New multi-objective optimization to evaluate the compressive strength and variability of concrete by combining non-destructive techniques

Concrete compressive strength is one of the most important parameters in the evaluation of the mechanical performance of reinforced concrete structures. The recent methodology for assessing concrete strength in an existing structure involves integrating Non-Destructive Testing (NDT) techniques, such as Rebound Hammer measurement and Ultrasonic Pulse Velocity measurement, with destructive sampling measurements to establish a conversion model that correlates mechanical strength with non-destructive measurements. The conversion model is then used to estimate the local value of strength at each location of non-destructive measurements and thus to represent the spatial variability. Despite successfully identifying the compressive mean strength through this methodology, there remains significant uncertainty regarding the variability of compressive strength. The goal of this study is to propose a new model identification approach based on multi-objective optimization to predict the compressive strength of concrete and its variability, based on the combination of NDT measurements. Monte Carlo simulations are carried out to check the performance by taking into account the uncertainty of NDT measurements and the variability of concrete. For this purpose, a large dataset of experimental and simulated data of both destructive and non-destructive tests is used. The conclusions drawn from the synthetic data will be compared with the results obtained on the experimental dataset in order to test the efficiency of the proposed methodology. This study shows the principle of the optimization methodology and the first results of its effectiveness in predicting compressive strength including its variability. The findings indicate that the multi-objective method is efficient in determining the mean strength compared to other approaches. Furthermore, by employing the multi-objective approach, engineers and researchers can attain enhanced accuracy in estimating the variability of concrete. Simultaneously, they can optimize the number of core samples, leading to improved efficiency, cost-effectiveness, and minimized impact on concrete structures.