami

Zhang

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

Spatio-temporal prediction of soil deformation in bucket excavation using machine learning

Cited by 5 publications

References 15 publications

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

Model Order Reduction with Neural Networks: Application to Laminar and Turbulent Flows

Prediction of the axial strain development curve of soft clay under dynamic triaxial conditions based on LSTM

Generalization techniques of neural networks for fluid flow estimation

Contact Info

Product

Resources

About