Jatoliya Ajay, Bhattacharya Debayan, Manna Bappaditya, Bento Ana Margarida, Fazeres Ferradosa Tiago
Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
Department of Civil Engineering, Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.
Philos Trans A Math Phys Eng Sci. 2024 Jan 8;382(2264):20220403. doi: 10.1098/rsta.2022.0403. Epub 2023 Nov 20.
Scour phenomena remain a significant cause of instability in offshore structures. The present study estimates scour depths using physics-based numerical modelling and machine-learning (ML) algorithms. For the ML prediction, datasets were collected from previous studies, and the trained models checked against the statistical measures and reported outcomes. The numerical assessment of the scour depth has been also carried out for the current and coupled wave-current environment within a computational fluid dynamics framework with the aid of the open-source platform REEF3D. The outcomes are validated against the previously reported experimental studies. The results obtained from ML schemes demonstrated that the artificial neural network and adaptive neuro-fuzzy interface system models have an elevated level of effectiveness compared with the other models. Whereas the numerical analysis results show a good agreement against the reported values. For the current only conditions, the normalized scour depth (/) at the front and rear end of the pier is 0.65 and 0.81. For the wave-current conditions, the normalized scour depth (/) is 0.26. The study highlights the importance of machine learning and physics-based numerical modelling to assess scour depth within a reasonable time frame without compromising accuracy. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.
冲刷现象仍然是近海结构物失稳的一个重要原因。本研究使用基于物理的数值模拟和机器学习（ML）算法来估算冲刷深度。对于ML预测，从先前的研究中收集了数据集，并根据统计量度和报告的结果对训练好的模型进行了检验。在开源平台REEF3D的帮助下，还在计算流体动力学框架内对当前以及耦合的波浪 - 水流环境下的冲刷深度进行了数值评估。结果与先前报道的实验研究进行了验证。从ML方案获得的结果表明，与其他模型相比，人工神经网络和自适应神经模糊接口系统模型具有更高的有效性水平。而数值分析结果与报道值显示出良好的一致性。对于仅水流条件，桥墩前端和后端的归一化冲刷深度（/）分别为0.65和0.81。对于波浪 - 水流条件，归一化冲刷深度（/）为0.26。该研究强调了机器学习和基于物理的数值模拟在合理时间框架内评估冲刷深度而不影响准确性的重要性。本文是主题特刊“基于物理的机器学习及其结构完整性应用（第2部分）”的一部分。
