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基于开封地区的圆穿透测试数据对土壤分类的评估,使用优化支矢量机器

Hanliang Bian¹, Zhongxun Sun¹, Jiahan Bian²

¹School of Civil Engineering and Architecture, Henan University, Kaifeng, 475004, China.

Scientific reports

|January 2, 2025

概括

优化支持矢量机 (SVM) 模型与进化算法显著提高土壤分类准确性使用圆透测试 (CPT) 数据. 这种方法增强了工程地质分析超出了当前标准.

科学领域:

地质技术工程地质技术工程
机器学习应用机器学习应用
计算科学计算科学

背景情况:

传统的土壤分类方法往往是昂贵和耗时的.
准确的土壤分析对于工程项目的成功至关重要.

研究的目的:

开发和验证用于土壤分类的增强型支持矢量机 (SVM) 模型.
为了提高土壤分析的准确性和效率,使用圆穿透测试 (CPT) 数据.

主要方法:

训练有素的SVM模型使用649个CPT数据集,输入的是圆尖阻力和袖子摩擦.
应用了25个优化算法来提高SVM模型的性能.
对208个CPT记录的独立数据集进行了验证模型.

主要成果:

23个优化算法提高了SVM分类准确度.
18个算法超过了"铁路工程地质学实地测量代码"的准确性.
热交换优化 (TEO) 算法得到了最显著的改进,精度增加了10%.

结论:

将进化算法与SVM集成,为土壤分类提供了更有效,更准确的方法.
优化的SVM模型与现有的工程标准相比,提供了更高的性能.
这种方法对推进地质工程应用具有重大前景.

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