Synergies between numerical modelling and machine learning in geotechnical engineering

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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.