A physics-inspired machine learning approach for water-tightness estimation of defective cut-off walls with random construction errors

Abstract

Underground cut-off walls are widely used in various geotechnical applications to hinder groundwater flow, contaminant transportation and possibly heat conduction. Cut-off walls were usually found defective due to construction errors during the installation phase, leading to significant leakages in subsequent operation phase. Existing physics-based leakage evaluation approaches, such as the finite element analysis and three-dimensional discretized algorithm, are computationally expensive and may not satisfy the need for instant on-site leakage risk assessment. In this regard, a more efficient mapping between construction errors and performance of cut-off walls is highly demanded. A natural option for such mapping is the artificial intelligence approach. Several novel physics-inspired neural network models are proposed based on the well-designed physical layers with varying complexity, to strike a balance between benefits of machine learning and physical approaches. The result shows that introducing physical layers with clearer physical meaning helps mitigating overfitting problems, improving prediction accuracy, result interpretability and model capacity, at the price of increasing the calculation efficiency during training. An optimized degree of physical meaning clarity can be achieved to strike a balance between fitting effect and training computation cost.