Abstract: A method for classifying a response signal of acceleration data of a structure includes obtaining at least one signal feature of a response signal, inputting the at least one signal feature into an artificial neural network, and classifying, using the artificial neural network, the response signal as an impact event or a non-impact event. One or more signal features may be used, including a response length feature, a number of peaks feature, a spectral energy feature, a dominant frequency feature, a maximum response feature, a center of mass feature, a slope feature, an average peak power feature, a response symmetry feature, or combinations thereof. One or more artificial neural networks may be used. The artificial neural networks may be trained using different combinations of signal features.

Abstract: A method for classifying accelerometer data of a structure includes obtaining acceleration data from an accelerometer positioned to monitor a structure and receive vibrations from the structure. The acceleration data includes a plurality of measurements. The method includes selecting a subset of the plurality of measurements as an event signal. The subset of the plurality of measurements have a magnitude that exceeds a noise floor and includes a first measurement, a second measurement, and a plurality of intermediate measurements between the first measurement and the second measurement. The plurality of intermediate measurements exceed an event threshold. The event threshold is greater than the noise floor. The method includes comparing the event signal to a set of historical events and classifying the event signal as an event type.

Abstract: A method for classifying accelerometer data of a structure includes obtaining acceleration data from an accelerometer positioned to monitor a structure and receive vibrations from the structure. The acceleration data includes a plurality of measurements. The method includes selecting a subset of the plurality of measurements as an event signal. The subset of the plurality of measurements have a magnitude that exceeds a noise floor and includes a first measurement, a second measurement, and a plurality of intermediate measurements between the first measurement and the second measurement. The plurality of intermediate measurements exceed an event threshold. The event threshold is greater than the noise floor. The method includes comparing the event signal to a set of historical events and classifying the event signal as an event type.

Abstract: A method for classifying a response signal of acceleration data of a structure includes obtaining at least one signal feature of a response signal, inputting the at least one signal feature into an artificial neural network, and classifying, using the artificial neural network, the response signal as an impact event or a non-impact event. One or more signal features may be used, including a response length feature, a number of peaks feature, a spectral energy feature, a dominant frequency feature, a maximum response feature, a center of mass feature, a slope feature, an average peak power feature, a response symmetry feature, or combinations thereof. One or more artificial neural networks may be used. The artificial neural networks may be trained using different combinations of signal features.