Scholarlink

In solving applications of geotechnical engineering, an important problem is the calculation of the shear wave velocity, since the properties to be determined depending on this value are available. This study aims to predict shear wave velocity (Vs (m/s)) using parameters such as depth (m), cone resistance (qc) (MPa), friction resistance (fs) (kPa), pore water pressure (u2) (kPa), N and unit weight (kN/m3) using Boosting Regression (GBR), Support Vector Regression (SVR) and Decision Tree Regression (DTR). Coefficient of Determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used as performance metrics for regression. Among these models, GBR showed the highest prediction performance (R2: 89.03%, MAE: 15.1139, RMSE: 18.8585).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Shear Wave Velocity Prediction Using Machine Learning

  • 章节

  • 发表日期：

    2026年

查看全文
  • Yaren Aydın,
  • Sinan Melih Nigdeli,
  • Gebrail Bekdaş

摘要

In solving applications of geotechnical engineering, an important problem is the calculation of the shear wave velocity, since the properties to be determined depending on this value are available. This study aims to predict shear wave velocity (Vs (m/s)) using parameters such as depth (m), cone resistance (qc) (MPa), friction resistance (fs) (kPa), pore water pressure (u2) (kPa), N and unit weight (kN/m3) using Boosting Regression (GBR), Support Vector Regression (SVR) and Decision Tree Regression (DTR). Coefficient of Determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used as performance metrics for regression. Among these models, GBR showed the highest prediction performance (R2: 89.03%, MAE: 15.1139, RMSE: 18.8585).