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Abstract Numerous studies have investigated the long-term monitoring of natural frequencies, primarily focusing on medium–large highway bridges, using expensive monitoring systems with a large array of sensors. However, this paper addresses the less explored issue of monitoring a footbridge, examining four critical aspects: (i) sensing system, (ii) frequency extraction method, (iii) data modelling techniques, and (iv) damage detection. The paper proposes a low-cost all-in-one sensor/logger unit instead of a conventional sensing system to address the first issue. For the second issue, many studies use natural frequency data extracted from measured acceleration for data modelling, the paper highlights the impact of the input parameters used in the automated frequency extraction process, which affects the number and quality of frequency data points extracted and subsequently influences the data models that can be created. After that, the paper proposes a modified PCA model optimised for computational efficiency, designed explicitly for sparse data from a low-cost monitoring system, and suitable for future on-board computation. It also explores the capabilities and limitations of a data model developed using a limited data set. The paper demonstrates these aspects using data collected from a 108 m cable-stayed footbridge over several months. Finally, the detection of damage is achieved by employing the one-class SVM machine learning technique, which utilises the outcomes obtained from data modelling. In summary, this paper addresses the challenges associated with the long-term monitoring of a footbridge, including selecting a suitable sensing system, automated frequency extraction, data modelling techniques, and damage detection. The proposed solutions offer a cost-effective and efficient approach to monitoring footbridges while considering the challenges of sparse data sets.
Ao et al. (Wed,) studied this question.