Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques

Abstract The compressive strength (CS) is the most important parameter in the design codes of reinforced concrete structures. The development of simple mathematical equations for the prediction of CS of concrete can have many practical advantages such as it save cost and time in experiments needed for suitable design data. Due to environmental concerns with the production of cement, different supplementary cementitious materials are often used as partial replacements for cement such as fly ash (FA), metakaolin (MK), and silica fume (SF). However, little work has been done for developing simple mathematical equations for the prediction of CS with FA, MK and SF by using the M5P algorithm. Moreover, the M5P algorithm is not compared with other modelling techniques such as linear regression analysis, gene expression programming (GEP) and response surface methodology. It is established that, for concrete with FA and SF, M5P showed superior prediction capability as compared with other modelling techniques, however, GEP gave the best performance for concrete with MK: CS decrease by increasing FA content, while it increases by increasing MK and SF content.

摘要 抗压强度（Compressive Strength，CS）是钢筋混凝土结构设计规范中的核心设计参数。开发用于预测混凝土抗压强度的简易数学方程具备诸多实际优势，可为获取合适设计数据所需的实验工作节省成本与时间。鉴于水泥生产过程存在环境隐患，工程中常采用各类辅助胶凝材料作为水泥的部分替代料，例如粉煤灰（Fly Ash，FA）、偏高岭土（Metakaolin，MK）与硅灰（Silica Fume，SF）。然而，目前针对采用M5P算法构建以粉煤灰、偏高岭土和硅灰为掺合料的混凝土抗压强度预测简易数学方程的研究仍较为有限。此外，现有研究尚未将M5P算法与其他建模技术进行对比分析，诸如线性回归分析、基因表达式编程（Gene Expression Programming，GEP）以及响应面法（Response Surface Methodology）。已有研究证实：对于掺加粉煤灰与硅灰的混凝土，M5P算法的预测性能优于其他建模技术；而对于掺加偏高岭土的混凝土，基因表达式编程则表现出最优的预测效果：随着粉煤灰掺量的提升，混凝土抗压强度随之降低，而偏高岭土与硅灰掺量的增加则会使抗压强度有所提升。