Estimation of compressive strength of cement mortars using impulse excitation technique and a 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 at various ages without causing damage. In this paper, a predictive framework using a genetic algorithm (GA) is proposed for estimating 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 model (PM) and the Lydon-Balendran model (LBM), the static elastic modulus of samples was calculated using constant coefficients, representing an equivalent compressive strength. A GA was then employed to determine optimal values for these coefficients. The results showed that the LBM-based strength was dominant in the middle range of the dynamic Young's modulus while the PM-based strength was dominant for higher and lower values of the dynamic Young's modulus. The model was found to have a small root mean square error (3.1%). The findings suggest that this non-destructive model has potential for predicting the mechanical properties of cement mortars. It allows efficient evaluation of compressive strength without destructive testing, offering advantages for reliable assessments of cement-based materials.