B. Steck, E. Robbe & N. Humbert


matrix model is based upon the PhD thesis work of B. Cauberghe[8,9]


. It improves the readability of the


stabilization diagram. Moreover, in the OMA case, a preliminary band-pass filtering step is conducted to eliminate poles outside the frequency range of interest.


• Modal Shape Estimation: Once the poles are selected, Data_Analytics proposes two pole/residue models, giving the possibility to estimate either real or complex mode shapes. Different levels of


optimization can be integrated to refine the initial estimation of residues, drawing upon published works by E. Balmès[9,10]


.


Figure 10. Stabilization diagram as it is given in Data Analytics - Stable poles correspond to black cross on the figure while instable ones are represented by red dot. The purple function seen in the figure is the Complex Modal Identification Function (CMIF)


4.2. Creation and parameterization of an automatic detection of the normal modes for the case of Lanoux dam


Data_Analytics is primarily designed for one-off measurement campaigns. The code has been automated to handle a large volume of measurements efficiently. The adopted approach involves extracting all stable poles within a predefined frequency range (here, between 3 Hz and 14 Hz) using the stabilization diagram. This method used on the whole year of ambient noise recording in the Lanoux dam on the upstream- downstream component enables the identification of the first six modes (see Figure 11). However, the detection process includes some false positives and missed detections, particularly for the first two modes, whose frequencies are very close (~0.1 Hz apart).


148 | Dam Engineering | Vol XXXIII Issue 3


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