A previously proposed nonlinearity detection method using the second derivative “snap” of the recorded absolute acceleration requires the determination of a threshold value based on the yield strength of the target vibration system, which may not be known. Therefore, this study aims to extend this detection method by determining the mathematical relation between the snap and stiffness change and velocity of the vibration system; the results indicate that the threshold value required to detect nonlinearities can be explicitly expressed by mathematical equations. Although the accuracy of this detection method is affected by the intensity of noise and the time intervals of the acceleration records, the introduced mathematical model can both explain these effects and allow the user to decide a priori whether this method can be used to detect nonlinearities. Furthermore, the proposed mathematical model for nonlinearity detection was verified by dynamic response analysis with varying natural periods, showing that the detectable range estimated by the model agreed with the range where the accuracy of nonlinearity detection by snap increases.
Evaluating roof snow load is an important problem for building design in snowy regions. A semi-full-scale building model was developed to obtain validation data for the model for evaluating roof snow load. The building model was placed in an observational field in Nagaoka City, where extensive weather conditions were measured. The seasonal change in the roof snow load was directly measured by a load cell installed between the pillars and beams. The water runoff from the roof was recorded at the drainpipe. The effectiveness of the measurement system was demonstrated with observation data from the winter of 2018-2019.
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