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.