Image-Data-Driven Deep Learning in Geosystems

2006年深度学习的突破引发了文本处理、语音识别、无人驾驶汽车、疾病诊断等领域的众多前沿创新。本项目将利用最新计算机视觉创新的核心概念来解决工程中很少讨论但又迫切的问题：如何分析传统方法难以分析的爆炸性增长的图像数据，包括图像和视频。这些概念将被用于探索利用图像数据准确评估挡土墙安全性的可能性。这项工作旨在建立一个范例，将工程学科与人工智能联系起来，并通过使用图像数据驱动的深度学习进行分析，提高地球系统作为重要基础设施组成部分的安全性。该项目将有助于重振岩土工程和其他工程领域的传统人工智能子领域，正如深度学习重新点燃了人们对人工神经网络和机器学习的兴趣，并使它们成为STEM研究和创新的领军人物一样。该项目还可能改变工程师对如何以一种革命性的深度学习方式创造知识的看法，即直接从数据中学习，而不是间接从基于数据建立的模型中学习。创新的教育和推广工作将通过开发一个移动应用程序来传播项目的理念和产品。该项目将通过向K-12学生、代表性不足的群体、岩土工程研究人员和从业者推广项目产品，包括在pi机构和专业会议上举办的各种活动中的应用程序，为教育做出贡献。本研究的目的是通过对挡土墙稳定性分析的探索性调查来理解地球系统中图像数据驱动的深度学习。为了实现这一目标，将研究最近在计算机视觉方面取得的突破，这些突破后来被用作谷歌的AlphaGo开发的核心技术之一，以评估典型地质系统（即挡土墙）的稳定性。使机器在视觉分类能力上超越人类的核心概念，即卷积神经网络（CNN），将被用于处理岩土工程中的大数据，这些大数据主要由静止和实时图像（视频）组成，传统的岩土工程方法无法轻易分析。传统的神经网络将用于分析挡土墙的图像，以判断墙是安全的还是失败的。在定量分析方面，将采用随机方法生成挡土墙的二维和三维图像，并使用传统的极限分析和数值标记方法进行分析。这些标记的图像数据将用作训练卷积神经网络进行监督学习的输入。训练后的网络将针对与训练数据相同的方式生成的另一组独立数据进行测试。本项目将开展三项研究任务：1)理解图像数据驱动岩土工程研究的数据科学；2)研究深度学习中图像模式与物理机制之间的联系；3)揭示深度学习方法在挡土墙稳定性分析中的鲁棒性和外推能力。

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.