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Experimental Study on Cementitious Composites Embedded with Organic Microcapsules.

ABSTRACT: The recovery behavior for strength and impermeability of cementitious composites embedded with organic microcapsules was investigated in this study. Mortar specimens were formed by mixing the organic microcapsules and a catalyst with cement and sand. The mechanical behaviors of flexural and compression strength were tested. The results showed that strength could increase by up to nine percent with the addition of a small amount of microcapsules and then decrease with an increasing amount of microcapsules. An orthogonal test for investigating the strength recovery rate was designed and implemented for bending and compression using the factors of water/cement ratio, amount of microcapsules, and preloading rate. It is shown that the amount of microcapsules plays a key role in the strength recovery rate. Chloride ion permeability tests were also carried out to investigate the recovery rate and healing effect. The initial damage was obtained by subjecting the specimens to compression. Both the recovery rate and the healing effect were nearly proportional to the amount of microcapsules. The obtained cementitious composites can be seen as self-healing owing to their recovery behavior for both strength and permeability.

SUBMITTER: Wang X

PROVIDER: S-EPMC5452675 | biostudies-other | 2013 Sep

REPOSITORIES: biostudies-other

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Project description:The present study introduces a novel approach utilizing machine learning techniques to predict the crucial mechanical properties of engineered cementitious composites (ECCs), spanning from typical to exceptionally high strength levels. These properties, including compressive strength, flexural strength, tensile strength, and tensile strain capacity, can not only be predicted but also precisely estimated. The investigation encompassed a meticulous compilation and examination of 1532 datasets sourced from pertinent research. Four machine learning algorithms, linear regression (LR), K nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB), were used to establish the prediction model of ECC mechanical properties and determine the optimal model. The optimal model was utilized to employ SHapley Additive exPlanations (SHAP) for scrutinizing feature importance and conducting an in-depth parametric analysis. Subsequently, a comprehensive control strategy was devised for ECC mechanical properties. This strategy can provide actionable guidance for ECC design, equipping engineers and professionals in civil engineering and material science to make informed decisions throughout their design endeavors. The results show that the RF model demonstrated the highest prediction accuracy for compressive strength and flexural strength, with R2 values of 0.92 and 0.91 on the test set. The XGB model outperformed in predicting tensile strength and tensile strain capacity, with R2 values of 0.87 and 0.80 on the test set, respectively. The prediction of tensile strain capacity was the least accurate. Meanwhile, the MAE of the tensile strain capacity was a mere 0.84%, smaller than the variability (1.77%) of the test results in previous research. Compressive strength and tensile strength demonstrated high sensitivity to variations in both water-cement ratio (W) and water reducer (WR). In contrast, flexural strength exhibited high sensitivity solely to changes in W. Conversely, the sensitivity of tensile strain capacity to input features was moderate and consistent. The mechanical attributes of ECC emerged from the combined effects of multiple positive and negative features. Notably, WR exerted the most significant influence on compressive strength among all features, whereas polyethylene (PE) fiber emerged as the primary driver affecting flexural strength, tensile strength, and tensile strain capacity.