Abstract
ABSTRACT This research proposes a novel approach leveraging deep learning techniques to enhance scour severity prediction that aims to analyse various parameters’ effects on scour severity and scour patterns around piers under different bed conditions. The methodology involves integrating deep learning techniques, including Linear Discriminant Analysis-t-Distributed Stochastic Neighbour Embedding (LDA-t-SNE) and Bayesian Neural Networks (BNNs), to extract features, reduce dimensionality, and improve detection accuracy. This study conducted correlation analysis to understand the relationships between parameters such as drainage area, stream slope, pier characteristics, flow dynamics, and sediment properties and their influence on scour severity. Additionally, sensitivity analysis was performed to assess the impact of different pier shapes and bed conditions on scour severity. Our results demonstrate the effectiveness of the proposed approach, with metrics such as RMSE (%) values of 0.025 and MAE (%) values of 0.011 and 0.01, respectively, outperforming traditional scour detection methods, achieving an accuracy of 98% consistently affirming the superior accuracy and reliability of the model. This research provides valuable insights into proactive scour management and infrastructure resilience, offering practical solutions for safeguarding hydraulic structures against scour-induced risks in civil engineering applications.
Published Version
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