Evaluation of statistical parameters of concrete strength from secondary experimental test data

The results of material acceptance tests or in-situ tests are a valuable source of information for reliability assessment of existing structures. Huge secondary databases of test results are usually available, coming from different sources, but individual results are often not associated to a given population, so their statistical analysis is a complicated process. In order to solve this problem, the paper presents a methodology that allows to identify homogeneous populations (or material classes), together with their statistical parameters, when mixed in arbitrary and unknown percentages in a secondary database. The methodology is based on the cluster analysis of data applying the Expectation-Maximization algorithm, which allows to figure out individual classes and their characterizing statistical parameters by fitting a Gaussian Mixture Model. The proposed methodology has been applied to a relevant case study, investigating the cubic concrete strength of the Italian production during the 1960s, also using different approaches. The study demonstrates that approximately six concrete classes can be identified, characterized by an almost constant standard deviation of about 4.0-4.5 MPa, in agreement with the results obtained by previous research. As the results obtained with different approaches agree satisfactorily, it can be concluded that, if enough experimental data are available, the proposed procedure is not only suitable for the intended applications, but it is also “robust” enough.