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A procedure for the identification of multiple cracks on beams and frames by static measurements
Author(s) -
Caddemi S.,
Caliò I.,
Cannizzaro F.,
Morassi A.
Publication year - 2018
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2194
Subject(s) - robustness (evolution) , inverse problem , identification (biology) , beam (structure) , bernoulli's principle , frame (networking) , algorithm , boundary value problem , euler's formula , inverse , structural engineering , mathematics , computer science , mathematical analysis , engineering , geometry , telecommunications , biochemistry , chemistry , botany , biology , gene , aerospace engineering
Summary In this work, a model of the Euler–Bernoulli beam in presence of multiple‐concentrated open cracks, based on the adoption of a localized flexibility model, is adopted. The closed‐form solution in terms of transversal displacements due to static loads and general boundary condition is exploited to propose an inverse damage identification procedure. The proposed identification procedure does not require any solution algorithm, on the contrary is formulated by means of simple explicit sequential expressions for the crack positions and intensities including the identification of the integration constants. The number of possible detected cracks depends on the couples of adopted sensors. Undamaged beam zones can also be easily detected in relation to the sensor positions. The analytical character of the explicit expressions of the identification procedure makes the inverse formulation applicable to damaged beams included in more complex frame structures. The proposed procedure is applied for the identification of the number, position, and intensity of the cracks along simple straight beams and also to more complex frame structures with the aim of showing its simplicity for engineering applications. In addition, the robustness of the methodology here described is shown through an accurate analysis of the basic assumptions on which the theory relies and by means of a study of the effect of noise on the identification results.