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Abstract：
Super absorbent polymer (SAP) has a capacity to enhance the characteristics of cementitious composites in both their fresh and hardened forms. However, it is essential to recognize that the strength of SAP concrete may decrease. By altering the concrete composition and selecting the appropriate type of SAP, it is possible to reduce this reduction. This work employs machine learning (ML) to tackle the issue of strength degradation. The analysis considers ten distinct variables linked to concrete composition and the type of SAP. The study uses machine learning approaches that involve both regression and classification tasks. The use of ensemble learning greatly improves the quality and accuracy of the results, showing its superiority in combining several models to produce more precise predictions. The findings demonstrate that the Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost) regression algorithms accurately forecasted the percentage of reduction in strength in SAP concrete. These predictions were based on the concrete composition and SAP details, resulting in R2 values of 0.90 and 0.88, respectively. Furthermore, XGBoost exhibited the highest accuracy, reaching 0.94, when compared to the various categorization algorithms. According to the results, the mean squared error (MSE) of the ensemble model demonstrated superior outcomes. Furthermore, the SHapley Additive exPlanations (SHAP) reveal that some variables, including SAP%, SAP size, and compressive strength, have a significant influence on the strength reduction model. This study aims to bridge the gap between academic research and practical application by developing a web application that employs ensemble learning to precisely forecast the reduction in compressive strength caused by the usage of SAP.
摘要：
高吸水性聚合物(SAP)具有增强新鲜和硬化形式的水泥基复合材料的特性的能力。然而,必须认识到SAP混凝土的强度可能会降低。通过改变混凝土组成并选择合适的SAP类型，可以减少这种减少。这项工作采用机器学习（ML）来解决强度下降的问题。该分析考虑了与具体组成和SAP类型相关的十个不同变量。该研究使用涉及回归和分类任务的机器学习方法。集成学习的使用大大提高了结果的质量和准确性，显示了它在组合几个模型以产生更精确预测方面的优势。研究结果表明，支持向量机（SVM）和极限梯度提升（XGBoost）回归算法可以准确预测SAP混凝土强度降低的百分比。这些预测是基于具体的组成和SAP细节，分别导致0.90和0.88的R2值。此外，XGBoost表现出最高的精度，与各种分类算法相比，达到0.94。根据结果,集成模型的均方误差(MSE)显示出优异的结果。此外，沙普利加法扩张(SHAP)揭示了一些变量，包括SAP%，SAP大小,和抗压强度，对强度折减模型有显著影响。本研究旨在通过开发一个Web应用程序来弥合学术研究和实际应用之间的差距，该应用程序采用集成学习来精确预测由于使用SAP而导致的抗压强度降低。
