Feature attention model for soil-geocomposite interface strength

Abstract

In geotechnical engineering, excess pore water pressure is one of the main reasons that cause structural instability. It greatly reduces the effective stress of soil and may lead to problems such as piping. At present, geocomposite drainage layers (GDL) are commonly used as effective drainage materials. However, it remains difficult to accurately predict the peak shear strength at the interface between soil and geocomposite drainage layers. The main reason is that many factors interact with each other, making the whole problem very complicated. This paper puts forward a deep learning model based on feature attention to predict the peak shear strength of the interface. The model uses residual connections to make deep features transfer better, and uses feature attention to automatically select and focus on important information. We verified the model with 209 sets of large scale direct shear test data. The results show that the Attention-DNN model has better prediction effect when dealing with geotechnical data that has a small sample size and large dispersion. On the test set, the model has an R 2 of 0.958 and an RMSE of 3.38 kPa, which is obviously better than the traditional DNN model. In order to make the research more useful in engineering, we also used Ridge regression and a dual threshold screening method to get an empirical formula. We tested this formula with 25 typical large scale direct shear test examples. The formula has a MAPE of 17.18% and shows good stability. It is more convenient for engineering technicians to apply. This study builds a complete and reliable technical system, which can provide scientific support for the interface design and safety evaluation of composite drainage systems in geotechnical engineering.