Compressive strength in cement mortars via impulse excitation technique and genetic algorithm

Compressive strength, a crucial mechanical property of cement mortars, is typically measured destructively. However, there is a need to evaluate the strength of unique cement-based samples over various aging periods without causing damage. This study proposes a predictive framework using a genetic algorithm to estimate the compressive strength of ordinary cement-based mortars based on their dynamic elastic modulus, measured non-destructively using the impulse excitation technique. By combining the Popovics and Lydon-Balendran models, the static elastic modulus of the samples is calculated with constant coefficients, representing an equivalent compressive strength. The genetic algorithm is employed to determine the optimal values for these coefficients. The results show that the combining model is more sensitive to the Lydon-Balendran approach within the middle range of the dynamic Young's modulus, while the Popovics-based strength dominates at higher and lower dynamic Young's modulus levels. The model exhibits a low root mean square error (RMSE) value of 3.1%. The findings suggest that this non-destructive model has potential as a candidate for predicting the mechanical properties of cement mortars in the industry. It enables efficient evaluation of compressive strength without destructive testing, offering advantages for assessing cement-based materials reliably.