With the expansion of marine resource utilization, geological disaster prediction, and ecological protection in deep-sea regions, accurate identification of deep-sea engineering geological environments has become paramount, highlighting the significance of understanding the mechanical characteristics of deep-sea surface ultrasoft sediments (DSUSs). However, DSUSs typically exhibit low strength, high compressibility, and significant fluidity, making sampling for indoor strength testing a challenging task. On-site testing employing a T-bar penetrometer has become increasingly prominent for assessing DSUS strength, but the complexity of influencing parameters limits the application of this valuable method. This study utilizes a validated computational fluid dynamics (CFD) approach to model the complete process of T-bar penetration into a DSUS. The aim is to understand the quantitative effects and corresponding mechanisms of various significant complex parameters, including the undrained shear strength, dimensionless penetration depth, rate effect, and interface contact condition, on the dimensionless penetration resistance coefficient. Additionally, a machine learning model based on the random forest algorithm is introduced to establish a multiparameter evaluation method for the dimensionless penetration resistance coefficient. The findings illuminate the intricate interactions and effective evaluation of these factors when evaluating DSUS strength using a T-bar penetrometer, addressing the above challenges in marine engineering geology and the environment.

中文翻译：

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通过 T 形杆穿透评估深海表面超软沉积物的强度：一种机器学习方法


随着深海地区海洋资源利用、地质灾害预报、生态保护等领域的拓展，深海工程地质环境的准确识别变得至关重要，凸显了了解深海表层超软沉积物力学特征的重要意义。 DSUS）。然而，DSUS 通常表现出低强度、高压缩性和显着的流动性，使得室内强度测试的取样成为一项具有挑战性的任务。使用 T 形杆贯入度仪进行现场测试对于评估 DSUS 强度已变得越来越重要，但影响参数的复杂性限制了这种有价值的方法的应用。本研究利用经过验证的计算流体动力学 (CFD) 方法来模拟 T 形杆穿透 DSUS 的完整过程。目的是了解不排水抗剪强度、无量纲贯入深度、速率效应、界面接触条件等各种重要复杂参数对无量纲贯入阻力系数的定量影响和相应机制。此外，引入基于随机森林算法的机器学习模型，建立了无量纲侵彻阻力系数的多参数评估方法。研究结果阐明了使用 T 形杆贯入度计评估 DSUS 强度时这些因素的复杂相互作用和有效评估，解决了海洋工程地质和环境中的上述挑战。