<![CDATA[Dynamic tests enable assessment of the structure’s technical condition and provide information necessary for management and maintenance throughout the object’s life cycle. On their basis, the dynamic characteristics of the object are estimated (e.g., the logarithmic decrement). The possible occurrence of atypical features in the obtained signal (e.g. amplitude beat, outliers), as well as the influence of the type of devices and sensors used for measurements, should be considered. If these features are omitted during the analysis, key dynamic characteristics may be evaluated incorrectly. Therefore, this study presents development of a reproducible, universal and robust open-source algorithm for effective estimation of the logarithmic decrement of bridge structures as a reproducible research. Using the presented approach, it is possible to obtain correct results regardless of the signal’s specificity and its atypical features, as well as the type of devices used to collect data in the in-situ conditions. Two approaches based on the use of advanced regression models are considered to estimate the logarithmic decrement. These are direct non-linear approximation (DNAP) and Hilbert non-linear approximation (HNAP). The enriched HNAPsolution was then implemented as a Python module with a "Signal" class and tested on two independent in-situ examples. The presented approach led to effective and correct estimation of the logarithmic decrement, and proved to be insensitive to the type of bridge, its structural characteristics, atypical features of the obtained signal, and the specificity of the data acquisition techniques. In contrast to methods based on deep machine learning, the presented solution does not require a large learning set representative for a given type of design and works independently of the size of the data sample. As demonstrated in the paper, the solution based on the Hilbert transform allows efficient determination of the damping decrement even in the presence of beat frequencies as well as outlier data. The algorithm works independently of the measurement method, with the necessary functions for preprocessing being implemented in the module itself. The solution has been optimized for improved speed, reliability, and reproducibility of results. Doi: 10.28991/CEJ-2022-08-04-02 Full Text: PDF]]>
