Scholarlink

This study aims to predict optimum reinforced concrete wall dimensions and cost by using the height of the wall (H) and surcharge load (q) inputs using Multioutput Regression. The base learner models for Multioutput Regression used in this study are Decision Tree Regression (DTR), Support Vector Regression (SVR), Elastic Net Regression (ENT) and Histogram Gradient Boosting Regression (HGBR). Coefficient of Determination (R2), Mean Absolute Error (MAE) and Mean Squared Error (MSE) are used for performance evaluation. Among these models, DTR showed the highest prediction performance (R2: 0.855, MSE: 3930.22, MAE: 18.09).

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

Multioutput Regression for Reinforced Retaining Wall Optimum Design with Machine Learning

  • 章节

  • 发表日期：

    2026年

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

摘要

This study aims to predict optimum reinforced concrete wall dimensions and cost by using the height of the wall (H) and surcharge load (q) inputs using Multioutput Regression. The base learner models for Multioutput Regression used in this study are Decision Tree Regression (DTR), Support Vector Regression (SVR), Elastic Net Regression (ENT) and Histogram Gradient Boosting Regression (HGBR). Coefficient of Determination (R2), Mean Absolute Error (MAE) and Mean Squared Error (MSE) are used for performance evaluation. Among these models, DTR showed the highest prediction performance (R2: 0.855, MSE: 3930.22, MAE: 18.09).