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<p>Incorporating rice husk ash (RHA) into concrete improves the structure’s compressive strength (CS) and durability and aids sustainability by lowering carbon emissions. This paper evaluates the application of twelve machine learning (ML) algorithms to predict the CS of concrete containing RHA. The dataset used to train, test, and validate the model comprised 500 experimental samples and 30 data points sourced externally. Through stepwise regression, seven input features were chosen: water-to-binder ratio (W/B), cement (C), superplasticizer (SP), water (W), RHA, coarse aggregate (CA), and fine aggregate (FA). Among the evaluated models, support vector regression (SVR), Gaussian process regression (GPR), and null‒space SVR (NuSVR) models emerged as the best performing, each attaining R² values over 0.93. DTR performed weakest, with R² values below 0.53, illustrating the importance of algoRhythm selection. The study’s results reaffirm the importance of RHA content and the W/B ratio as the two major determinants of the CS increase. To assist practitioners iapplyingng the trained models, a simple graphical user interface (GUI) was created to allow engineers to quickly evaluate CS and refine concrete mix designs. The combination of sophisticated ML methods with the data on RHA concrete will therefore support the overarching strategy to achieve sustainability in construction and high operational reliability of structures.</p>

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Forecasting compressive strength of concrete containing rice husk ash using various machine learning algorithms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

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  • Ala’a R. Al-Shamasneh,
  • Manish Kewalramani,
  • Arsalan Mahmoodzadeh,
  • Abdulaziz Alghamdi,
  • Jasim Alnahas,
  • Nejib Ghazouani,
  • Mohammed Sulaiman

摘要

Incorporating rice husk ash (RHA) into concrete improves the structure’s compressive strength (CS) and durability and aids sustainability by lowering carbon emissions. This paper evaluates the application of twelve machine learning (ML) algorithms to predict the CS of concrete containing RHA. The dataset used to train, test, and validate the model comprised 500 experimental samples and 30 data points sourced externally. Through stepwise regression, seven input features were chosen: water-to-binder ratio (W/B), cement (C), superplasticizer (SP), water (W), RHA, coarse aggregate (CA), and fine aggregate (FA). Among the evaluated models, support vector regression (SVR), Gaussian process regression (GPR), and null‒space SVR (NuSVR) models emerged as the best performing, each attaining R² values over 0.93. DTR performed weakest, with R² values below 0.53, illustrating the importance of algoRhythm selection. The study’s results reaffirm the importance of RHA content and the W/B ratio as the two major determinants of the CS increase. To assist practitioners iapplyingng the trained models, a simple graphical user interface (GUI) was created to allow engineers to quickly evaluate CS and refine concrete mix designs. The combination of sophisticated ML methods with the data on RHA concrete will therefore support the overarching strategy to achieve sustainability in construction and high operational reliability of structures.