A Review on the Effect of Water-Cement Ratio on the Flow and Strength of Self-compacting Concrete
Keywords:
Compressive strength, Flowability, Machine learning, Recycled aggregates, Self-compacting concrete, Supplementary cementitious materials, Water-cement ratioAbstract
Self-compacting concrete is an extremely free-flowing and uniform type of concrete which is suitable for filling shuttering molds surrounding the rebars under the influence of self-weight without any mechanical compaction. The behavior of SCC is controlled by a complicated interrelationship of mix-design factors, including W/C ratio, which plays a crucial and predominant function. The W/C ratio strongly impacts fresh flow-related performance involving fluidity, passing capacity, particle separation, as well as in hardened state, like mechanical performance, compression-related, tension-related and bending-related strength. This literature review provides an in-depth summary of laboratory-based tests, theoretical tests, and microstructure-related studies which influence the mechanical strength and flow-related properties of SCC. The literature review systematically analyzes that how changes in W/C ratio influence the fresh state properties, which are evaluated by means of EFNARC standards and test methods that change the strength progression and long-term performance of concrete. The effect of reused and non-conventional aggregates on actual water-cement ratio and SCC performance is critically examined. Moreover, current development in ML-based prediction model development of SCC characteristics is examined, which highlights the importance of W/C ratio as an important input factor and discusses the role of XAI in enhancing predictive model clarity. The review paper recognizes major difficulties involving a shortage of uniform SCC mix design practices, variation in primary ingredients, a shortage of long-term performance datasets and the constrained generalization ability of available AI models. Based on the integrated published studies, the forthcoming investigation will direct the recommended enhancement in durable and eco-friendly AI-enabled SCC systems. This review focuses on serving as an extensive resource for investigators and field-based practitioners working in SCC mix design improvement.
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