Deep Learning Convolutional Neural Network
This study presents the preliminary results for the use of Convolutional Neural Network 6 (CNN) in the stability analysis of geosystems. The long goal is to understand the image data7 driven deep learning in geosystems to explore the use of image data for the safety analysis of 8 infrastructure. For this goal, the core concept enabling the recent breakthroughs in computer vision, 9 CNN, which helped machines surpass humans in visual classification capacity, was used to process 10 the image data for the stability analysis of geosystems. The concept used in the famous Dogs vs. 11 Cats Kaggle challenge (www.kaggle.com) in which machine learning algorithms were used to 12 classify whether an image contains a dog or a cat, was employed. We will use CNN to analyze 13 images for a retaining wall to tell whether the wall is a "cat" (safe) or a "dog" (failed) For 14 quantitative analysis, 10000 2D images for retaining walls were generated using stochastic 15 methods and analyzed using a traditional limit analysis method, i.e. Bishop Simplified Method, 16 for labeling. 5/6 of these labeled image data were used as input to train CNNs for supervised 17 learning. The trained nets will be tested against another independent set of data (2000 images) 18 generated in the same way as the training data. The results indicated that the success rate can reach 19 97.2% 20