Abstract
Obtaining the strength-oriented mix design, balanced proportions of the additional constituents of Self Compacting Concrete (SCC) has remained a time-consuming, approximated, and repetitive work. In addition, the conventional evaluation methods have raised the challenges of accuracy and interpretation of the results, thereby limiting SCC applications. Nevertheless, in the past few years, researchers are working to overcome these challenges by incorporating the optimization methods and prediction models for the mix designing using artificial intelligence (AI) concepts and modern optimization methods, namely artificial neural network (ANN). This paper reviews the recent advances in integrating the AI methods to obtain mix design of the SCC ingredients and the strength prediction of the optimized mixtures. The review revealed that an appropriate AI-based optimization method could provide the optimum proportions of the varying constituents of SCC effectively and accurately. Moreover the AI-based methods supported the construction and utilization of the models to predict the strength and durability properties. On the other hand, the review highlighted the need to extend the AI concepts to automate the conventional testing methods of SCC mixes. The review also provides essential information about the innovative futuristic work required to refine the SCC mix design and performance further using AI methods.
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