Crack width prediction of self-healing engineered cementitious composite using multi-expression programming

Abstract

Concrete structures are prone to cracking due to environmental damage and/or tensile strain. Self-healing concrete has garnered scientific interest in recent years as it addresses the cracking problem and extends the service life of concrete structures. Engineered cementitious composite (ECC), a high-performance fiber-reinforced cement composite, is known to have some self-healing characteristics. However, the self-healing capability of ECC is characterized by the type and content of input parameters and is difficult to predict. This investigation utilised a multi-expression programming algorithm for the prediction of self-healing characteristics of ECC containing various admixtures. The database, containing 619 points, was extracted from literature containing crack width before self-healing (CB), limestone powder (LP), silica fume (SF), and fly ash (FA) as input parameters and crack width after self-healing as an output parameter. The performance of the developed model was evaluated based on various statistical indices and parametric analysis. The results showed that the developed model is robust and efficient in predicting the self-healing behavior of ECC containing various admixtures. Parametric and sensitivity analysis revealed that among the admixtures used, fly ash had the least impact and limestone powder had the highest impact on the self-healing capacity of ECC.