改进干燥和饱和条件下岩石 S 波速度的测定:机器学习算法的应用

IF 4.9 2区 工程技术 Q1 ENGINEERING, CIVIL
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引用次数: 0

摘要

S 波速度(Vs)的测定在采矿、土木和岩土工程等多个工程学科中都具有重要意义。在干燥和饱和条件下间接测定 Vs 并了解其与影响输入变量(取芯深度 (H)、耐久性指数 (DI)、含水量 (Wa)、干密度 (ρd)、饱和密度 (ρs) 和孔隙度 (n) )之间的关系非常有益。在本研究中,我们使用三种多重机器学习算法(MLAs):人工神经网络(ANN)、模糊推理系统(FIS)和基因表达编程(GEP),结合线性回归方法(LRM)对这些关系进行了评估,并预测了岩石的干 S 波速度(Vs-dry)和饱和 S 波速度(Vs-sat)。研究涉及对 90 个数据集的分析,这些数据集来自伊朗阿扎德和巴赫蒂亚里坝址采集的片岩、辉绿岩和砂岩样本。这些数据集的多样性是本研究的主要优势,为模型的训练和测试提供了坚实的基础,同时增强了模型的普适性。在 Python、MATLAB、GenXProTools 和 SPSS 环境中分别采用了模型优化技术,以确定最有效的 ANN、FIS、GEP 和 LRM 模型版本。预测性能分析表明,所有应用模型预测 Vs-dry 和 Vs-sat 的准确度都达到了可接受的水平。然而,GEP 是预测 Vs-dry 和 Vs-sat 的最佳模型。ANN 和 FIS 模型也达到了较高的准确度,而 LRM 模型的准确度相对较低。此外,使用余弦振幅法(CAM)进行的敏感性分析凸显了不同变量对 Vs-dry 和 Vs-sat 的影响。结果发现,ρd 是对 Vs-dry 影响最大的参数,而 DI 的影响最小。相反,ρs 对 Vs-sat 的影响很大,而 Wa 的影响最小。这些拟议工作重点的卓越性能证实了它们在实际岩石工程和岩土工程项目中的适用性,可提供 Vs 的精确测定。所研究岩石类型和数据集的多样性,以及所使用的具有成本效益且易于测量的输入数据、干燥和饱和状态下的 Vs 测定,以及应用稳健的 MLA 进行 Vs 测定,是本研究的主要创新点。不过,要验证这些发现,还需要涉及更多数据集和更多岩石类型的进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved determination of the S-wave velocity of rocks in dry and saturated conditions: Application of machine-learning algorithms

The determination of S-wave velocity (Vs) is of significant importance in various engineering disciplines, including mining, civil, and geotechnical engineering. It is beneficial to indirectly determine Vs under both dry and saturated conditions and to understand its relationship with influencing input variables: coring depth (H), durability index (DI), water content (Wa), dry density (ρd), saturated density (ρs), and porosity (n). In this study, we evaluate these relationships using three multiple machine-learning algorithms (MLAs): artificial neural network (ANN), fuzzy inference system (FIS), and gene expression programming (GEP), alongside a linear regression method (LRM) and predict both dry S-wave velocity (Vs-dry) and saturated S-wave velocity (Vs-sat) of rocks. The research involves the analysis of 90 datasets derived from samples of schist, phyllite, and sandstone rocks collected from Azad and Bakhtiari dam sites in Iran. The diversity of these datasets is a key advantage of this study, providing a solid foundation for models training and testing while enhancing the models’ generalizability. Model optimization techniques are employed in the Python, MATLAB, GenXProTools, and SPSS environments to identify the most effective versions of ANN, FIS, GEP, and LRM models, respectively. The prediction performance analysis reveals that all applied models yield acceptable levels of accuracy for predicting Vs-dry and Vs-sat. However, GEP emerges as the best model for predicting both Vs-dry and Vs-sat. The ANN and FIS models also achieve high levels of accuracy, while LRM performs comparatively less well. Additionally, sensitivity analysis conducted using the cosine amplitude method (CAM) highlights the influence of different variables on Vs-dry and Vs-sat. The ρd is found to be the most influential parameter on Vs-dry, whereas DI exhibits the least impact. Conversely, the ρs significantly affects Vs-sat, while Wa shows the lowest impact. The exceptional performance of these proposed MLAs confirms their applicability in real-world rock engineering and geotechnics projects, offering precise determination of Vs. The diversity of studied rock types and datasets, along with the use of cost-effective and easy measurable inputs, the determination of Vs in both dry and saturated status, and the application of robust MLAs for Vs determination are the main novelties of this study. However, further researches involving additional datasets and more rock types are required to validate these findings.

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来源期刊
Transportation Geotechnics
Transportation Geotechnics Social Sciences-Transportation
CiteScore
8.10
自引率
11.30%
发文量
194
审稿时长
51 days
期刊介绍: Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.
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