Synergies between numerical modelling and machine learning in geotechnical engineering

深度神经网络的训练需要包含数千个时间序列或图像的大数据集。每个数据集条目都必须贴上标签。例如，要使用卷积神经网络从照片中估计土壤颗粒的大小，数据集必须包括数千张具有相应颗粒大小的图像。与生成这些大型数据集相关的成本往往令人望而却步，特别是在岩土工程等专门领域。我们最近在颗粒材料侵蚀的数值模拟和从照片中确定颗粒尺寸分布(PSD)方面的工作突出了使用有限元或离散元模型来创建用于机器学习的大型合成数据集的潜力。我们研究计划的长期目标是创建将数值建模和深度学习相结合的新工作流程，以开发岩土工程中的智能测试和传感方法。我们的第一个短期目标是通过更现实的数据和随机化来改善域转移。域转移问题是阻碍基于合成数据的网络训练的主要障碍，是由合成数据的理想化性质引起的。该研究计划的第二个目标是使用数值方法产生具有物理意义的二级神经网络输入。我们将使用三个实际问题来应用我们的方法。第一个问题是从照片中确定颗粒状材料的PSD。具有不同真实感水平的合成图像将被准备用卷积神经网络来预测PSD。第二个问题涉及仪器数据的分析。采用有限元方法对大坝和隧道进行数值模拟，得到与实际破坏机理相对应的合成孔压时间序列。最后一个例子涉及对尚普兰粘土的沉降预测。将使用遥感技术编制定居点地图。不同层次的信息(土地利用、地表地质、地下水位深度)将与一系列简单的有限元模型的输出相结合，以预测定居点。域转移问题对于所有涉及合成数据的应用都很重要。选择的三个问题都是重要的岩土工程应用，将受益于机器学习解决方案。PSD测定在许多工程领域(例如粉末技术)中很常见，而且仍然经常使用筛子进行。现在生成和存储大量检测数据相对容易。由于缺乏分析工具，这些数据很少被充分利用。最后，在气候变化的背景下，敏感粘土中的定居点是一个重大问题，已经引起了加拿大东部媒体的关注。这项研究计划还将为培养高素质的岩土工程师提供机会。

The training of deep neural networks requires large datasets with thousands of time series or images. Each dataset entry must be labelled. For example, to estimate the size of soil particles from photographs with a convolutional neural network, the dataset must include thousands of images with corresponding particle sizes. The costs associated with the generation of these large datasets are often prohibitive, especially in specialized fields such as geotechnical engineering. Our recent work on the numerical modelling of erosion in granular materials and the determination of particle size distributions (PSD) from photographs has highlighted the potential of using finite-element or discrete-element models for the creation of large synthetic datasets for machine learning. The long-term objective of our research program is to create new workflows that combine numerical modelling and deep learning for the development of intelligent testing and sensing methods in geotechnical engineering. Our first short-term objective is to improve domain transfer through more realistic data and randomization. Domain transfer issues, the main barriers preventing network training based on synthetic data, are caused by the idealized nature of synthetic data. The second objective of the research program is to use numerical methods to generate secondary neural network inputs with physical meaning. Three practical problems will be used to apply our methods. The first problem is the determination of the PSD of granular materials from photographs. Synthetic images with different levels of realism will be prepared to predict the PSD with convolutional neural networks. The second problem concerns the analysis of instrumentation data. Synthetic pore pressure time series corresponding to realistic failure mechanisms will be created with the finite element method for dams and tunnels. The last example concerns the prediction of settlements for Champlain clays. Settlements maps will be prepared using remote sensing. Different layers of information (land use, surface geology, water table depth) will be combined with the output of a series of simple finite element models to predict settlements. Domain transfer problems are important for all applications involving synthetic data. The three problems that were chosen are important geotechnical applications that would benefit from machine learning solutions. PSD determinations are common in many fields of engineering (e.g. powder technology) and still often conducted with sieves. It is now relatively easy to generate and to store large amount of instrumentation data. These data are seldom used to their full potential because of a lack of analysis tools. Finally, in the context of climate changes, settlements in sensitive clays are a significant problem that is already attracting media attention in Eastern Canada. This research program will also provide an opportunity for the training of highly qualified geotechnical engineers.