Predicting wave-induced liquefaction around submarine pipelines is crucial for marine engineering safety. However, the complex of interactions between ocean dynamics and seabed sediments makes rapid and accurate assessments challenging with traditional numerical methods. Although machine learning approaches are increasingly applied to wave-induced liquefaction problems, the comparative accuracy of different models remains under-explored. We evaluate the predictive accuracy of four classical machine learning models: Gradient Boosting (GB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF). The results indicate that the GB model exhibits high stability and accuracy in predicting wave-induced liquefaction, due to its strong ability to handle complex nonlinear geological data. Prediction accuracy varies across output parameters, with higher accuracy for seabed predictions than for pipeline surroundings. The combination of different input parameters significantly influences model predictive accuracy. Compared to traditional finite element numerical methods, employing machine learning models significantly reduces computation time, offering an effective tool for rapid disaster assessment and early warning in marine engineering. This research contributes to the safety of marine pipeline protections and provides new insights into the intersection of marine geological engineering and artificial intelligence.

中文翻译：

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使用有限元数据评估海底液化的机器学习模型的比较评估


预测海底管道周围的波浪诱导液化对于海洋工程安全至关重要。然而，海洋动力学和海底沉积物之间复杂的相互作用使得传统数值方法难以进行快速准确的评估。尽管机器学习方法越来越多地应用于波诱导的液化问题，但不同模型的相对准确性仍未得到充分探索。我们评估了四种经典机器学习模型的预测准确性：梯度提升 （GB）、支持向量机 （SVM）、多层感知器 （MLP） 和随机森林 （RF）。结果表明，GB 模型由于处理复杂非线性地质数据的能力很强，因此在预测波浪诱导液化方面表现出很高的稳定性和准确性。预测精度因输出参数而异，海底预测的精度高于管道环境。不同输入参数的组合会显著影响模型预测准确性。与传统的有限元数值方法相比，采用机器学习模型可显著缩短计算时间，为海洋工程的快速灾害评估和预警提供了有效的工具。这项研究有助于海洋管道保护的安全，并为海洋地质工程和人工智能的交叉点提供了新的见解。