Statistical Parameters of Steel Rebars of Reinforced Concrete Existing Structures

Historical and cognitive investigations supported by in-situ and/or laboratory tests are needed for a robust reliability assessment of existing structures. Indeed, an adequate knowledge of material properties and their statistical description is the basis for carrying out accurate reliability analyses and verifications on the investigated structures. In this paper, a procedure for the definition of pdfs of mechanical parameters of steel rebars is proposed based on secondary experimental test data. This information is very helpful for the reliability assessment of existing r.c. buildings, where estimation of statistical parameters of mechanical properties of steel reinforcement is very difficult. In fact. It must be highlighted on the one hand that direct information about the examined structure are commonly not sufficient, on the other hand that the number of rebar samples extracted from the structure, if available, is so limited that it does not allow a complete statistical analysis. The first step has been the collection of experimental acceptance tests carried out by Department of Civil and Industrial Engineering of University of Pisa on steel rebars of reinforced concrete (r.c.) structures during the 1960s. The yield strength and the tensile strength are extrapolated for each sample defining a significant database of experimental test results for existing r.c. structures. Then, probability distribution models for the mechanical properties of steel reinforcement have been defined as already done by the authors for concrete strength. A cluster analysis has been carried out based on the Gaussian Mixture Model applying the Expectation-Maximization algorithm to identify homogeneous material classes and their associated pdfs of material mechanical parameters. The main advantage of proposed procedure consists in its “blindness”, In fact, not requiring subjective information like pre-classification of data, the methodology is not sensitive to alterations caused by engineering judgement or by inexact identification of declared strength class of the tested samples, due for example to downgraded materials