Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: a comparative study

Alkali silica reaction (ASR) is a harmful distress mechanism which results in expansion and reduction of mechanical properties of concrete. The latter may cause loss of serviceability and load carrying capacity of affected concrete structures. Influences of ASR on concrete are known to be complex in nature, for which the traditional empirical and curve-fitting approaches are insufficient to provide adequate models to capture such complexity. Recent advancement in soft computing (SC) offers a new tool for tackling the complexity of ASR affected concrete. Most of previous experimental studies agreed that as a result of ASR, the elastic modulus suffers a significant reduction compared with other properties such as compressive and tensile strength of the affected concrete. In this study, an investigation has been conducted, utilising different SC models to quantify ASR-induced elastic modulus degradation of unrestrained concrete. Five SC techniques, namely support vector machine (SVM), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), M5P model and genetic expression programming (GEP), are investigated comparatively in this research. The models, on basis of SC techniques, are developed and tested using a comprehensive dataset collected from existing publications. In order to demonstrate the superiorities of SC techniques, the proposed approaches are compared to several empirical models developed using same dataset. The comparative results show that the developed SC models outperform empirical models in a wide range of evaluation indices, which indicates promising applications of the proposed approach