Scholarlink

<p>This study presents a machine learning-driven comparative analysis to predict frost resistance in rubberized concrete, focusing on the interpretability and performance of three models: Stepwise Linear Regression (SLR), Stepwise Polynomial Regression (SPR), and Classification and Regression Tree (CART). Leveraging historical experimental data, the methodology integrates rigorous preprocessing via Interquartile Range (IQR) outlier detection and tenfold cross-validation to ensure robustness. Key variables, including rubber content (0–20% fine aggregate replacement), water-cement ratio, and freeze–thaw cycles, were analyzed to balance durability and sustainability goals. The CART model offered interpretable decision rules (test R<sup>2</sup> = 97.01%) identifying critical thresholds like rubber content ≤ 15%, providing a clear guideline for maximizing durability. Comparatively, the study advances prior Artificial Neural Network (ANN)-based approaches by delivering mathematical transparency (SLR, SPR) and actionable insights (CART). These outputs directly guide mix design: the SPR equation enables precise prediction of frost resistance for specific combinations of mix parameters, while the CART rules establish safe application limits. This moves beyond prediction to offer practical tools for optimizing sustainable concrete mixes under frost exposure, prioritizing both accuracy and implementable insight.</p>

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

Predicting frost resistance of rubberized concrete for sustainable design: a comparative analysis of regression trees and stepwise regression models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月10日

查看全文
  • Iman Kattoof Harith

摘要

This study presents a machine learning-driven comparative analysis to predict frost resistance in rubberized concrete, focusing on the interpretability and performance of three models: Stepwise Linear Regression (SLR), Stepwise Polynomial Regression (SPR), and Classification and Regression Tree (CART). Leveraging historical experimental data, the methodology integrates rigorous preprocessing via Interquartile Range (IQR) outlier detection and tenfold cross-validation to ensure robustness. Key variables, including rubber content (0–20% fine aggregate replacement), water-cement ratio, and freeze–thaw cycles, were analyzed to balance durability and sustainability goals. The CART model offered interpretable decision rules (test R2 = 97.01%) identifying critical thresholds like rubber content ≤ 15%, providing a clear guideline for maximizing durability. Comparatively, the study advances prior Artificial Neural Network (ANN)-based approaches by delivering mathematical transparency (SLR, SPR) and actionable insights (CART). These outputs directly guide mix design: the SPR equation enables precise prediction of frost resistance for specific combinations of mix parameters, while the CART rules establish safe application limits. This moves beyond prediction to offer practical tools for optimizing sustainable concrete mixes under frost exposure, prioritizing both accuracy and implementable insight.