Abstract: The disclosure relates to structural health monitoring (SHM). In particular determining a health condition of a structure, such as a bridge, based on vibration data measured of the bridge. Measured vibration data is calibrated (410-450). Features are then extracted from the calibrated data (610-630) and a support vector machine classifier is then applied (720) to the extracted features to determine (730) the health condition of a part of the structure. Training of the support vector machine classifier by a machine learning process (910) is also described.

Abstract: The present disclosure generally relates to infrastructure health condition estimation. There is provided a computer-implemented method for characterising behaviours of working events of components of an infrastructure (110), the working events comprising previous working events. The method comprises obtaining historical data representing the previous working events of the components (112) of the infrastructure (110); and determining, based on the historical data, values of parameters of a stochastic process model to characterise the behaviours of the working events, wherein the stochastic process model comprises a set of Hawkes processes that characterise occurrences of the working events and a Bayesian nonparametric process that characterises dependency of the working events.

Abstract: The disclosure relates to structural health monitoring (SHM). In particular determining a health condition of a structure, such as a bridge, based on vibration data measured of the bridge. Measured vibration data is calibrated (410-450). Features are then extracted from the calibrated data (610-630) and a support vector machine classifier is then applied (720) to the extracted features to determine (730) the health condition of a part of the structure. Training of the support vector machine classifier by a machine learning process (910) is also described.