Breakthroughs in deep learning in 2006 triggered numerous cutting-edge innovations in text processing, speech recognition, driverless cars, disease diagnosis, and so on. This project will utilize the core concepts underlying the recent computer vision innovations to address a rarely-discussed, yet urgent issue in engineering: how to analyze the explosively increasing image data including images and videos, which are difficult to analyze with traditional methods. These concepts will be employed to explore the possibility of accurately assessing the safety of retaining walls with image data. This effort aims at setting up a paradigm for connecting engineering disciplines to artificial intelligence and enhancing the safety of geosystems as an essential infrastructure component by enabling their analysis with image-data-driven deep learning. The project will help revitalize traditional artificial intelligence sub-areas in geotechnical and other engineering areas, just as deep learning rekindled the interest in artificial neural networks and machine learning, and turned them into leading players in STEM research and innovations. The project may also change engineers' opinions regarding how to create knowledge with a revolutionary way attributed to deep learning, i.e., learning directly from data instead of indirectly from models established based on the data. Innovative education and outreach effort will be made by means of developing a mobile app to disseminate the idea and products of the project. The project will contribute to education by outreaching to K-12 students, underrepresented groups, and geotechnical engineering researchers and practitioners with the project products including the app at various events at the PIs' institution and professional conferences.
The goal of this study is to understand the image-data-driven deep learning in geosystems with an exploratory investigation into the stability analysis of retaining walls. To achieve the goal, the recent breakthroughs in computer vision, which were later used as one of the core techniques in the development of Google's AlphaGo, will be studied for its capacity in assessing the stability of a typical geosystem, i.e., retaining walls. The core concept enabling machines to surpass humans in visual classification capacity, i.e., convolutional neural nets (CNN), will be used to process the big data in geotechnical engineering, which primarily consist of still and live images (videos), that cannot be readily analyzed using traditional geotechnical engineering methods. Conventional neural nets will be used to analyze images for retaining walls to tell whether a wall is safe or failed. For quantitative analysis, 2D and 3D images for retaining walls will be generated using stochastic methods and analyzed using traditional limit analysis and numerical methods for labeling. These labeled image data will be used as input to train convolutional neural nets for supervised learning. The trained nets will be tested against another independent set of data generated in the same way as the training data. Three research tasks will be conducted in this project: 1) understanding the data science for image-data-driven geotechnical engineering research, 2) investigating the connections between those image patterns in deep learning and the physical mechanisms, and 3) revealing the robustness and extrapolation capacity of the deep learning approach in the stability analysis of retaining walls.
