Assessing concrete strength with rebound hammer: review of key issues and ideas for more reliable conclusions

On site concrete strength assessment via non-destructive techniques (NDT) is one of the main challenges that NDT is faced with. Along the past 50 years a huge number of both laboratory and onsite experimental programs have been carried out and a variety of models have been proposed in order to correlate concrete strength with rebound hammer measurements. It is commonly agreed that none of these models is able to accurately predict the concrete strength with enough accuracy for using the assessed value for further structural computations. The large variety of concrete compositions and the fact that other influent factors may exist are the major arguments advance in order to explain the failure of modeling and assessment strategies. Based on (a) an in-depth literature review enabling a meta-analysis of existing models and, (b) Monte-Carlo simulations used to simulate what happens when rebound measurements are carried on onsite, and used to assess the concrete strength after a calibration stage, we will point first the previously unnoticed consistency between existing models. This consistency will be then explained by the trade-off which exists between the unknown model parameters that must be identified. In the context of real measurements, i.e., those concerning heterogeneous materials with uncontrolled factors and error measurements, it will be shown that this trade-off systematically exists. This fact is inherent to any inverse or identification problem. Its magnitude depends on several factors, among which the quality of the NDT measurement and the number of control cores used for model calibration play a key role. Its consequences are (a) a limited ability to assess strength and, (b) the impossibility to use the model in different context. Some proposals are finally presented in order to obtain a more robust assessment for the concrete strength. They combine guidelines about the measurement error estimation, a calibration approach which will be less dependent on the information provided by a limited number of cores, and the combination of several measurements in order to reduce the effect of uncontrolled factors.