Deep Neural Network Analysis on Uplift Resistance of Plastic Greenhouses for Sustainable Agriculture

Abstract

In this study, we attempted to find an alternative method to identify and efficiently predict the interaction between the soil and basic structure of plastic greenhouses for sustainable agriculture. The interaction between the foundation structure of the plastic greenhouse and the soil appears as uplift resistance. We first measured the uplift resistance by using various artificial neural networks. The data required by the model were obtained through laboratory experiments, and a deep neural network (DNN) was employed to improve the model performance. We proposed a new deep learning structure called DNN-T that has the advantage of stabilizing neural circuits by suppressing feedback by using the concept of biological interneurons. The DNN-T was trained using driving data for four scenarios. The upward resistance of the DNN-T according to the training conditions showed a high correlation (r = 0.90), and the error decreased when the input conditions of the training data were varied. DNN-Ts mimicking interneurons can contribute to solving various nonlinear problems in geotechnical engineering. We believe that our DNN-T model can be used to determine the uplift resistance of solid and continuous pipe foundations, effectively reducing the need for time-consuming and extensive testing.