Application of Machine Learning to optimize concrete properties, minimizing variability in concrete production

Self-consolidating concrete (SCC) is engineered to facilitate casting and accelerate the construction process while enhancing structural performance and durability. Its high deformability allows SCC to spread and fill formwork under its own weight, eliminating the need for external vibration. The mix design of SCC is critical for achieving an optimal balance between fluidity and stability, thus preventing the separation of its constituents. Traditional design methods can be extensive and time-consuming, requiring careful adjustments of mix parameters to meet specific performance targets. The integration of artificial intelligence to predict the properties of self-consolidating concrete represents a significant advancement, improving the accuracy and efficiency of mix design processes.

Faculty Supervisor:

Ammar Yahia

Student:

Partner:

University of Colorado Denver

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program: