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- W3208340571 abstract "Underground cut-off walls are widely used in various applications to hinder groundwater flow and contaminant transportation. Although the cut-off walls are installed with due discretion, occasional construction errors were found to be inevitable, causing significant leakage or even failures. An on-site risk assessment tool is needed, to map the input construction error and water-tightness performance of the cut-off walls quickly and accurately. Although a highly efficient physical-based algorithm (three-dimensional discretized algorithm, TDA) is developed, its calculation speed may not suffice to serve as an instant on-site evaluation tool. In this regard, a direct relationship between the random seeds of positioning errors and seepage flow rate is expected to be explored, and based on this, a surrogate model using the AI approach is established. A novel physical-based Neural Network (NN) model is proposed to train NN from a more physical and interpretative perspective. The proposed physical-based NN is reasonably accurate but much more efficient than the benchmark physical-based method (TDA), and the prediction accuracy and result interpretability is also superior than the traditional NN." @default.
- W3208340571 created "2021-11-08" @default.
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- W3208340571 date "2021-10-01" @default.
- W3208340571 modified "2023-09-26" @default.
- W3208340571 title "The fusion of physical mechanism and artificial intelligence – A case study of water-tightness estimation for geometrically imperfect cut-off walls" @default.
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- W3208340571 doi "https://doi.org/10.1088/1755-1315/861/7/072054" @default.
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