Knowledge-integrated deep learning for predicting stochastic thermal regime of embankment in permafrost region

Abstract

The warming and thawing of permafrost are the primary factors that impact the stability of embankments in cold regions. However, due to uncertainties in thermal boundaries and soil properties, the stochastic modeling of thermal regimes is challenging and computationally expensive. To address this, we propose a knowledge-integrated deep learning method for predicting the stochastic thermal regime of embankments in permafrost regions. Geotechnical knowledge is embedded in the training data through numerical modeling, while the neural network learns the mapping from the thermal boundary and soil property fields to the temperature field. The effectiveness of our method is verified in comparison with monitoring data and numerical analysis results. Experimental results show that the proposed method achieves good accuracy with small coefficient of variation. It still provides satisfactory accuracy as the coefficient of variation increases. The proposed knowledge-integrated deep learning method provides an efficient approach to predict the stochastic thermal regime of heterogeneous embankments. It can also be used in other permafrost engineering investigations that require stochastic numerical modeling.

Topics & Concepts